Biofuels and food security

Tatsuji Koizumi n,1

Food and Agricultural Organization of the United Nations (FAO), Viale delle Terme di Caraclla, 00153 Rome, Italy

a r t i c l e i n f o

Article history:Received 5 November 2014Received in revised form30 April 2015Accepted 29 June 2015Available online 25 August 2015

Keywords:BiofuelsFood securityOwn price elasticityFeedstock supplyPer capita consumption

a b s t r a c t

One of the most crucial problems with increasing biofuel production is that it competes for natural andagricultural resources with food and food related use, because its main feedstock is agricultural product.Increasing biofuel production therefore is going to have an impact on world agricultural commodityprices and food security. The purpose of this study is to conduct an economic analysis of increasingbiofuel production on food security. The own price elasticities of supply equations in the long-term arethe key to deciding agricultural commodity price adjustments as a result of an increase in biofuelproduction. Biofuel production may have a negative impact on food security, but on the other hand theycan create opportunities for agricultural development. It is critical to understand that own priceelasticity of feedstock supply is a key factor in deciding how biofuel development can contributeagricultural development. Policy makers should recognize the importance of the price elasticity offeedstock supply when they promote biofuel programs and select feedstock to contribute to agriculturaldevelopment.

& 2015 Published by Elsevier Ltd.

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8302. Biofuel development and markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 830

2.1. Global biofuel markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8302.2. Biofuel use ratio for agricultural commodity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8302.3. Biofuel developments and competition between biofuels and food . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 832

3. Biofuel impact on agricultural markets and food security. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8333.1. Definition of competition between biofuels and food . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8333.2. Biofuel impact on agricultural markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8333.3. Relationship between biofuel and food security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 833

4. Price elasticity of feedstock supply and consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8344.1. Demand and supply curve of feedstock markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8344.2. Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8354.3. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 835

5. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8366. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 837Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 838

Contents lists available at ScienceDirect

journal homepage: www.elsevier.com/locate/rser

Renewable and Sustainable Energy Reviews

http://dx.doi.org/10.1016/j.rser.2015.06.0411364-0321/& 2015 Published by Elsevier Ltd.

n Tel.:þ39 06 57055597.E-mail addresses: Tatsuji.koizumi@FAO.org, koizu@affrc.go.jp1 The views expressed in this study are those of the author and do not reflect the official view of the FAO.

Renewable and Sustainable Energy Reviews 52 (2015) 829–841

Appendix 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 838Appendix 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 839References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 840

1. Introduction

To deal with energy security, to reduce greenhouse gas (GHG)emissions, and to strengthen agricultural/rural development, biofuelsare being produced and utilized all over the world. At present, themain feedstock is agricultural product. One of the most crucialproblems with increasing biofuel production is that it competes foragricultural resources with food production and food-related use.Biofuel markets are projected to expand in the future and most of thebiofuel production will be derived from agricultural products. Con-sequently, this increase in biofuel production is going to impact onthe world agricultural commodity markets. It can also impact on foodsecurity at all levels. The 1996 World Food Summit adopted a morecomplex definition of food security and refined it in the WorldSummit on Food Security in 2009; Food security is a situation thatexists when all people, at all times, have physical, social andeconomic access to sufficient, safe and nutritious food to meet theirdietary needs and food preferences for an active and healthy life [1].Food security has international, national, sub-national, householdand individual levels.

A total of 842 million people in 2011–2013, or around one ineight people in the world, were estimated to be suffering fromchronic hunger, or not regularly getting enough food to conduct anactive life [2]. To cope with food insecurity is one of the criticaltasks for human beings on a global level. The State of Food andAgriculture 2008 [3] stressed that biofuels have both risksand opportunities. There is a risk that higher food prices couldthreaten the food security of the world's poorest people, many ofwhom spend more than half of their household incomes onfood. On the other hand, biofuels may offer an opportunityfor developing countries – where 75% of the world's poordepend on agriculture for their livelihoods – to harness agricul-tural growth for broader rural development and poverty reduction[3].2

Mitchell [4], Rosegrant [5], FAO [3], Tokgoz et al. [6], NationalResearch Council [7], and IEEP [8] have examined how biofuel canimpact on agricultural commodity markets.3 Baffes [10] examinedenergy's role in the food price determination process and provideda framework for analyzing its likely future impact. Nonhebel [11]compared the need for biofuel and needs for food and feed onglobal scale. His study implied that the present energy policies inthe OECD countries carries the risk of generating food insecurity inlow-income countries. Ewing and Msangi [12] assessed howbiofuel development affected welfare and food security in devel-oping countries. Huang et al. [13] examined how global biofuelproduction could impact poverty in China. Huang et al. [14] alsoassessed the future impact of biofuel production on regionalagriculture and related sectors in developing nations. HLPE [15]analyzed biofuel and food security in global level. Moss et al. [16]examined the effect of increased energy prices on agriculture byestimating agriculture's elasticity of demand for energy. However,

none of these studies analyzed the comparison between priceelasticity of feedstock supply and consumption. The purpose ofthis study is to conduct an economic analysis of the effects ofbiofuel on food security, analyzing in particular feedstock supplyand per capita consumption elasticity. The next section coversbiofuel development and markets at a global level, while the thirdsession covers the impact of biofuel on agricultural markets. Thefourth section discusses price elasticity of feedstock supply andconsumption. The fifth section presents the discussion and thefinal section the conclusions.

2. Biofuel development and markets

2.1. Global biofuel markets

Biofuel production surged from 45,933 million L in 2006 to117,715 million L in 2013 [17,18]. In 2013, bioethanol made up 75%of total biofuel production with the remaining 25% accounted forby biodiesel. Bioethanol production increased from 39,187 mil-lion L in 2006 to 83,353 million L in 2012 (Table 1). The USA is thelargest bioethanol producer in the world, and it produced50,397 million L in 2013. Brazil is the second largest bioethanolproducer and produced 25,530 million L in 2013. These twocountries accounted for 86% of global bioethanol production.China, France, Canada and other countries are also producingbioethanol from various feedstocks. Biodiesel productionincreased from 6,746 million L in 2006 to 29,545 million L in2013 (Table 1). The EU is the largest producer of biodiesel,producing 12,103 million L in 2013. The USA, Argentina, Brazil,Indonesia and other countries are also producing biodiesel fromvarious feedstocks.

Biofuels are produced from various feedstocks. Bioethanol isproduced from corn, sugarcane, sugarcane molasses, cassava,wheat, sweet potato, sweet sorghum and other crops. Biodieselis produced from rapeseed oil, soybean oil, palm oil, and othervegetable and animal fats. They are called the first generationbiofuels.4 Biofuels can also be produced from agricultural resi-duals, wood waste, municipal solid waste, energy crops (forexample, Jatropha) and other sources. They are called the secondgeneration biofuels.5

2.2. Biofuel use ratio for agricultural commodity

At present, the main feedstock of biofuels is agricultural product.This means biofuel consumption is competing with food and food-related demand. Bioethanol production accounted for 43.9% of totalU.S. domestic corn consumption in 2013/14.6 In the case of Brazil,the world's second-biggest bioethanol producer, more than half ofits sugarcane production has been used for bioethanol use since

2 Moreover, higher commodity prices alone are not enough; investments inproductivity and sustainability-enhancing research, enabling institutions, infra-structure and sound policies are also urgently needed. The State of Food andAgriculture 2008 also stated that market opportunities cannot overcome existingsocial and institutional barriers to equitable growth –with exclusion factors such asgender, ethnicity and political powerlessness [3].

3 Correlation and cross comparison of these studies, please see Appendix 1. Forother biofuel studies, please see Koizumi [9].

4 The first-generation biofuels usually refer to bioethanol produced from sugar-rich and starch rich crops, and to biodiesel made from oilseed crops or animal fat[15].

5 The second generation biofuels are those made from non-edible and/or lingo-cellulosic biomass, and typical outputs are lingo-cellulosic bioethanol, biomass-to-liquids, and bio-synthetic natural gas ([3,19]). In addition to the above, biofuels canalso be produced from algae. These are categorized as the third generation biofuels.

6 The Marketing year begins from September 1 every year. It is calculated fromUSDA–FAS [20] and USDA–ERS [21].

T. Koizumi / Renewable and Sustainable Energy Reviews 52 (2015) 829–841830

1990. In 2013/14,7 54.5% of sugarcane production8 was used forbioethanol production [22]. In EU27, 59.1% of rapeseed oil con-sumption was used for biodiesel in 2013/14.9

The kinds of feedstock used in biofuel production varyaccording to geographical zone. The USA, Canada, the EU andChina are producing bioethanol from corn. The total corn use forbioethanol production was estimated at 144.5 million tons in2012.10 This translates to 15.3% of global corn consumption in2013 (Table 2).11 Brazil, Paraguay, Colombia, the Philippines andThailand are producing bioethanol from sugarcane. The totalsugarcane use for bioethanol production was estimated at 346.2million tons and 18.4% of global sugarcane production in 2012.12

The EU is producing biodiesel from rapeseed oil. The total rape-seed oil use for biodiesel production was 25.0 million tons and22.4% of global rapeseed oil consumption in 2012.13 The EU,

Brazil, the USA and Argentina are all producing biodiesel fromsoybean oil. The total soybean oil use for biodiesel productionwas estimated to be 6.9 million tons, making up 15.5% of globalsoybean oil production in 2012.14 The EU, Indonesia, Malaysiaand Colombia are producing biodiesel from palm oil. The EU isimporting palm oil from Indonesia and Malaysia. The total palmoil use for biodiesel production was estimated at 5.9 million tonsin 2013,15 accounting for 10.3% of global palm oil consumption.

As mentioned above, bioethanol use accounted for 17.0% ofglobal sugarcane production and 18.4% of the global consumptionof corn in 2013. Biodiesel use accounted for 22.4% of the globaldemand for rapeseed oil, 15.5% of the world's total soybean oildemand and 10.3% of global palm oil consumption in 2013. At theother end of the scale, coconut oil use for bioethanol productionaccounted for 5.4% of global coconut oil consumption in 201316

Table 2Biofuel use ratio for agricultural commodity demand/production.Data source: estimated by author.

2004 (%) 2005 (%) 2006 (%) 2007 (%) 2008 (%) 2009 (%) 2010 (%) 2011 (%) 2012 (%) 2013 (%)

Sugarcane 13.6 14.6 14.3 16.8 19.6 21.0 20.5 16.6 16.5 18.4Corn 5.0 5.6 8.0 10.6 12.7 15.2 16.0 15.8 15.0 15.3Rapeseed oil 0.0 0.0 21.1 23.2 29.7 27.9 29.3 27.7 25.8 22.4Soybean oil – 4.8 6.1 8.3 10.1 12.2 14.3 18.1 16.8 15.5Palm oil 0.0 0.0 1.8 2.7 4.7 3.7 4.9 7.2 8.7 10.3Coconut oil 0.0 0.0 0.1 1.4 2.0 3.5 3.4 3.8 3.7 5.4Sugar beet 0.0 0.0 1.2 2.1 4.6 4.0 4.7 3.7 4.3 4.3Cassava 0.0 0.0 0.1 0.1 0.2 0.4 0.5 0.6 0.9 1.0Wheat 0.0 0.0 0.5 0.6 0.6 0.8 1.0 1.0 0.9 0.7

Global sugarcane, sugar beet and cassava production data are derived from FAO [26].Global corn, rapessed oil, soybean oil, palm oil, coconut oil, wheat consumption data are derived from USDA–FAS [20].Biofuel use data are derived from USDA–FAS [22–34], USDA–ERS [21] and Licht [35].

Table 1World biofuel production.Data source; Licht [17,18].

(Unit: million L)

2006 2007 2008 2009 2010 2011 2012 2013

Biofuel production total 45.967 60.492 81.557 90.872 104.570 108.510 109.652 117.715World bioethanol production total 39.221 49.750 66.019 73.066 84.927 84.147 83.349 88.170USA 18.381 24.552 34.968 40.728 50.088 52.805 50.350 50.397Brazil 16.700 20.000 24.200 23.920 25.530 21.020 21.622 25.530China 1.685 1.700 2.000 2.050 2.050 2.100 2.100 2.100EU 1.611 1.799 2.728 3.545 4.135 4.387 4.511 4.552Others 844 1.699 2.123 2.823 3.124 3.835 4.766 5.591World biodiesel production total 6.746 10.742 15.538 17.806 19.643 24.363 26.303 29.545EU 5.039 7.035 8.398 10.350 10.666 10.684 11.077 12.103USA 938 1.933 3.010 1.898 1.286 3.751 3.830 5.390Argentina 114 341 809 1.340 2.062 2.758 2.779 2.269Brazil 69 404 1.167 1.608 2.386 2.673 2.718 2.918Indonesia 57 278 682 568 909 1.420 1.761 2.045Others 531 750 1.471 2.042 2.333 3.077 4.138 4.819

7 Aggregated local crop year.8 There are no statistics for sugarcane demand in Brazil. Instead of sugarcane

consumption, its production was applied in this part.9 It is calculated from USDA–FAS [20] and USDA–FAS [23].10 These bioethanol use data are derived from USDA–ERS [21] and USDA–FAS

([20,23–25]). Other countries could produce bioethanol from corn, however, theydo not have any official production data.

11 World corn consumption data is derived from USDA–FAS[20].12 Sugarcane production data is derived from FAO[26]. These bioethanol use

data are derived from USDA–FAS ([22,27–30]). Other countries could producebioethanol from sugarcane, however, they do not have any official production data.

13 Rapeseed oil consumption data is derived from USDA–FAS [20]. Thesebioethanol use data are derived from USDA–FAS ([20,23]). Other countries could

(footnote continued)produce biodiesel from rape seed oil, however, they do not have any officialproduction data.

14 Soybean oil consumption data is derived from USDA–FAS[20]. Thesebioethanol use data are derived from USDA–ERS [21] and USDA–FAS ([23,31,32]).Other countries could produce biodiesel from soybean oil, however, they do nothave any official production data.

15 Palm oil consumption data is derived from USDA–FAS[20]. These bioethanoluse data are derived from USDA–FAS [33,34]. Other countries could producebiodiesel from palm oil, however, they don’t have any official production data.

16 Coconut oil consumption data is derived from USDA–FAS [20].The bioetha-nol use data are derived from USDA–FAS [29]. Other countries could producebiodiesel from coconut oil, however, they do not have any official production data.

T. Koizumi / Renewable and Sustainable Energy Reviews 52 (2015) 829–841 831

and sugar beet use for bioethanol production accounted for 4.3% ofglobal sugar beet production in 2013.17 Wheat use for bioethanolproduction accounted for 0.7% of global wheat consumption in201318 whereas Cassava use for bioethanol production accountedfor 1.0% of global cassava production in 2013.19 Consequently, thebiofuel use ratios to global demand or production for sugarcane,corn, rapeseed oil, soybean oil and palm oil are higher than theratios for the other agricultural commodities.

Biofuel markets will expand in the future. Global biofuelproduction is projected to increase from 125,583 million L in2011–2013 to 198,304 million L in 2023 [36]. It is also projectedthat most of the biofuel production will be derived from agricul-tural products. This means that the more biofuel demandincreases, the more the demand increases for agricultural feed-stock. Consequently, increasing biofuel production has impact onworld agricultural commodity markets and food security.

2.3. Biofuel developments and competition between biofuels andfood

Agricultural commodity prices (grains, soybeans, rapeseed oil,palm oil price, sugar prices and others) increased from 2004 toJune 2008. During the same period biofuel markets expanded allover the world. In July 2006, Laster Brown20 warned of animpending famine as a result of increasing of biofuel production.In April 2008, the UN secretary-general, Ban Ki-moon, called for acomprehensive review of the policy on biofuels as a result of thecrisis in global food prices. At the same time, the World BankPresident, Robert Zoellick, stated that the demand for biofuels wasa significant contributor to soaring food prices around the world.

By 2008, soaring food prices had become one of the mostimportant topics in global discussions. To address the challenges ofsoaring food prices, bioenergy and climate change, FAO organized

the high level conference on World Food Security which witnessedthe participation of one hundred eighty-one member countries inJune 2008.21

As mentioned above, from 2006 to 2008 there were manydebates about the effects of biofuel production on food security.The global discussion on biofuels and food security was covered asthe main topic on the agenda in the G8 Hokkaido Toyako Summitin July 2008. FAO published the annual report; the state of foodand Agriculture; biofuels: prospects, risks and opportunities inOctober 2008. FAO, on behalf of the UN, stressed that biofuels haveboth risks and opportunities. Biofuels may offer an opportunity fordeveloping countries – where 75% of the world's poor depend onagriculture for their livelihoods – to harness agricultural growthfor broader rural development and poverty reduction. At the sametime, there is a risk that higher food prices could threaten the foodsecurity of the world's poorest people, many of whom spend morethan half of their household incomes on food [3].22

World grain prices increased dramatically from 2006 to 2008,but in August 2008 they dropped suddenly. The main reason wasthe plunge in global financial markets, the impending economicslowdown and the fall in crude oil prices [38] in mid-2008. As aresult of the rapid drop of world grain prices, most people allaround the world believed that the “biofuel crisis” was over.Consequently, the biofuels and food security debate seemed tolose importance in the latter half of 2008.

World grain prices trended downwards from August 2008 tothe beginning of 2010 only to increase again from April 2010. Dueto the extreme heat and dryness in the Mid-West of the USA, U.S.corn production decreased to its lowest level for 6 years. Thedirector general of FAO called on the U.S. government to suspendthe Renewable Fuel Standard in August 2008. FAO organized acommittee on world food security (CFS) on 7–11 October 2013.Biofuels and food security was covered as one of the main topicson the agenda. CFS stressed that biofuel development encom-passed both opportunities and risks for economies, societies and

Fig. 1. The concept of competition between biofuels and food data source; modified from Koizumi [39].

17 Sugar beet production data is derived from FAO[26]. The bioethanol use dataare derived from USDA–FAS [23]. Other countries could produce biodiesel fromsugar beet, however, they do not have any official production data.

18 Wheat consumption data is derived from USDA–FAS[20]. The bioethanol usedata are derived from USDA–FAS ([23–25]). Other countries could producebiodiesel from wheat, however, they do not have any official production data.

19 Cassava production data is derived from FAO [26]. The bioethanol use dataare derived from USDA–FAS ([25,30]) and Licht [35]. Other countries could producebiodiesel from cassava; however, they do not have any official production data.

20 A founder of Earth Policy Institute and World Watch Institute.

21 The conference stressed the need for in-depth studies to ensure that theproduction and the use of biofuels is sustainable and takes into account the goal ofachieving and maintaining world food security [37].

22 FAO stated that technological innovation in biofuel processing could alsolower costs dramatically, potentially bringing second-generation biofuels derivedfrom cellulosic feedstocks into commercial production, thereby reducing competi-tion with agricultural crops and the pressure on commodity prices.

T. Koizumi / Renewable and Sustainable Energy Reviews 52 (2015) 829–841832

the environment, and encouraged FAO and other stakeholders tolook at ways of helping countries strengthen their capacities toaddress their situation with regards to biofuels, taking intoaccount food security concerns at global, regional and nationallevels, and legitimate land tenure rights.

3. Biofuel impact on agricultural markets and food security

3.1. Definition of competition between biofuels and food

One of the most crucial problems with increasing biofuelproduction is competition for resources with food and food-related use. The definition of competition between biofuels andfood can be divided into two dimensions. The first dimension iscompetition with food and food-related demand. The seconddimension is competition with natural and agricultural resources(Fig. 1). When biofuel demand increases, feedstock demand alsoincreases. At present, the main feedstock of biofuels all over theworld is agricultural product. The more biofuel demand increases,the more the demand increases for agricultural feedstock. Thedemand for biofuels, therefore, competes with food use, feed use,processed food, and other industrial uses, and this competition canhave a direct impact on national food consumption. This competi-tion in agricultural commodity demand for food and feed use,which is prevalent domestically and internationally, can havenegative impacts on the agricultural commodity markets andlivestock markets. For food importing countries, this is crucialconcern. In addition to this, each competition dimension can bedivided into direct and indirect competition. Indirect competitioncan cause substitution effects to alternative commodities; forexample, a soybean oil price increase can stimulate demand forother vegetable oils and increases in these prices.23

The competition for natural and agricultural resources meanscompetition for land and water use, fertilizer use, pesticide use,agricultural machinery use, labor use, capital use, and others. Suchcompetition can cause indirect land-use change.24 Such competitioncan be divided into direct and indirect competition. In this dimen-sion, the competition for land use is the most crucial problem,especially in the USA and Brazil. The next section looks at this issue.

3.2. Biofuel impact on agricultural markets

In the case of the U.S., the more bioethanol demand increases,the more the corn price increases. Corn and soybean are compet-ing for planting mainly in the Mid-West area. As a result, the areaharvested for soybeans in the U.S. could decrease, particularly inthe Mid-West area. The major effects of U.S. bioethanol expansionon U.S. agriculture occur through corn prices trickling down toother feed grains and other crop prices, inducing significant U.S.land allocation changes. These price effects also transmit world-wide, especially for feed grains and soybeans, and thus affect theland allocation in countries such as Argentina and Brazil [42].Therefore, due to the increasing bioethanol demand, the soaringcorn price can affect not only U.S. but also international land use.

In the case of Brazil, the area for sugarcane cultivation hasexpanded as a result of increasing bioethanol demand. Sugarcaneis competing for land with other crops such as coffee beans,oranges, cotton, rice and others. This competition is causing

changes in land use patterns, especially in Sao Paulo state (directland use change).

The indirect impact on food security and the environment can becaused by direct and indirect land-use change. Indirect land-usechange occurs when the feedstock for biofuel production are nottriggering land-use change on-site but elsewhere, due to the needto compensate for production now used for biofuels. This is whybiofuels might not cause land-use change locally, but may well“displace” food or pasture land for livestock production, which thenmoves to other regions, and can be, in turn, responsible fordeforestation [43]. Bioethanol and biodiesel programs may causeindirect land-use change as a result of expanding production. Thereare concerns that such expansion could generate indirect pressureto push cattle into Amazonia leading to further deforestation in thatarea, but there is no direct evidence for this [44]. Even if no directland-use change is observed (biofuels are produced on existingcropland), such biofuel production could “push” the displaced food/feed or other production towards forests and grasslands, in a“domino” effect, through indirect land-use change [15].

3.3. Relationship between biofuel and food security

The relationship between biofuels and food security has5 stages (Fig. 2). The first stage, which is the competition betweenbiofuels and food, occurs in each country and region.

The second stage is the price reaction as a result of thecompetition. Soaring agricultural commodity prices caused bythe competition are not always linked to national food prices.Some agricultural commodity prices will increase, but others willremain unchanged or decrease.25

During the third stage we see competition effect transmitted tofood prices. The link between agricultural commodity prices andfood prices can depend on the country and market structures.Agricultural commodity prices do not link with food prices instraight forward way. The rates of increase in food prices areusually lower than those of agricultural commodity prices. Thefourth stage shows price reaction as a result of price transmission.The level of integration of a domestic market with the interna-tional markets differs per country.26 When agricultural commodityprices increase, agricultural commodity supply will increase with atime lag. The rate of increase of supply depends on price elasticityof agricultural supply as discussed in the following section. Forsome countries, food prices may increase, but for other countries,they may remain unchanged or decrease. The fifth stage demon-strates the effect of soaring food prices on food security. Increasingfood prices, caused by the competition between biofuels and food,can have a more negative impact on lower income consumers thanon higher income consumers, because lower income consumersspend a larger portion of their income on food expenditure and ofwhich staple food commodities represent a larger part [46].Consequently, consumers in low-income and food-deficit coun-tries are more vulnerable than in developed countries. It dependson the Engel coefficient on each household.

Soaring food prices can have a negative impact on food securityat national, sub-national, household and individual levels. Whenthis occurs, monitoring and policy recommendations are required

23 This section is mainly derived from Koizumi [39].24 The basic concept of indirect land-use change is that natural ecosystems

elsewhere might be converted to croplands to replace crops (either animal feed orfood) that are lost due to biofuel production [40]. This can imply land-use change(by changing, e.g., forest into agricultural land), which can result in substantialamounts of CO2 emissions being released into the atmosphere [41].

25 For examples, the increasing biodiesel blend ratio in Brazil will have diverseresults for the world biodiesel market, as well as the soybean oil, soybean meal, andsoybean markets. The result of this study shows that the increasing biodiesel blendratio in Brazil will not always have negative impacts on agricultural and foodmarkets. For more detailed information, please refer to Koizumi [9].

26 The extent and timing of the linkage between agricultural commodity pricesand food prices depends on several factors; the structure of the markets,infrastructure and marketing costs, agricultural and trade policy measures, localcurrency valuation, the level of self-sufficiency, amount of product typically lost towaste, spoilage and theft, and degree of processing of final consumption goods [45].

T. Koizumi / Renewable and Sustainable Energy Reviews 52 (2015) 829–841 833

by the UN and international organizations. On the other hand,soaring food prices can contribute to agricultural and rural devel-opment, and an increase in food supply and the food self-sufficiencyratio at a national level.27 The competition between biofuels andfood does not always equate to a negative impact on food security.

4. Price elasticity of feedstock supply and consumption

4.1. Demand and supply curve of feedstock markets

Biofuel programs have created a new market for agriculturalcommodity markets, because most of the feedstock is currently

made up of agricultural products. Biofuel programs have causedstructural change in agricultural commodity markets. Oladosu andMsangi [47] illustrated the price changes in the food market undersupply shock. This study developed them. Fig. 3 shows the supplyand demand curve of agricultural commodity in the long-term.28

Most of the demand curve for agricultural commodities is inelasticto price change. As a result of biofuel programs, biofuel demandfor agricultural feedstock will increase from D1 to D2. If the supplycurve for agricultural feedstock is inelastic to price change,agricultural commodity prices will increase from P0 to P1 level(Fig. 3A).

As a result of the price increase of the agricultural commodity,farmers will increase their production; such as increasing their

Fig. 3. Demand and supply curve shift of agricultural commodity.

Fig. 2. The relationship between biofuels and food security data source; modified from Koizumi [9].

27 We have to be careful of the impact of indirect land-use change. 28 Price elasticity of supply is inelastic to price change in the short term.

T. Koizumi / Renewable and Sustainable Energy Reviews 52 (2015) 829–841834

planting areas and agricultural investment with a time-lag due to theproduction cycle. Consequently, the supply curve can shift from S1 toS2. As a result of the supply curve shift, the agricultural commodityprice will decrease to P2 level (Fig. 3B). In the case of Fig. 3A and B, thesupply curves are inelastic to price change. If demand and supply areinelastic to price change, the price will have to rise.

Fig. 3C shows that the supply curve is elastic to price change. Atfirst, the price will increase from P0 to P3, and the supply curvewill shift from S1 to S2. As a result, the price will decrease to P4level. The price will be lower and supply will increase, comparedwith Fig. 3A and B. Consequently, the elasticity of the supply curveis the key factor in determining the agricultural commodity priceand the increase in supply resulting from the price change, as aresult of an increase in biofuel demand.

4.2. Methodology

Based on the theory outlined above, this section demonstratesan econometric analysis of price elasticity of both feedstock supplyand consumption for main feedstock. The purpose of this analysisis to compare the price elasticity of both feedstock supply anddemand. It focuses on the price elasticity of feedstock supply forsugarcane (Brazil), soybean (Brazil), corn (USA), soybeans (USA),palm oil (Indonesia), palm oil (Malaysia), cassava (Thailand) andcorn (China). It also estimates price elasticity of feedstock con-sumption elasticity for sugar (Brazil), soybean oil (Brazil), corn(USA), soybean oil (USA), palm oil (Indonesia), palm oil (Malaysia),cassava (Thailand) and corn (China). However, there are noconsumption data for sugarcane, because sugarcane is not con-sidered to be a final consumption good. Therefore this studyapplies the price elasticity of sugar consumption instead of thatof sugarcane. The soybean crop in Brazil and the USA is mainlyused to produce soybean oil and therefore, this study utilizes theprice elasticity of soybean oil consumption.

This study uses time-series data to estimate price elasticity offeedstock supply and demand. Agricultural commodity productiondata are taken from MAPA data [48] for Brazil, PS&D [20] for the

US market, and FAOSTAT [26] for the others. Producer price dataare derived from USDA–ERS [49,50] for the US market andFAOSTAT [26] for other countries. Consumption data are derivedfrom FAOSTAT [26]. Population data are derived from WorldPopulation Prospects: the 2012 revision [51]. Per capita GDP dataare derived from the World Bank [52].

This study applies OLS to examine price elasticity of feedstocksupply and demand. Feedstock production equation depends onown price and substitute price as follows:

ln QPFt;c=QPFt�1;c� �¼ Cþa1 ln PPOt�1;c=PPOt�2;c

� �

þa2 PPSt�1;c=PPSt�2;c� �þa3 ln Tt=Tt�1

� �

where QPF is feedstock production, C is constant, PPO is producerprice of own feedstock, PPS is producer price of substitute feed-stock, T is time-trend, t is time, c is country, a1 is own priceelasticity, a2 is substitute price elasticity and a3 is time trendelasticity. As for each feedstock supply equations of sugarcane(Brazil), soybean (Brazil and USA), corn (USA and China), palm oil(Indonesia and Malaysia) and cassava (Thailand), please refer toAppendix 1.

Feedstock per capita consumption equation depends on ownprice, substitute price, per capita GDP and time trend as follows:

ln PQCFt;c=PQCFt�1;c� �¼ Cþa4 ln PPOt;c=PPOt�1;c

� �þa5 PPSt;c=PPSt�1;c� �

þa6 ln PCGDPt;c=PCGDPt�1;c� �þa7 ln Tt=Tt�1

� �

where PQCF is per capita consumption of feedstock, C is constant,PCGDP is per capita GDP, T is time-trend, t is time, c is country, a4 isown price elasticity, a5 is substitute price elasticity, a6 is incomeelasticity and a7 is time-trend elasticity. As for per capita con-sumption equations of sugar (Brazil), soybean oil (Brazil and USA),corn (USA and China), palm oil (Indonesia and Malaysia), andcassava (Thailand), please refer to Appendix 2.

4.3. Results

As results of the analysis, Table 3 shows the supply elasticity ofthe main agricultural feedstock; sugarcane (Brazil), soybeans

Table 3Supply elasticity for agricultural commodities.Data source; estimated by author.

Dependent variable Sugarcane production (Brazil) Soybean production (Brazil) Corn production (USA) Soybeans production (USA)

Intercept 12.873850 0.363661 8.988448 7.742702Price elasticity (own) 0.429946 0.349350 0.201038 0.324977Price elasticity (substitute) – �0.059825 �0.197847 �0.307757Linear trend 1.866189 2.750135 0.932118 0.928882Dummy1 �0.255660o20024 0.168646o20024 �0.229658o19934 �0.189130o19934Dummy 2 0.408073o20094 -0.191949o20084 �0.124551o20024 0.148151o19974Dummy 3 – – �0.176513o20124 -0.132784o20034Observation numbers 23 24 23 23Observation numbers 1990–2012 1990–2012 1990–2012 1990–2012R2 0.965166 0.981157 0.882265 0.953042Adj R2 0.957245 0.975923 0.827322 0.935433

Dependent variable Palm oil production (Indonesia) Palm oil production (Malaysia) Cassava production (Thailand) Corn production (China)Intercept �7.598778 0.829437 16.168070 11.312380Price elasticity (own) 0.045982 0.019429 0.157131 0.129398Price elasticity (substitute) – – – –

Linear trend 4.500679 2.290733 0.006848 0.208894Dummy1 �0.207279o19974 �0.114963o19974 �0.141515o19964 �0.355841o19964Dummy 2 – – 0.269168o20074 �0.007969o20004Dummy 3 – – 0.194240o20084 –

Dummy 4 – – 0.283702o20094 –

Observation numbers 23 23 23 23Observation numbers 1990–2012 1990–2012 1990–2012 1990–2012R2 0.994105 0.989513 0.770808 0.736649Adj R2 0.993174 0.987857 0.665027 0.670811

T. Koizumi / Renewable and Sustainable Energy Reviews 52 (2015) 829–841 835

(Brazil), corn (USA), soybeans (USA), palm oil (Indonesia andMalaysia), cassava (Thailand) and corn (China). Table 4 showsper capita consumption elasticity of this feedstock. Most of theabsolute values of each supply elasticity are higher than per capitaconsumption elasticity. In the case of sugarcane (Brazil), soybeans(Brazil) and corn (USA), the absolute value of own price elasticityof supply is much higher than that of per capita consumption(Case1). Case 1 feedstock supply is expected to increase as resultsof a price increase promoted by biofuel programs. The own priceelasticity of sugarcane supply in Brazil is 0.42995, and the ownprice elasticity of per capita consumption of sugar is �0.02952.This means that sugarcane production is elastic to price change.The own price elasticity of soybeans supply in Brazil is 0.34935,and the own price elasticity of per capita consumption of soybeanoil is �0.06712. The own price elasticity of soybeans supply in USAis 0.32498, and the own price elasticity of per capita consumptionof soybean oil is �0.07853. The own price elasticity of corn supplyin USA is 0.2010, and the own price elasticity of per capitaconsumption of corn is �0.0926. They mean that Braziliansoybeans, the U.S. soybeans and corn are elastic to price change.This feedstock may be an effective feedstock at responding to pricechange with a rapid increase in supply.

In the case of palm oil (Indonesia and Malaysia) and corn(China), the absolute value of own price elasticity of supply ishigher than that of per capita consumption (Case 2). The own priceelasticity of palm oil supply in Indonesia is 0.04598, and the ownprice elasticity of per capita consumption of palm oil is �0.00575.The own price elasticity of palm oil in Malaysia is 0.01943, and the

own price elasticity of per capita consumption of palm oil is�0.00443. The own price elasticity of corn supply in China is0.12940, and the own price elasticity of per capita consumption ofpalm oil is �0.04350. However, the differences between supplyand per capita consumption elasticity are much lower than in theCase 1 feedstock. The absolute values of own price elasticity ofsupply are not much higher than those of per capita consumption.The price adjustment caused by an increase biofuel demand isweaker than Case 1 feedstock. In Case 2 feedstock, there can be anincrease in feedstock supply, but the price adjustment from theprice change is weaker than in Case 1 feedstock.

In the case of cassava (Thailand), the absolute value of ownprice elasticity of supply is lower than that of consumption (Case3). The own price elasticity of cassava supply in Thailand is0.15713, and the own price elasticity of per capita consumptionof cassava is �0.23919. In this case, it is difficult to decrease theagricultural commodity price when feedstock demand increasesand the price adjustment is weaker than Case1 and Case2 feed-stock. Case 3 feedstock is not an effective feedstock at adjusting itssupply in response to the price change resulting from biofuelprogram development.

5. Discussion

A comparison between price elasticity of agricultural feedstocksupply and that of per capita consumption demonstrates that inthe case of sugarcane (Brazil), soybeans (Brazil) and corn (USA) the

Table 4Per capita demand elasticity for agricultural commodities.Data source; estimated by author.

Dependentvariable

Sugar per capita consumption(Brazil)

Soybean oil per capitaconsumption (Brazil)

Corn per capita consumption(USA)

Soybean oil per capitaconsumption (USA)

Intercept 5.661670 3.279930 4.350966 1.368002Price elasticity

(own)�0.029519 �0.067124 �0.092566 �0.078525

Price elasticity(substitute)

– 0.108989 0.039821 –

Per capita GDP 0.440945 0.435164 0.340308 1.089132Linear Trend 0.944240 1.284622 0.608442 0.497859Dummy1 – – 0.078170o19864 �0.146890o20094Dummy 2 – – 0.133545o20104 �0.134967o20104Dummy 3 – – – –

Observationnumbers

28 33 28 23

Observationnumbers

1985–2012 1980–2012 1985–2012 1990–2012

R2 0.983802 0.917630 0.897182 0.808076Adj R2 0.981777 0.905863 0.867806 0.736105

Dependentvariable

Palm oil per capita consumption(Indonesia)

Palm oil per capita consumption(Malaysia)

Cassava per capita consumption(Thailand)

Corn per capita consumption(China)

Intercept �1.454770 �0.486215 2.427881 5.693537Price elasticity

(own)�0.005472 �0.004425 �0.239186 �0.043499

Price elasticity(substitute)

– – – –

Per capita GDP 0.628456 0.892945 0.373831 0.489768Linear Trend 2.575525 2.107189 0.650893 0.612300Dummy1 – – 0.686024o19944 –

Dummy 2 – – 0.624737o20084 –

Dummy 3 – – – –

Observationnumbers

21 33 21 22

Observationnumbers

1992–2012 1980–2012 1992–2012 1991–2012

R2 0.956039 0.956637 0.746508 0.883314Adj R2 0.948281 0.952152 0.662011 0.854143

T. Koizumi / Renewable and Sustainable Energy Reviews 52 (2015) 829–841836

absolute value of own price elasticity of supply is much higherthan that of per capita consumption. Sugarcane is a perennial cropand there is a 2–4 year time lag when responding to price changes.However, Brazil has abundant arable land which can be used toincrease sugarcane production. An increase in sugarcane areaharvested has contributed to an increase in its production. Thismeans that Brazilian sugarcane is elastic to price change and it canbe an effective feedstock at responding to price changes with rapidincreases in supply. Sugar is categorized as food in many countries,but it is not a cereal and staple food and is unlikely to havenegative impact on food security.

To reduce its oil import bill, the government of Brazil inaugurated anational bioethanol program (PROALCOOL) in 1975. An importantdirect effect of the program was the creation of huge domesticconsumption for its sugarcane market, as sugarcane is the feedstockof bioethanol production. As a result of PRPALCOOL, Brazilian bioetha-nol production has increased from 1975, and its sugarcane productionhas also increased to satisfy the increased demand for feedstock.Another effect is that the Brazilian bioethanol program has alsocontributed agricultural and rural development. The number of formaljobs in the Brazilian bioethanol, sugar and sugarcane industriesincreased from 642.8 thousand in 2000 to 982.6 thousand in 2005[53] and, in total, it is thought that the program has been directlyresponsible for creating about one million jobs. In addition, it isestimated that another one million job opportunities have beencreated in related sectors, such as the transportation sector, theagricultural machineries sector and the agricultural chemical sector[53]. Bioethanol, sugar and sugarcane industry advertisements claimprofits amounted to 28 billion USD and accounted for 1.8% of total GDPin 2011. The bioethanol and sugar share of total agribusiness exportswas 18.0% in 2010[53]. In addition, bioelectricity from bagasse isgrowing and becoming a new energy industry in Brazil, and thereforethe Brazilian bioethanol industry, including bioelectricity, is becomingone of Brazil's main industries. As we have seen, PROALCOOL hassuccessfully contributed to agricultural and rural development andBrazilian sugarcane is the most effective biofuel feedstock to use for itsbiofuel program.

Brazilian soybeans are also elastic to price change as they are aneffective feedstock at responding to price change with rapidincrease in supply. Brazilian soybeans production has satisfied theincreasing consumption of soybean oil and meal from the interna-tional market, especially from China. Its soybeans area harvestedhas also increased, contributing to the growth in production.

U.S. corn and soybeans can also be effective feedstock at respond-ing to price change with rapid increase in their supply. The U.S. cornand soybeans production has satisfied increasing domestic and inter-national consumption for food, feed and energy purposes. An expan-sion in corn and soybeans area harvested contributed to the increasein corn and soybeans production in the USA.

In the case of palm oil (Indonesia and Malaysia) and corn(China), the absolute values of own price elasticity of supply arenot much higher than those of per capita consumption, and thesupply increase and price adjustment caused by an increase inbiofuel demand is weaker than those of Brazilian sugarcane and U.S. corn. Palm oil is not a staple food in many countries, but it iscategorized as a necessary good in Indonesia and Malaysia.Stabilizing the domestic palm oil price is a political priority forthe governments of Indonesia and Malaysia. Indonesian andMalaysian policy makers recognize that price adjustments areweaker than case 1 feedstock. Consequently, they have to takeaction in order to stabilize domestic palm oil prices, when theypromote an increase in biodiesel production by their programs.

The Chinese government has promoted bioethanol programssince 2002, using corn as the main feedstock. When the govern-ment started to expand the corn-based bioethanol program, cornending stocks were abundant and the government tried to

eliminate them. However, in September 2006 the domestic cornwholesale price began to increase, because the Chinese cornsupply and demand situation had become tight due to the factthat corn consumption for bioethanol was competing with cornconsumption for food, feed and other industries. For this reason,the Chinese government started to regulate corn-based bioethanolexpansion in December, 2006. This regulation allowed bioethanolproduction to continue at the 2006 level, but limited furtherexpansion of corn-based bioethanol production. This was anappropriate policy decision by the Chinese government (TheEnergy Bureau of the National Development and Reform Commis-sion (NDRC)). This is because Chinese corn supply increase andprice adjustment are weaker than in the case of Brazilian sugar-cane or U.S. corn. Chinese policy makers recognized this priceadjustment mechanism and their decision could have helpedmitigate the soaring of the world corn price from the end of2006 to mid-2008. Without this decision, the world corn pricecould have been much higher.

Using Cassava as feedstock may not contribute to agriculturaldevelopment, because its price elasticity to supply is much lowerthan that of per capita consumption. Cassava is not an effectivefeedstock because of the relative difficulty in adjusting its supplyin response to the price change resulting from biofuel programdevelopment. In 2013, 2.231 thousand tons of cassava was used forbioethanol production in Thailand [30]. The cassava use ratio ofbioethanol production increased from 0.7% in 2006 to 7.4% in2013.29 When the government promotes an expansion of bioetha-nol production from cassava in Thailand, it risks causing a feed-stock price increase. Consequently, policy makers should recognizethis point and may have to suspend increases in bioethanolproduction from cassava or promote the use of other feedstock.

Thus, policy makers should recognize the importance of priceelasticity of feedstock supply when they promote biofuel programsand select feedstock which can contribute to agriculturaldevelopment.

6. Conclusions

To deal with energy security, to reduce greenhouse gas (GHG)emissions, and to strengthen agricultural/rural development, biofuelsare being produced and utilized all over the world. One of the mostcrucial problems with increasing biofuel production is that it competeswith food and food related use for natural and agricultural resources,because the main feedstock is agricultural product. Increasing biofuelproduction will have impact on world agricultural commodity pricesand food security at global, national, household and individual levels.

Soaring food prices can have a negative impact on food securityat national, sub-national, household and individual levels. Theown price elasticity of supply equation in the long-term is the keyfactor in determining agricultural commodity price adjustments asresults of rises in biofuel demand. Biofuels may have a negativeimpact on food security, but on the other hand they can createopportunities for agricultural development. Critically, the ownprice elasticity of feedstock supply is the key factor in decidingwhether biofuel development can contribute to agricultural devel-opment or not. Using Brazilian sugarcane as an example, acomparison between price elasticity of agricultural feedstocksupply and that of per capita consumption demonstrates that theprice elasticity of sugarcane supply in Brazil is much higher thanother feedstock. It means that sugarcane supply is elastic to pricechange and it is an effective feedstock at responding to pricechange with a rapid increase in supply. Consequently, Brazilian

29 The production data are derived from FAOSTAT [26].

T. Koizumi / Renewable and Sustainable Energy Reviews 52 (2015) 829–841 837

sugarcane is the most effective biofuel feedstock to use for theBrazilian biofuel program as it promotes agricultural development.

Brazilian soybeans, U.S. soybeans and corn are also elastic toprice change and they are effective feedstock at reacting to pricechange with a rapid rise in supply. However, in the case of palm oilin Indonesia and Malaysia, and corn in China, the absolute valuesof own price elasticity of supply are not much higher than those ofper capita consumption and the supply increases and the priceadjustments caused by an increase in biofuel demand are weakerthan those seen with Brazilian sugarcane and soybeans and U.S.soybean and corn. Consequently, these policy makers have to takesteps to stabilize palm oil prices when they promote increases inbiodiesel production. The Chinese government started to regulatecorn-based bioethanol expansion at the end of 2006, which was anappropriate policy decision that could have helped mitigate thesoaring of the world corn price from the end of 2006 to mid-2008.

On the other hand, using cassava as feedstock is unlikely tocontribute to agricultural development, because its price elasticityto supply is much lower than that of per capita consumptionresulting in very weak price adjustments. Consequently, policymakers should recognize the importance of price elasticity offeedstock supply when they promote biofuel programs and selectfeedstock which can contribute agricultural development.

However, the price elasticity of feedstock is not the only absolutecriteria for biofuel development. For example, the absolute value ofown price elasticity of supply in U.S. corn is much higher than that ofper capita consumption and it may be an effective feedstock atresponding to price change with rapid increase in supply. However,U.S. corn is used for feed and food, and care must be taken of the directeffect on food importing countries and the indirect effects on livestockmarkets. Consequently, the U.S. policy makers should fully considerthe direct and indirect effect that using corn as feedstock can have onfood security. In the case of in Brazilian sugarcane and soybeans, andthe U.S. soybeans and corn, the own price elasticity of supply is higherthan that of per capita consumption, because both Brazil and the USAhave abundant arable land in comparison to other countries. Further-more, Brazil can potentially increase its biofuel production due to thisabundance of arable land. However, such expansion could cause directand indirect land use change with undesirable environmental effects.Consequently, in addition to conducting an economic analysis, policymakers will also have to consider the impact of biofuel production onenvironmental sustainability.

This study did not conduct sensitivity analysis of the parametersconsidered in the econometric model. The author will conduct itafter this study. The future direction of the study is also to examinehow biofuel policies and markets can impact food security at aglobal, national, sub-national and household level, using a partialequilibrium (PE) model, or a computable general equilibrium (CGE)model. This study did not examine price elasticity of rapeseed oiland other vegetable oils used by EU countries, which would alsoneed to be included in the future. Moreover, this study does notcover how biofuel programs can contribute to rural developmentand future studies also need to analyze the impact of biofuel onenergy security, GHG reduction, biodiversity, and the effect onsociety at a global and national level in more depth.

Acknowledgments

The author would like to thank Mr. Ralph Trimnell for reviewingthis article. The author also thanks the editor and reviewers of thearticle.

Appendix 1

See the Appendix Table A1. Table

A1

Mainstudiesforbiofuel

impacton

agricu

lturalco

mmod

ityan

dfood

prices.

Source:

revisedby

Koizu

mi[9].

Source

Methodology

Biofu

elim

pac

tonag

ricu

lturalco

mmodityan

dfoodprice

s

Mitch

ell[4]

Econ

omic

mod

el70

–75

%of

theincrea

sein

food

commod

itiespriceswas

dueto

world

biofuelsan

drelatedco

nsequ

encesof

low

grainstoc

ks,large

landuse

shifts,speculative

activity

andex

portba

ns.

Roseg

rant[5]

Econ

omic

mod

elIncrea

sedbiofuel

dem

andis

foundto

hav

eacco

untedfor30

%of

theincrea

sein

weigh

tedav

erag

egrainprices,39

%of

theincrea

sein

real

maize

prices,21

%of

theincrea

sein

rice

prices

and22

%of

therise

inwhea

tprices.

FAO

[3]

Econ

omic

mod

elIn

thecase

ofa15

%reductionin

biofuel

feed

stoc

kuse

by20

10,w

orld

maize

priceswou

ldbe

5%lower,v

egetab

leoilp

rices3%

lower

andsu

garprices10

%lower

than

baselin

escen

ario.

Anincrea

seof

30%in

biofuel

feed

stoc

kuse

by20

10wou

ldcause

pricesin

that

year

toincrea

seby

asmuch

as26

%in

thecase

ofsu

garan

dby

11%an

d6%

,respective

ly,for

maize

and

vege

tableoil.

Tokg

ozet.al[6]

Econ

omic

mod

elEx

pan

ded

U.S.b

ioethan

olproductionwill

cause

long-runcrop

pricesto

increa

se.Inresp

onse

tohigher

feed

costs,liv

estock

farm

gate

priceswill

increa

seen

ough

toco

verthefeed

cost

increa

ses.

Retailmea

t,eg

gan

ddairy

priceswill

also

increa

se.

National

Research

Cou

ncil[7]

Econ

omic

mod

elU.S.b

iofuel

policyco

ntributed20

–40

%of

food

commod

ityprices

IEEP

[8]

Econ

omic

mod

elEU

biofuel

policyhas

range

ofprice

effects(8–20

%on

oilsee

ds,1–

36%on

vege

tableoils,1

–22

%on

cereals/co

rn,1

–13

%on

whea

tan

d1–

21%on

suga

r).G

loba

l/multi-region

albiofuel

man

dates

has

range

ofprice

effects(2–7%

onoilsee

ds,

35%on

vege

tableoils,1

–35

%on

cereals/maize

and1–

8%on

whea

t)

T. Koizumi / Renewable and Sustainable Energy Reviews 52 (2015) 829–841838

Appendix 2

Estimated equations1. Production

1) Sugarcane Prodution in Brazil

ln QPSCt;c=QPSCt�1;c� �¼ 12:8739þ0:429946 ln PPSt�1;c=PPSt�2;c

� �

þ1:866189 ln Tt=Tt�1� �

35:22006ð Þ 5:512980ð Þ 18:81748ð Þ�0:255660 DUM02þ0:408073 DUM09 �32:69055ð Þ 4:571460ð Þ

2) Soybeans production in Brazil

ln QPSt;sc=QPSt�1;sc� �¼ 0:363661þ0:349350 ln PPSOt;c=PPSOt�1;c

� �

�0:059825 ln PPCt;c= 1:056823ð Þ�

� 2:066550ð Þ �0:471066ð ÞPPCt�1;c�

þ2:750135 ln Tt=Tt�1� �þ0:168646DUM02

�0:191949DUM08 29:59429ð Þ 2:167158ð Þ �2:212619ð Þ

3) Corn production in USA

ln QPCt;c=QPCt�1;c� �¼ 8:988448þ0:210383 ln PPCt;c=PPCt�1;c

� �

�0:197847 ln PPSOt;c�

= 20:44235ð Þ 2:072957ð Þ �1:677548ð ÞPPSOt�1;c�

þ0:932118 lnðTt=Tt�1Þ�0:229658DUM93�0:124551Dum02 7:873554ð Þ �2:609618ð Þ �1:477626ð Þ�0:176513DUM12 �1:945693ð Þ

4) Soybeans production in USA

ln QPSt;c=QPSt�1;s� �¼ 7:742702þ0:324977 ln PPSOt;c=PPSOt�1;c

� �

�0:407757 ln PPCt;c 31:58915ð Þ 3:277818ð Þ�

� �5:255954ð Þ=PPCt�1;c�

þ0:928882 lnðTt=Tt�1Þ�0:189130DUM93þ0:148151DUM97� 14:15915ð Þ �4:030454ð Þ� 2:900043ð Þ0:132784DUM03 �2:899899ð Þ

5) Palm oil production in Indonesia

ln QPPt;c=QPPt�1;s� �¼ �7:598778þ0:045982 ln PPPt;c=PPPt�1;c

� �

þ4:500679 lnðTt=Tt�1Þ �25:06146ð Þ 0:787506ð Þ 55:37312ð Þ�0:207279DUM97 �3:246257ð Þ

6) Palm oil production in Malaysia

ln QPPt;c=QPPt�1;s� �¼ 0:829437þ0:019429 ln PPPt;c=PPPt�1;c

� �

þ2:290733 lnðTt=Tt�1Þ 4:017793ð Þ 0:488715ð Þ 41:39397ð Þ�0:114963 DUM97 �2:644393ð Þ

7) Cassava production in Thailand

ln QPCAt;c=QPCAt�1;s� �¼ 16:16807

þ0:157131 ln PPCAt;c=PPCAt�1;c� �

þ0:006848 lnðTt=Tt�1Þ 61:90381ð Þ 1:940258ð Þ 1:464073ð Þ

�0:141515 DUM96þ0:269168 DUM07þ0:194240 DUM08þ0:283702 �1:200271ð Þð2:456347Þ 1:753178ð Þ� 2:284623ð ÞDUM09

8) Corn production in China

ln QPCt;c=QPCt�1;s� �¼ 11:31328þ0:129398 ln PPCt;c=PPCt�1;c

� �

þ0:208894 ln Tt=Tt�1� �

148:6596ð Þ 1:374342ð Þ 6:387063ð Þ�0:355841DUM96�0:007969DUM00 �2:268783ð Þ�ð�2:268783Þ

where QPSC is sugarcane production, PPS is producer price ofsugar, T is time-trend, DUM02 is dummy variable for 2002,DUM09 is dummy variable for 2009, t is time, c is country, QPS issoybeans production, PPSO is producer price of soybean oil, PPCis producer price of corn, DUM08 is dummy variable for 2008,QPC is corn production, DUM93 is dummy variable for 1993,DUM12 is dummy variable for 2012, DUM97 is dummy variablefor 1997, QPP is palm oil production, PPP is producer price ofpalm oil, CPCA is cassava production, PPCA is producer price ofcassava, DUM96 is dummy variable for 1996, DUM07 is dummyvariable for 2007, and DUM00 is dummy variable for 2000.

2. Per capita consumption equations

1) Sugar per capita consumption of sugar in Brazil

ln PQCStc=PQCSt�1;c� �¼ 5:661670�0:029519 ln PPSt;c=PPSt�1;c

� �

þ0:440945 ln PCGDPt;c�

= 61:00477ð Þ �1:280638ð Þ 2:240671ð ÞPCGDPt�1;c�

þ0:944224 ln Tt=Tt�1� �

36:78244ð Þ

2) Per capita consumption of soybean oil in Brazil

ln PQCSOtc=PQCSOt�1;c� �¼ 3:279930�0:067124 ln PPSOt;c=PPSOt�1;c

� �

þ0:440945 ln PPCOt;c= 11:44290ð Þ�

� �0:299412ð Þ 0:638624ð Þ PPCOt�1;c�

þ0:435164 ln PCGDPt;c=PCGDPt�1;c� �

þ1:284622 0:695105ð Þ 15:95637ð Þ ln Tt=Tt�1� �

3) Per capita consumption of corn in USA

ln PQCCtc=PQCCt�1;c� �¼ 4:350966�0:092566 ln PPCt;c=PPCt�1;c

� �

þ0:039821 ln PPSOt;c= 20:35315ð Þ�

� �0:984899ð Þ 0:409504ð ÞPPSOt�1;c�

�0:340308 ln PCGDPt;c=PCGDPt�1;c� �

þ0:608442 ln Tt=Tt�1� � �1:967279ð Þ 10:61133ð Þ

þ0:078170 DUM86þ0:133545 DUM10 1:144212ð Þ 2:158778ð Þ

4) Per capita consumption of soybean oil in USA

ln PQSOtc=PQSOt�1;c� �¼ 1:368002�0:078525 ln PPSOt;c=PPSOt�1;c

� �

þ0:38913 ln PCGDPt;c= 5:465253ð Þ�

� �0:812967ð Þ 5:465253ð ÞPCGDPt�1;c�

þ0:4978592 ln Tt=Tt�1� �

�0:146890 DUM09 7:268457ð Þ �2:011343ð Þ�0:134967 DUM10 �2:934172ð Þ

T. Koizumi / Renewable and Sustainable Energy Reviews 52 (2015) 829–841 839

5) Per capita consumption of palm oil in Indonesia

ln PQCPtc=PQCPt�1;c� �¼ �1:454770�0:005472 ln PPPt;c=PPPt�1;c

� �

þ0:628456 ln PCGDPt;c= �2:865825ð Þ �0:088534ð Þ�

� 1:438408ð Þ =PCGDPt�1;c�

þ2:575525 ln Tt=Tt�1� �

19:01334ð Þ

6) Per capita consumption of palm oil in Malaysia

ln PQCPtc=PQCPt�1;c� �¼ �0:486215��0:004425 ln PPPt;c=PPPt�1;c

� �

þ0:892945 ln PCGDPt;c= �1:572837ð Þ�

� �0:049513ð Þ 1:408431ð Þ PCGDPt�1;c�

þ2:107189 ln Tt=Tt�1� �

24:69043ð Þ

7) Per capita consumption of cassava in Thailand

ln PQCCAtc=PQCCAt�1;c� �¼ 2:427881�0:239186 ln PPCAt;c=PPCAt�1;c

� �

þ0:373831 ln PCGDPt;c= 3:454675ð Þ�

� �1:161898ð Þ 0:182327ð Þ PCGDPt�1;c�

þ0:650893 ln Tt=Tt�1� �

þ0:686024DUM94 6:122725ð Þ 1:769430ð Þþ0:624737 DUM08 1:556980ð Þ

8) Per capita consumption of corn in China

ln PQCCtc=PQCCt�1;c� �¼ 5:693537�0:43499 ln PPCt;c=PPCt�1;c

� �

þ0:095754 ln PPSBt;c=PPSBt�1;c� �

40:26285ð Þ� �0:310906ð Þ 0:392699ð Þþ0:489768 ln PCGDPt;c=PCGDPt�1;c

� �

þ0:612300 ln Tt=Tt�1� �

1:327215ð Þ 9:963405ð Þ

where PQCS is per capita consumption of sugar, PPS is producerprice of sugar, PCGDP is per capita GDP, T is time-trend, t is time, cis country, PQCSO is per capita consumption of soybean oil, PPSO isproducer price of soybean oil, PPCO is producer price of corn oil,PQCC is per capita consumption of corn, PPC is producer price ofcorn, DUM86 is dummy variable for 1986, DUM10 is dummyvariable for 2010, PQSO is per capita consumption of soybean oil,DUM09 is dummy variable for 2009, DUM10 is dummy variable for2010, PQCP is per capita consumption of palm oil, PPP is producerprice of palm oil, PQCAA is per capita consumption of casava, PPCAis producer price of casava, DUM94 is dummy variable for 1994,DUM08 is dummy variable for 2008, and PPSB is producre price ofsoybeans.

Note: the figure under each elasticity is a t-Value.

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