ECONOMICS DEPARTMENT WORKING PAPER. Department of Economics Tufts University Medford, MA (617)

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1 ECONOMICS DEPARTMENT WORKING PAPER 2015 Depatment of Economics Tufts Univesity Medfod, MA (617)

2 The Long Run Impact of Biofuels on Food Pices by Ujjayant Chakavoty, Maie-Hélène Hubet, Michel Moeaux and Linda Nøstbakken 1 Abstact Moe than 40% of US con is now used to poduce biofuels, which ae used as substitutes fo gasoline in tanspotation. Biofuels have been blamed univesally fo ecent inceases in wold food pices. Many studies have shown that these enegy mandates in the US and EU may have a lage (30-60%) impact on food pices. In this pape we use a patial equilibium famewok to show that demand-side effects - in the fom of population gowth and income-diven pefeences fo meat and daiy poducts athe than ceeals - may play as much of a ole in aising food pices as biofuel policy. By specifying a Ricadian model with diffeential land quality, we find that a significant amount of new land will be conveted to faming which is likely to cause a modest incease in food pices. Howeve, biofuels may incease aggegate wold cabon emissions, due to leakage fom lowe oil pices and convesion of pastue and foest land fo faming. Keywods: Clean Enegy, Food Demand, Land Quality, Renewable Fuel Standads, Tanspotation JEL Codes: Q24, Q32, Q42 This Vesion : Novembe Chakavoty (Coesponding Autho): Depatment of Economics, Tufts Univesity (TSE, CESifo), ujjayant.chakavoty@tufts.edu; Hubet: CREM, Depatment of Economics, Univesity of Rennes 1, maiehelene.hubet@univ-ennes1.f; Moeaux: Toulouse School of Economics (IDEI, LERNA), mmichel@cict.f; and Nøstbakken: Depatment of Economics, Nowegian School of Economics, linda.nostbakken@nhh.no.

3 1. Intoduction Biofuels ae poviding an eve lage shae of tanspot fuels, even though they have been univesally attacked fo not being a geen altenative to gasoline. In the United States, about 10% of gasoline now comes fom con and this shae is expected to ise thee-fold in the nea futue. The Euopean Union, 2 India and China have aggessive biofuel mandates as well. Studies that have modeled the effect of these policies on food pices pedict lage inceases, and have been suppoted by the un-up in commodity pices in ecent yeas. Fo example, the Intenational Food Policy Reseach Institute (Rosegant et al.,2008) suggests that pices of cetain cops may ise by up to 70% by In this pape, we examine the long-un effects of US and EU biofuel policy in a dynamic, patial equilibium setting. 4 Ou appoach is unique in two espects. It is common knowledge that as poo counties develop, thei diets change in fundamental ways. In paticula, they eat less ceeal and moe animal potein in the fom of meat and daiy poducts. 5 This fact is impotant because poducing meat and daiy uses moe land than gowing con. 6 Coupled with global inceases in population, these demand shifts should cause an incease in food pices even without any biofuel policy. 2 The EU equies that biofuels must supply at least 10% of tanspotation fuels by 2020, fom a cuent shae of about 4%. 3 Othe studies have also found a significant impact, although not to the same degee. Fo example, Robets and Schlenke (2013) use weathe-induced yield shocks to estimate the supply and demand fo caloies and conclude that enegy mandates may tigge a ise in wold food pices by 20-30%. Hausman, Auffhamme and Beck (2012) use stuctual vecto auto-egession to examine the impact of biofuel poduction in the U.S. on con pices. They find that one thid of con pice inceases duing (which ose by 28%) can be attibuted to the US biofuel mandate. Thei shot-un estimates ae consistent with ou pediction that in the long-un, the impacts may be significantly lowe. This is because highe food pices ae likely to tigge supply side esponses only with a time lag, especially if significant land convesion wee to occu. 4 Both have imposed lage biofuel mandates. Othe nations such as China and India have also announced biofuel mandates but thei implementation is still in pogess. We discuss them late in the pape. 5 Fo instance, aggegate meat consumption in China has inceased by 33 times in the last 50 yeas, yet its population has only doubled (Robets and Schlenke 2013). 6 On aveage, eight kilos of ceeals poduce one kilo of beef and thee kilos of ceeals poduce one kilo of pok. 1

4 Second, many studies assume a fixed supply of land. Thee is plenty of land in the wold, although of vaying quality fo food poduction. Sustained food pice inceases will cause new land to be bought unde faming, but as we move down the Ricadian land quality gadient, costs will ise, which may in tun put an upwad pessue on pices. 7 The model we develop in this pape explicitly accounts fo the above effects in a dynamic setting whee we allow fo a ising supply cuve of cude oil. 8 Fig.1 shows the dispaity in meat and ceeal consumption in the United States and China. Chinese pe capita meat consumption is about half of the US, but ceeal consumption is much highe. These gaps ae expected to naow significantly in the nea futue as the Chinese diet gets an inceasing shae of its caloies fom animal potein. 9 Income-induced changes in dietay pefeences have been lagely ignoed in pevious economic studies. Ou esults show that about half the pedicted ise in food pices may be due to changes in diet. Since ou main pemise is that the pessue on food pices will lead to moe land convesion, the model we popose explicitly accounts fo the distibution of land by quality. We use USDA data which classifies land by soil quality, location, poduction cost and cuent use as in pastue o foest. With inceased use of biofuels, oil pices will fall, which will lead to leakage in the fom 7 In the last fou yeas, significant amounts of new land have been conveted fo faming (Tyne, 2012). 8 Hetel, Tyne and Biu (2010) use a geneal equilibium tade model (GTAP) to exploe the impact of biofuels poduction on wold agicultual makets, specifically focusing on US/EU mandatoy blending and its effects on individual counties. They use disaggegated data on wold land quality. Howeve, thei static famewok does not account fo changes in food pefeences. Reilly and Paltsev (2009) also develop a static enegy model that does not account fo heteogeneity in land quality. 9 Although we use China as an example, the tend holds fo othe counties as well. Fo example, pe capita meat and daiy consumption in developed nations is about fou times highe than in developing counties. 2

5 (a) Ceeal consumption (b) Meat consumption Figue 1: Pe capita ceeal and meat consumption in China and US, Souce: FAOSTAT. Note: Chinese ceeal consumption excludes gain conveted to meat. of highe oil use by counties with no biofuel policy. We endogenously detemine the wold pice of cude oil and the extent of this spatial leakage. 10 We show that biofuel policy may educe diect cabon emissions (fom combustion of fossil fuels) in the mandating counties but it is lagely offset by an incease in emissions elsewhee. Howeve, indiect emissions (fom land use) go up because of the convesion of pastue and foest land, mainly in the developing counties. Aggegate global geenhouse gas emissions fom the US and EU biofuel mandates actually show a small incease. The main message of the pape is that demand shifts may have as much of a ole in the ise of food pices as biofuel policy. 11 Moeove, this pice incease may be significantly lowe because of supply side adjustments in the fom of an incease in the extensive magin. These esults ae obtained with assumptions of modest gowth ates in the poductivity of land and in the enegy secto. Geneal equilibium effects of these policies, which we do not conside, may futhe 10 Othe studies do not detemine cude oil use in a dynamic setting. 11 Additional biofuel mandates imposed by China and India also have a supisingly small effect on food pices. 3

6 diminish the pice impact of biofuel mandates. By the same token, models that do not account fo supply side effects of ising food pices will tend to find lage impacts. Section 2 descibes the undelying theoetical model. Section 3 epots the data used in the calibation. In section 4 we discuss esults. In section 5 we pefom sensitivity analysis. Section 6 concludes the pape. The Appendix povides data on the paametes used in the model. 2. A Ricadian Model of Land Quality In this section, we pesent the detailed theoetical stuctue of the calibation model used to estimate food pices. The Theoetical Famewok Conside a dynamic, patial equilibium economy in which thee goods, namely ceeals, meat and enegy ae poduced and consumed in five egions, espectively denoted by. The egional consumption of these goods is denoted by q, q c m and q e whee c, m and e denote ceeals, meat and enegy, espectively. Consumes fom each egion obtain utility fom consuming these goods, given by U ( q ), U ( q ) and U q ) espectively. All utility functions ae assumed to be stictly c c m m ( e e inceasing and concave, and satisfy Inada conditions. Land fo faming comes in thee qualities denoted by n = { High, Medium, Low} with High being the highest quality. The aceage of land quality n in egion devoted to ceeals, meat o biofuel poduction at any time t is given by Lnc( t), Lnm( t) and by L nb(t) espectively. We denote the diffeent land uses by j = c, m,b { }. Let j L ( t) be the total aceage fo land quality n at any nj 4

7 time t and L n be the initial land aea by quality available fo cultivation. Aggegate land unde the thee cops cannot exceed the endowment of land, hence L nj (t) = L n (t) L n, fo all j. Let j new land bought unde cultivation at any time t be denoted by l n (t), i.e., L! ( t) l ( t), whee n = dot denotes the time deivative. The vaiable l n (t) may be negative if land is taken out of poduction: hee we only allow fo new land to be bought unde cultivation. 12 The egional total cost of binging new land into cultivation is inceasing and convex as a function of aggegate land cultivated in the egion, but linea in the amount of new land used at any given instant this cost n is given by 2 c c ( ln whee we assume that > 0, > 0. Additional land bought unde 2 L n L c Ln ) n poduction is likely to be located in emote locations. Thus the geate is the land aea aleady unde cultivation, the highe the unit cost of binging new land into faming within a given quality. Let the yield fo land quality n allocated to use j be given by k. 13 Yields ae highe on highe quality land. 14 Then the output of food o biofuel enegy at any time t is given by n 5 nj k. njlnj Regional poduction costs ae a function of output and assumed to be ising and convex, i.e., moe aea unde ceeals, meat o biofuel poduction implies a highe cost of poduction, given by w j ( n k nj L nj ). 12 Allowing land to be taken out of poduction will make the optimization pogam complicated. When we un ou calibation model, this vaiable is neve zeo befoe the yea 2100 except in the US (whee land convesion is small in any case, as we see late in the pape) and is neve zeo in any egion afte the yea 2100 because population keeps inceasing and diets tend towad moe meat and daiy consumption which is land intensive. Howeve, if food pices fall because of exogenous technological change, some land may go out of poduction in the distant futue, but that is beyond the scope of ou analysis. 13 In the calibation model, cops ae tansfomed into end-use commodities (ceeals, meat and biofuels) by means of a coefficient of tansfomation (cops into commodities) and a cost of tansfomation, both linea. Thei values ae epoted in the Appendix. 14 See Appendix Tables A5 and A6.

8 Oil is a nonenewable esouce and we assume a single integated bathtub wold oil maket as in Nodhaus (2009). Let X be the initial wold stock of oil that is used only fo tanspotation, X (t) be the cumulative stock of oil extacted until date t and x (t) the egional ate of consumption so that X! ( t) = x ( t). The unit extaction cost of oil is inceasing and convex with the cumulative amount of oil extacted, denoted by g (X ). Thus total cost of extaction is g X ) x ( t) (. Cude oil is tansfomed into gasoline by applying a coefficient of tansfomation ω so that total poduction of gasoline is q g = ωx. 15 Tanspot fuel is poduced fom combining gasoline (deived fom cude oil) and biofuels in a convex linea combination using a CES σ 1 specification, given by q e = π µ σ g q g + (1 µ g )q b σ 1 σ σ σ 1 whee q e is the poduction of tanspot fuel, π is a constant, q, q the quantities consumed of gasoline and biofuel, g b µ is the g shae of oil and (1 ) is the shae of biofuels in tanspot fuel, σ is the egional elasticity of substitution. µ g Food commodities and biofuels ae assumed to be taded without fiction acoss egions. Then we can wite the net expot demand (egional poduction net of consumption) fo ceeals, meat and biofuels as knclnc qc, knmlnm qm, and, n n k nblnb qb espectively. Tanspot fuel is n not taded but blended and consumed domestically. 15 We also include the cost of efining cude oil into gasoline, descibed in the Appendix. 6

9 The social planne uses a discount ate ρ > 0 and chooses the egional aceage allocated to food and biofuel poduction, the amount of new land bought unde cultivation, the quantity of each food and enegy used and the quantity of gasoline used at each time t in each egion, witten as ρt Max e Uc( qc) + Um( qm) + Ue( qe) c ( Ln) ln w j( knjlnj) g( X ) x dt (1) n j n Lnj, qj, ln, x 0 subject to: L nj = L n L n, n (2) j!l n (t) = l n (t), n (3)!X(t) = x (t) (4) σ 1 q e = π µ σ g q g + (1 µ g )q b k nj L nj q j = 0 n σ 1 σ σ σ 1 (5) (6) whee q g = ωx. The coesponding genealized Lagangian can be witten as: L = U c (q c ) +U m (q m ) +U e (q e ) c (L n )l n w j ( k nj L nj ) g(x) x n j n + β n (L n L nj ) +θ n l n λ n j + ν j k nj L nj q j n j x whee β n is the multiplie associated with the static land constaint (2), θn and λ ae multiplies associated with the two dynamic equations (3) and (4), and ν j epesents the wold pice of taded goods (ceeals, meat and biofuels). We get the following fist ode conditions: 7

10 k nj (ν j w j ') β n 0(= 0if L nj > 0), j = c,m, b { } (7) U j ' ν j 0(= 0if q j > 0), j = { c,m} (8) U e ' q e q ν b 0(= 0if q b > 0) (9) b θ n c (L n ) 0(= 0if l n > 0) (10) U e ' q e q g(x) λ 0(= 0if q g > 0) (11) g and finally the dynamics of the co-state vaiables is given as!λ(t) = ρλ + g '( X ) x!θ n (t) = ρθ n + c ' (L n )l n β n. (13) (12) This is a standad optimization poblem with a concave objective function note that the utility functions ae concave and costs ae linea o convex. The constaints ae linea. By imposing appopiate bounday conditions such as Inada conditions on the utility functions, we can obtain a unique, inteio solution. 16 Conditions (7) suggest that the cultivated land in each egion is allocated eithe to ceeals, meat and enegy poduction until the pice(ν j ) equals the sum of the poduction cost plus the shadow value of the land constaint, given by β n. Equation (8) suggests that the maginal utility of ceeals and meat ( U ) equals its wold pice (ν j ). Equation (9) suggests that the maginal utility ' j of biofuels ( U ), weighted by the tem ' e q q e b equals its wold pice ( ν b ). Equation (10) indicates that the maginal cost of land convesion equals the dynamic shadow value of the stock 16 Fo an analytical solution to a much simple but simila poblem, see Chakavoty, Magne and Moeaux (2008). 8

11 of land, θ. Equation (11) states that the maginal utility of gasoline ( U ), weighted by n ' e q e q g equals its cost augmented by the scacity ent λ. Conditions (12) and (13) give the dynamic path of the two co-state vaiables λ and θ. n Because of the Inada conditions, tanspot fuel poduction is stictly positive in all egions, so we have > Impefect substitutability between gasoline and biofuels implies that q g > 0 and q e q > 0. Accoding to equations (9) and (11), consumption of biofuel and gasoline ae b espectively given by U e q β qe ' = wb ' + and U e ' = g( X ) + λ. Hence, the weighted maginal q k q e b n nb costs of biofuels and gasoline ae equal. Bounday conditions also ensue that ceeal and meat consumption ae stictly positive, so, q > 0 and q > 0. This implies that a positive quantity of c land must be allocated to the poduction of ceeals, meat and enegy. Note that equilibium ents should be highe on highe quality land. An incease in the demand fo enegy will induce a shift of aceage fom food to enegy and hence dive up the pice of food, as well as bing moe land into cultivation, potentially of a lowe quality. m g The biofuel mandate is imposed by equiing a minimum level of consumption of biofuels in tanspotation at each date until the yea Define the egional mandate in time T as q (T ) b which implies that biofuel use must not be lowe than this level at date T. This constaint can be witten as ( q b (T ) q b (T )) 0. This will lead to an additional tem τ ( q b (T ) q b (T )) in the genealized Lagangian. The new condition fo allocating land to biofuel (modified equations 7 17 Tanspot fuel is poduced and consumed domestically since it is not taded. 9

12 and 9) will be k nb U e ' q e q w ' b + τ b β n 0,(= 0if L nb > 0) fo all n. The shadow pice τ can be intepeted as the implicit subsidy to biofuels that bidges the gap between the maginal cost of gasoline and the maginal cost of biofuel. The Euopean mandate is a popotional measue, which pescibes a minimum pecent of biofuel in the tanspot fuel mix. This estiction is q ( T) implemented in the model by witing b s( T) q ( T) whee s(t ) is the mandated minimum shae of biofuels in tanspot at time T. e 3. Calibation of the Model In this section we discuss the empiical model that is deived fom the famewok pesented above. We aggegate the counties into thee goups using data on goss national poduct pe capita (Wold Bank 2010). These ae High, Medium and Low Income Counties (HICs, MICs and LICs). Since ou study focuses specifically on US and EU biofuel mandates, the HICs ae futhe divided into thee goups - the US, EU and Othe HICs. Thee ae five egions in all. Table 1 shows aveage pe capita income by egion. The MICs consist of fast gowing economies such as China and India that ae likely to account fo a significant shae of futue wold enegy demand as well as lage biofuel poduces like Bazil, Indonesia and Malaysia. The LICs ae mainly nations fom Afica. Specification of Demand. We conside thee final consumption goods - namely ceeals, meat and daiy poducts and enegy fo tanspotation. Ceeals include all gains, stach cops, suga and sweetenes and oil cops. Meat and daiy include all meat poducts and daiy such as milk and butte. Fo convenience, we call this goup meat. We sepaate ceeals fom meat because thei 10

13 consumption is income-sensitive and meat poduction is moe land intensive. Tanspot enegy is supplied by gasoline and biofuels. Ceeals, meat and biofuels compete fo land that is aleady unde faming as well as new land, which is cuently unde gassland o foest cove. 18 Table 1. Classification of egions by income (US$) Regions GDP pe capita Majo counties US 46,405 - EU 30,741 - Othe HICs 36,240 Canada, Japan MICs 5,708 China, India, Bazil, Indonesia, Malaysia LICs 1,061 Mostly Afican counties Notes: Pe capita GDP in 2007 dollas, PPP adjusted. Souce: Wold Bank (2010) Utility fom consumption is just the aea unde the egional demand cuve fo each of these goods, which is simply the demand pe capita times population. Regional demands (fo the thee consumption goods - ceeals, meat and tanspotation fuel) ae modeled by means of Cobb- Douglas demand functions, which ae exogenously diven by egional pe capita income and population. 19 Regional demand D i fo good i takes the fom D i α i βi = Ai Pi y N (14) whee Pi is the output pice of good i in dollas, αi is the egional own-pice elasticity, β i is the egional income elasticity fo good i which changes exogenously with pe capita income eflecting changes in food pefeences, y is egional pe capita income, N is egional population and A i is the constant demand paamete fo good i which is calibated to epoduce the baseyea demand fo final commodities fo each egion. Demand fo food is in billion tons and demand fo fuel is in billion miles diven. 18 Obviously many othe commodities can be included fo a moe disaggegated analysis, but we want to keep the model tactable so that the effects of biofuel policy on land use ae tanspaent. 19 Demands fo ceeals and meat ae assumed to be independent as in othe studies (see Rosegant et al. (2001) and Hetel, Tyne and Biu (2010)). 11

14 As incomes ise, we expect to obseve inceased pe capita consumption of meat elative to the consumption of ceeals, as noted in numeous studies (e.g., Keyze et al. 2007). We model this shift towads animal potein by using income elasticities fo food that ae highe at lowe levels of pe capita income. Specifically, income elasticities fo the US, EU and othe HICs ae taken to be stationay in the model since dietay pefeences as well as income in these egions ae not expected to change significantly in the long un, at least elative to the developing counties. Howeve, they ae likely to vay in the MICs and LICs due to the lage incease in pe capita incomes. The highe the income, the lowe is the income elasticity. All pice and income elasticities ae specific to each food commodity (e.g., meat, ceeals) and taken fom GTAP (Hetel et al., 2008) as descibed in the Appendix (Tables A1-A3). 20 We account fo egional dispaities in the gowth of population. While the population of high income nations (including the US and EU) is expected to be faily stable ove the next centuy, that of middle income counties is expected to ise by about 40% by 2050 and moe than double fo lowe income counties (United Nations Population Division, 2010). Demand is also impacted by pe capita income in each egion, which is assumed to incease steadily ove time but at a deceasing ate, as in seveal studies (e.g., Nodhaus and Boye 2000). Again, egional diffeences ae ecognized, with the highest gowth ates in MICs and LICs It is impotant to note that not all developing counties have exhibited as lage a gowth in meat consumption as China. Fo example, a thid of Indians ae vegetaian and a change in thei incomes may not lead to dietay effects of the same magnitude. Moeove beef and pok ae moe land-intensive than chicken, the latte being moe popula in counties like India. The distibution of income may also affect this behavio. If it is egessive, the effect on diets may be limited. 21 Initial population levels and pojections fo futue gowth ae taken fom the United Nations Population Division (2010). Both wold food and enegy demands ae expected to gow significantly until about 2050, especially in the MICs and LICs. By 2050, the cuent population of 6.8 billion people is pedicted to each nine billion. Beyond that time, population gowth is expected to slow, with a net incease of one billion people between 2050 and

15 Figue 2. Distibution of land quality Souce: U.S. Depatment of Agicultue, (Eswaan et al p.121). Notes: Land quality is defined along two dimensions: soil pefomance and soil esilience. Soil pefomance efes to the suitability of soil fo agicultual poduction; soil esilience is the ability of land to ecove fom a state of degadation. Land quality 1 is the highest quality and 9 the lowest. In ou model, we ignoe categoy 7 though 9 which ae unsuitable fo agicultual poduction and aggegate the est into thee qualities (categoies 1 and 2 become High quality land, 2 and 3 Medium quality land and 5 and 6, Low quality land). 22 Land Endowment and Poductivity The initial global endowment of agicultual land is 1.5 billion hectaes (FAOSTAT). The egional distibution of land quality is not even, as is evident fom Figue 2 which shows land endowments based on climate and soil chaacteistics. Most good land is located in highe income counties, but Bazil and India also have sizeable endowments of high quality land. Initial endowment fo each of the thee land qualities can be divided into land aleady unde cultivation and fallow land. 23 As shown in Table 2, moe than half of the agicultual land in the HICs (US, EU and Othes) is classified as high land quality, while the coesponding shaes ae oughly a thid fo MICs and LICs, espectively. Most land of medium and low qualities is cuently fallow, in the fom of gasslands and foests, and located 22 Many factos such as iigation and climate change can affect land quality. Fo instance, investment in iigation can impove the poductivity of land. In nothen egions like Canada and Russia highe tempeatues may cause an expansion of land suitable fo agicultual poduction; hence, aea unde medium and low qualities may incease in the futue. The net effect of these factos on the poductivity of new land is unclea and left fo futue wok. Howeve, we do allow fo inceasing poductivity of land ove time (see below). 23 See Appendix fo details on land classification. Accoding to FAO (2008a), an additional 1.5 billion hectaes of fallow lands could be bought unde cop poduction in the futue. This is appoximately equal to the total land aea aleady unde cultivation. 13

16 mostly in MICs and LICs. Note fom Table 2 that thee is no high quality land available fo new poduction. Futue expansion must occu only on lowe quality lands. Bazil alone has 25% of all available lands in the MICs and is the biggest poduce of biofuels afte the US. Land aleady unde Agicultue (million ha) Land available fo faming (incl. fallow lands) (million ha) Table 2. Land cuently in faming and endowment of fallow land Land quality US EU Othe HICs MICs LICs Wold High Medium Low High Medium Low Souces: Eswaan et al. (2003), FAO (2008a), Fische and Shah (2010). As in Gouel and Hetel (2006), the unit cost of accessing new land in a egion inceases with land convesion. This can be witten as c L n Ln φ 1 φ log (15) Ln ( Ln ) = 2 whee Ln is the initial endowment of quality n, so that L n L n is the fallow land available at date t, φ1 and φ2 ae model paametes, positive in value (calibated fom data) assumed to be the same acoss land quality but vaying by egion. 24 Impovements in agicultual poductivity ae exogenous and allowed to vay by egion and land quality (see Appendix Table A5). All egions ae assumed to exhibit inceasing poductivity ove time, mainly because of the adoption of biotechnology (e.g., high-yielding cop vaieties), access to iigation and pest management. Howeve, the ate of technical pogess is highe in MICs and φ 1 24 Intuitively, is the fixed cost of land convesion. Convesion costs incease without bound as the stock of fallow land declines, since the log of the backeted tem is negative. 14

17 LICs because thei cuent yields (conditional on land quality) ae low due to a lag in adopting moden faming pactices (FAO 2008a). The ate of technical pogess is also likely to be lowe fo the lowest land quality. Biophysical limitations such as topogaphy and climate educe the efficiency of high-yielding technologies and tend to slow thei adoption in low quality lands, as pointed out by Fische et al. (2002). The poduction cost fo poduct j (e.g. ceeal, meat o biofuel) fo a given egion is w j η 2 = η1 knjlnj (16) n whee the tem inside backets is the aggegate poduction ove all land qualities (denoted by n ) in the egion η1 and η2 ae egional cost paametes. 25 Fo food and biofuels, we distinguish between poduction and pocessing costs. All cops need to be packaged and pocessed, and when they ae conveted to biofuels, the efining costs ae significant. Fo ceeals and meat, we use the GTAP 5 database which povides sectoal pocessing costs by county (see Appendix, Table A8). Pocessing costs fo biofuels ae discussed below. The Enegy Secto As in the theoetical famewok, tanspotation enegy q! is poduced fom gasoline and biofuels in a convex linea combination using a CES specification. Fo biofuels we model both land using (Fist Geneation biofuels) and newe technologies that ae less land-using (Second Geneation), the latte ae descibed in moe detail below. They ae teated as pefect substitutes, but with diffeent unit costs as in many othe studies (Chen et al. 2012). We use estimates of the elasticity of substitution made by Hetel, Tyne and Byu (2010). The constant 25 The calibation pocedue fo this equation is explained in the Appendix. 15

18 paamete in the CES poduction function is calibated to epoduce the base-yea poduction of blending fuel (see Table A10 in the Appendix fo paametes value). Fo cude oil eseves, both conventional and unconventional oils (e.g., shale) ae included. Accoding to IEA (2011), aound 60% of cude oil is used by the tanspotation secto. Fom the estimated oil eseves in 2010, we compute the initial stock of oil available fo tanspotation as 153 tillion gallons (3.6 tillion baels, Wold Enegy Council 2010). The unit cost of oil depends on the cumulative quantity of oil extacted (as in Nodhaus and Boye 2000) and can be witten as Xt () g( Xt ( )) = ϕ1+ ϕ 2 X ϕ 3 (17) whee X ( t) = x ( t) is the cumulative oil extacted at time t and X is the initial stock of t ϕ1 1 2 cude oil; is the initial extaction cost and ( ϕ + ϕ ) is the unit cost of extaction of the last unit ϕ1 ϕ2 3 of oil. The paametes, and ϕ ae obtained fom Chakavoty et al. (2012). The initial extaction cost of oil is aound $20 pe bael (o $0.50 pe gallon) and can each aound $260 pe bael (o $6.50 pe gallon) if the stock appoaches exhaustion (see Table A9 in the Appendix). The specification of this extaction cost allows us to take into account the fact that as the oil pice inceases, unconventional oils become competitive. Fo each egion, we conside a epesentative fuel: gasoline fo the US and diesel fo the EU. 26 We simplify by consideing a epesentative fist geneation biofuel fo each egion. This assumption is easonable because thee is only one type of biofuel that dominates in each egion. 26 In the US, gasoline epesents moe than thee-quates of tanspot fuel use while diesel accounts fo about 60% in the EU (Wold Resouces Institute 2010). The coefficients of tansfomation of oil into gasoline and into diesel ae epoted in the Appendix. 16

19 Fo example, 94% of poduction in the US is ethanol fom con, while 76% of EU poduction is biodiesel fom apeseed. Bazil, the lagest ethanol poduce among MICs, uses sugacane. Hence, sugacane is used as the epesentative cop fo MICs. In the LICs, 90% of biofuels ae poduced fom cassava, although it amounts to less than 1% of global poduction. Table 3 shows the epesentative cop fo each egion and its pocessing cost in the base yea. 27 Note the significant diffeence in costs acoss cops. These costs ae assumed to decline by aound 1% a yea (Hamelinck and Faaij 2006) mainly due to a decease in pocessing costs. 28 We model a US tax cedit of 46 cents/gallon, which consists of both state and fedeal cedits (de Gote and Just 2010) which is emoved in the model in yea 2010, as done in othe studies (Chen et al. 2012). EU states have tax cedits on biodiesel anging fom cents (Kojima et al. 2007). We include an aveage tax cedit of 60 cents fo the EU as a whole. Table 3. Unit pocessing costs of fist geneation biofuels US EU Othe HICs MICs LICs Feedstock Con Rapeseed Con Suga-cane Cassava (94%) (76%) (96%) (84%) (99%) Cost ($/gallon) Souces: FAO (2008a); Eisentaut (2010); Notes: The numbes in paentheses epesent the pecentage of fistgeneation biofuels poduced fom the epesentative cop in the base yea, 2007 (e.g., con). Second gen biofuels can be divided into thee categoies depending on the fuel souce: cops, agicultual and non-agicultual esidue. They cuently account fo only about 0.1% of total biofuel poduction although the maket shae may incease with a eduction in costs and 27 The total cost of biofuels is the sum of the poduction and pocessing costs plus ent to land net the value of bypoducts. Note that poduction costs depend on what type of land is being used and in which geogaphical egion, and land ent is endogenous. By-poducts may have significant value since only pat of the plant (the fuit o the gain) is used to poduce fist-geneation biofuels. Fo instance, cushed bean cake (animal feed) and glyceine ae bypoducts of biodiesel that can be sold sepaately. The costs shown in table epesent about 50% of the total cost of poduction. 28 Except fo cassava, fo which we have no data. 17

20 impoved fuel pefomance and eliability of the convesion pocess. Compaed to fist gen fuels, they emit less geenhouse gases and ae less land consuming. Among seveal second gen biofuels, we model the one that has the highest potential to be commecially viable in the nea futue, namely cellulosic ethanol (fom miscanthus, which is a type of peennial gass that poduces biofuel) in the US and biomass-to-liquid (BTL) fuel in EU (IEA 2009b). Thei enegy yields ae much highe than fo fist gen biofuels. In the US, 800 gallons of ethanol (fist gen) ae obtained by cultivating one hectae of con, while 2,000 gallons of ethanol (second gen) can be poduced fom ligno-cellulosic (Khanna 2008). In EU, aound 1,000 gallons/ha can be obtained fom BTL, but only 400 gallons/ha ae obtained fom fist gen biofuels. 29 Second gen fuels ae moe costly to poduce. The pocessing cost of cellulosic ethanol is $3.00 pe gallon while fist gen con ethanol cuently costs about $1.01 pe gallon and ethanol fom suga cane costs $ The pocessing cost of BTL diesel is $3.35 pe gallon - twice that of fist gen biodiesel. Howeve, technological pogess is expected to gadually naow these cost diffeentials and by about 2030, the pe gallon pocessing costs of second gen biofuels and BTL diesel ae pojected to be $1.09 and $1.40, espectively. 31 Finally, second gen fuels enjoy a subsidy of $1.01 pe gallon in the US (Tyne 2012), which is also accounted fo in the model. US and EU mandates The US mandate sets the domestic taget fo biofuels at nine billion gallons annually by 2008, inceasing to 36 billion gallons by The bill specifies the use of 29 By second geneation biofuels, we mean cellulosic ethanol in the US and BTL in the EU. 30 Fo second geneation biofuels, pocessing is moe costly than fo fist-geneation biofuels and poduction costs plus land ent account fo about 65% of the total cost. 31 All data on poduction costs ae fom IEA (2009b). Second geneation biofuels costs ae assumed to decease by 2% pe yea (IEA 2009b). 32 It is not clea whethe the mandates will be imposed beyond 2022 but in ou model, we assume that they will be extended until In fact ethanol use in the US has aleady hit the 10% blending wall imposed by Clean Ai 18

21 fist and second gen biofuels (espectively, con ethanol and advanced biofuels) as shown in Figue 3. The fome is scheduled to incease steadily fom the cuent annual level of 11 to 15 billion gallons by The bill equies an incease in the consumption of advanced biofuels (o second geneation biofuels) fom nea zeo to 21 billion gallons pe yea in In the EU the mandate equies a minimum biofuels shae of 10% in tanspot fuel by Unlike the US, the EU has no egulation on the use of second gen fuels. 33 Cabon emissions The model accounts fo diect cabon emissions fom fossil fuel consumption in tanspotation and indiect cabon emissions induced by the convesion of new Figue 3. US biofuel mandate land into agicultue. Cabon fom biofuel use is mainly emitted duing poduction and hence is cop-specific. Consideing only diect emissions, displacing gasoline by con ethanol educes egulations which must be elaxed fo futhe inceases in biofuel consumption. We abstact fom distinguishing between the thee categoies of advanced biofuels in the US mandate. Of the 21 billion of second gen biofuels mandated, 4 billion gallons ae low emission biofuels that can be met by biofuels othe than cellulosic, such as sugacane ethanol impoted fom Bazil. Anothe billion gallons may be met by biodiesel, which is used mainly fo tucks. In this study, we assume that the entie taget fo advanced biofuels has to be met by cellulosic ethanol. 33 US and EU mandates intoduce othe mino citeia that we do not model. Fo instance, the EU mandate specifies that biofuel should not be poduced on lands with significant biodivesity. 19

22 emissions by 35%; 70% if displaced by ethanol fom sugacane. Second-geneation biofuels educe cabon by 80% compaed to gasoline (Chen et al. 2012). Convesion of land fo faming also eleases cabon into the atmosphee. 34 Using Seachinge et al. (2008), we assume that the cabon eleased is 300 and 500 tons of CO 2 e (CO 2 equivalent) pe hectae espectively fo medium and low quality land, immediately afte land convesion. This is because medium quality land has moe pastue and less foests than low quality land, and the fome emits less cabon. 35 Tade among egions Goods ae teated as pefectly homogenous. We assume fictionless tading in cude oil and food commodities between counties. In eality, thee ae significant tade baies in agicultue, but given the level of aggegation in ou model, it is difficult to model agicultual taiffs, which ae mostly commodity-specific (suga, wheat, etc.). Howeve, we do model US and EU taiffs on biofuels. The US ethanol policy includes a pe unit taiff of $0.54 pe gallon and a 2.5% ad valoem taiff (Yacobucci and Schnepf, 2007). The EU specifies a 6.5% ad valoem taiff on biofuel impots (Kojima et al. 2007). Afte 2012, US tade taiffs ae emoved fom the model to match with cuent policy (The Economist, 2012). The discount ate is assumed to be 2% as is standad in such analyses (Nodhaus and Boye 2000). The model is simulated ove 200 yeas ( ) in steps of five, to keep the uns tactable. It is calibated fo the base yea This is a gadual pocess. Fo foests it may also depend on the final use of foest poducts. Howeve, we assume that all cabon is eleased immediately following land-use change, an assumption also made in othe well-known studies (e.g., Seachinge, et al. 2008). 35 Thee have been ecent studies (see Hetel et al., 2010) which suggest that the emissions fom indiect land use change ae likely to be somewhat smalle than those assumed by Seachinge. Howeve, given that significant land use change occus both in ou base model and the one unde egulation, these new estimates ae unlikely to affect the cental conclusions of ou pape. Emission levels may change, not the net effect of biofuel egulation. 20

23 Model validation It is not possible to test model pedictions ove a long time hoizon because biofuel mandates have been imposed only ecently. Howeve, as shown in Fig.4, the model does tack the US gasoline consumption quite closely fom 2000 to The aveage diffeence between obseved and pojected values is systematically aound 3%. The model pedicts the annual aveage incease in food pices fom 2000 to 2012 at 9%. 37 Accoding to the FAO, food pices gew at an annual ate of 7.5% duing this peiod. The model solution suggests that aound 19 million hectaes of new land ae conveted fo faming fom 2000 to Accoding to FAOSTAT, 21 million hectaes of land wee bought into cultivation duing this peiod. These indicatos suggest that the model pefoms easonably well in pedicting the impact of the mandates on diffeent vaiables of inteest. Figue 4: Model pediction vs actual US oil consumption fom 2000 to 2012 Souce: Consumption figues ae fom EIA (2013). Notes: The diffeence between obseved values and pedicted values is highe afte 2008 since US gasoline consumption fell significantly duing the ecession Ou long-un model does not captue shot-un changes. 36 Note that we only impose biofuel mandates in ou model so the gasoline consumption is detemined endogenously. 37 Ou wold food pice is the aveage of ceeal and meat pices weighted by the shae of each commodity in total food consumption. In geneal, it is had to accuately pedict food pices in the shot un, because of weathe-elated vaiability (doughts such as the one that occued in Austalia in 2008 o Russia in 2010), cuency fluctuations and othe macoeconomic phenomena. 21

24 4. Simulation Results We fist state the scenaios modeled in the pape and then descibe the esults. In the Baseline case (model BASE), we assume that thee ae no enegy mandates and both fist and second gen fuels ae available. This case seves as the countefactual. The idea is to see how substitution into biofuels takes place in the absence of any clean enegy egulation. In the Regulatoy Scenaio (model REG), US/EU mandatoy blending policies, as descibed ealie, ae imposed. The key esults ae as follows: Effect of biofuel mandates on food pices. We find that the effect of the mandates on food pices is significant, but not huge (see REG in Table 4). With no enegy mandates, food pices ise by about 15%, which is puely fom changes in population and consumption pattens (see BASE). 39 With enegy mandates, they go up by 32% (see REG). Thus the additional incease in 2022 fom enegy egulation is about 17%. 40 This is much smalle than what most othe studies pedict (Rosegant et al. 2008, Robets and Schlenke 2012) Ou esults ae time sensitive but to steamline the discussion, we mostly focus on the yea In the moe distant futue (say aound 2050 and beyond), ising enegy pices and a slowdown in demand gowth makes biofuels economical, even without any suppoting mandates. Mandates become somewhat edundant by then. Given the lack of space, we do not discuss what happens in 2050 and beyond. 39 The model is calibated to tack eal food pices in Ceeal and meat pices fo that yea fo the BASE case ae $218 and $1,964 pe ton. Obseved pices in 2007 wee $250 and $2,262, espectively (Wold Bank 2010). The small diffeence can be explained by ou calibation method which is based on quantities not pices. 40 Since the model is dynamic, the initial conditions ae endogenous, hence the stating pices in 2007 ae not exactly equal (Table 4). 41 In geneal, it is difficult to compae outcomes fom diffeent models, but Rosegant et al. (2008) pedict pices of specific cops such as oilseeds, maize and suga ising by 20-70% in 2020 which ae, in geneal, significantly highe than in ou case. Robets and Schlenke (2013) poject that 5% of wold caloic poduction would be used fo ethanol poduction due to the U.S. mandate. As a esult, wold food pices in thei model ise by 30%. These studies assume enegy equivalence between gasoline and biofuels, i.e., one gallon of gasoline is equivalent to one gallon of biofuel. We account fo the fact that one gallon of ethanol yields about a thid less enegy than gasoline, as in Chen et al. (2012). 22

25 Figue 5 shows the time tend in food pices unde the two egimes. Note that pices incease both with and without egulation. 42 The substantial incease in food demand in MICs and LICs accompanied by a change in dietay pefeences aises the demand fo land, which dives up its oppotunity cost. Without enegy egulation, meat consumption in these two egions inceases by 8% (fo MICs) and 34% (fo LICs) between 2007 and 2022, with the latte stating fom a smalle base. The consumption of ceeals emains stable. Since moe land is used pe kilogam of meat poduced, the oveall effect is inceased pessue on land. Food pices decline ove time as the effects of the mandates wea off. 43 This is mainly because population gowth levels off by that time hoizon and yields incease due to technological impovements in agicultue. Table 4. Wold food, biofuel and gasoline pices (in 2007 Dollas) Weighted food pice ($/ton) Biofuel pice ($/gallon) Cude oil pice ($/bael) BASE REG (15%) 746(32%) Notes: Weighted food pice is the aveage of ceeal and meat pices weighted by the shae of each commodity in total food consumption. The numbes in backets epesent the pecentage change in pices between Ou pedictions fo cude oil pices ae quite close to US Depatment of Enegy (EIA 2010, p.28) Refeence pojections of $115/bael in 2022, which ae between thei High and Low Oil Pice scenaios. 2. Demand gowth causes most of the land convesion, nealy all of it in developing counties. Table 5 shows that the eally big inceases in land use occu even without mandates: in the MICs, 119 million ha (= ) ae bought unde poduction between without any mandates 42 Although eal food pices have declined in the past fou decades, the potential fo both aceage expansion and intensification of agicultue though impoved technologies is expected to be lowe than in the past (Ruttan 2002). Fom 1960 to 2000, cop yields have moe than doubled (FAO 2003). But ove the next five decades, yields ae expected to incease by only about 50%, see the data pesented in the Web Appendix (Table A5). Howeve, yields may also espond to highe food pices, an effect we do not captue hee. That will imply an even smalle impact of enegy mandates on food pices. 43 The incease in pice due to egulation is about 6% in the yea

26 Figue 5. Wold weighted food pices Notes: The baseline model is in blue and the egulated model in ed. The weighted food pice is the aveage of ceeal and meat pices weighted by the shae of each commodity in total food consumption. (see BASE). This is about two thids of all cultivated land cuently in poduction in the US. No new land (including land available unde the Consevation Reseve Pogam) is bought unde cultivation in the US due to highe convesion costs than in MICs. With the mandates, MICs bing anothe 74 (= ) million hectaes unde faming. Food poduction in the US/EU declines but ises in the MICs. Biofuel mandates incease aggegate land aea in agicultue, because of convesion of new land. Table 5. Land allocation to food and enegy poduction (in million ha) US EU MICs BASE REG BASE REG BASE REG Land unde food poduction Land unde biofuel poduction Total cultivated land Notes: Land allocation in Othe HICs and LICs ae simila acoss the diffeent scenaios. Fig.6 shows land use fo food and fuel. Note that in the US about 60 million ha a thid of all famland is moved fom food to fuel poduction. 44 But no new land is added (Fig.6a). Howeve, 44 It is impotant to note that thee ae othe souces of second geneation biofuels that ae less land-consuming, such as con stowe and foest poducts which can affect these land convesion estimates significantly. They may affect the gowth of food pices to a lesse extent than pedicted in the pape. 24

27 the MICs convet a significant amount of land, iespective of the enegy mandates (Fig.6b). 45 Both fist and second gen biofuel poduction inceases shaply unde the US mandate. US food poduction declines by almost 27% as a esult of the enegy mandates (not shown). US food expots go down by moe than 80% (fom 75 to 13 million tons). This is because land is shifted out of food to poduce biofuels fo domestic consumption. Impots of fist gen biofuels moe than double. 3. Mandates lead to big inceases in biofuel poduction, ealie in time. Without egulation, biofuel consumption in the EU and US in 2022 is aound 2 and 8 billion gallons, and accounts fo 3% and 5.5% of fuel consumption, espectively. This is much lowe than what is pescibed by the mandates. Fig.7 shows consumption with and without the mandates (BASE, REG). The mandatoy blending policy equies an additional 30 billion gallons of biofuels in 2022 compaed to the unegulated case, mostly in the US. 46 The US taget is much moe ambitious. It binds until 2040 (see panels a and b). The gap in consumption with and without the mandate is bigge in the US than in the EU. As seen fom Fig. 7(a) and 7(c), fist gen fuels decline in use without a mandate fo seveal yeas befoe becoming economical in esponse to ising enegy pices. Afte 2030, thei use inceases even without a mandate. In the absence of egulation, the global shae of oil in tanspot steadily deceases fom 95% in 2007 to 84% in The shae of biofuels inceases, mainly due to an incease in the maket shae of fist gen fuels. With no egulation, second gen biofuels ae not economically viable by 2022 in the US wheeas they ae adopted by 2017 in the EU. This is due 45 We do not show the EU case because it does not change appeciably. 46 Global biofuels poduction unde the baseline scenaio is 18 billion gallons in

28 to lowe unit costs in the EU. The poduction of fist gen fuels, howeve, does show a moe apid gowth afte 2030, mainly because of a educed demand fo land (see Fig.7a and 7c). Fig. 6(a). Land allocation in US: land is shifted out fom food to fuel Fig. 6(b) Land convesion in MICs Figue 6. Land allocation unde Base and REG (yea 2022) Note: An aea lage than cuent US famland is cleaed in the MICs but most of it is due to demand gowth not enegy policy With no egulation, annual wold poduction of biofuels is constant at about 20 billion gallons until 2020, inceasing to 96 billion in 2050 (not shown). 47 The stagnation until 2020 is due to a apid incease in the oppotunity cost of land, caused by the gowing demand fo food. Indeed, land ents double in the US and EU duing this peiod. Beyond 2020 howeve, food demand levels off, and so do land ents. But the scacity ent of oil continues to incease, making gasoline expensive and biofuels economically feasible (Fig. 7). 47 Although the fist gen biofuels consumption goes beyond that in REG as shown in Fig 7(a), the total consumption of biofuels (sum of fist-and-second gen biofuels) is lage unde the REG. Unde the BASE scenaio, the consumption of second gen biofuels is nil since they ae not competitive. 26

29 Fig. 7(a) US fist gen biofuel use Fig. 7(b) US second gen biofuel use Fig. 7(c) Shae of biofuels in tanspot in EU Figue 7. US and EU biofuel use (with and without mandates) Notes: The US mandate is moe stingent, as can be obseved by the vetical distance between the dashed and solid lines. Since the EU mandate is in pecent tems, we epot pecent figues fo it. 4. Mandates educe cude oil pices and cause significant leakage and diect emissions. The pimay goal of biofuel egulation is to educe diect emissions fom the enegy secto. US emissions fall by less than 1% and EU emissions by about 1.5% (see Table 6). 48 The switch towads the less cabon intensive enegy is patially offset by the ise in the demand fo the blended fuel. 48 Obseved aveage cabon emissions fo pevious yeas ae close to ou model pedictions. The fome ae 1.7, 0.9 and 5.8 tons of CO 2 e fo the US, EU and Wold in 2007, vey simila to ou base figues shown in Table (IEA, 2009c). 27

30 Table 6. Diect cabon emissions in billion tons of CO 2 e (REG) US EU Wold (-0.9%) 0.81(-1.5%) 6.30 (-0.5%) Note: We compute cabon emissions in tems of CO 2 e (CO 2 equivalent), which includes othe geenhouse gases such as nitogen dioxide and methane. Numbes in paenthesis epesent the pecentage change of cabon emissions compaed to BASE model, which is not shown. The mandates, while inceasing the consumption of biofuels in the US/EU, incease oil consumption and educe biofuel use elsewhee. This occus because of tems of tade effects the mandate lowes the wold pice of oil (see Table 4). In 2022 the pice of oil is about 1% lowe, while the pice of biofuels inceases by 11% with mandatoy blending. The net effect is that biofuel consumption outside the US and EU goes down by 20% in 2022, most of it in MIC counties. Oil use in the est of the wold goes up by 1%. 49 Globally, annual diect emissions of cabon decease by less than 1%. Although the US/EU consume a significant shae of global tanspotation enegy - 53% in 2007 which declines to 28% in 2050 the decline in emissions in these two egions is mostly offset by spatial leakage. The net effect of mandatoy blending policies on global diect emissions is small (Table 6). 5. Indiect cabon emissions incease. Biofuel mandates lead to an incease in indiect global emissions (see Fig.8). The mandates incease total emissions in most yeas elative to the unegulated (BASE) case, which to a lage degee is due to land convesion. Total emissions (diect and indiect) also incease in the nea tem (see Figue 8). Since we tack the amount and quality of land that is conveted fo agicultue, we can compute indiect emissions fom land use. Regadless of whethe biofuel mandates ae imposed in ou model, the inceased demand fo food 49 We only discuss spatial leakage while othe models have studied inte-tempoal leakage (e.g., see Fische and Salant, 2011) and inte-sectoal leakage (Fulleton and Heutel, 2010). 28

31 and enegy causes lage-scale land convesion. The mandates only acceleate this pocess. In 2022, indiect cabon emissions incease by 60% (o 4.4 billion tons of CO 2 e). As a esult, total cabon emissions in non-egulated counties incease by the same amount, which is much lage than the annual savings fom egulation in the mandated counties (0.01 billion tons). In aggegate, cabon emissions incease by about 4.4 billion tons of CO 2 e due to mandatoy blending (see Fig. 8). Figue 8. Biofuel mandates do not educe cabon emissions Notes: Shown fo Total emissions ae the sum of diect and indiect emissions. 6. Welfae declines in the non-egulated counties. We compute the egional gains and losses in aggegate consume and poduce suplus fo the food and enegy commodities as a esult of the mandates. Medium and low income counties expeience the lagest loss in welfae with mandatoy blending. Howeve, the US expeiences a slight incease in welfae. These esults ae pimaily diven by changes in suplus fom agicultue. The mandates incease biofuel poduction, which causes an incease in the oppotunity cost of land, which in tun dives up the pice of agicultual commodities (both food and enegy). This has a significant positive impact on suplus in the US agicultual secto, which is one of the stated goals of the mandate (see de Gote and Just 2010). 29