bio-fuel production system in france: an economic analysis

Biomass and Bioenergy 20 (2001) 483–489

Short communication

Bio-fuel production system in France:an Economic Analysis�

J.C. Sourie, S. Rozakis ∗

INRA Economie et sociologie rurales, BP 01-78850 Thiverval-Grignon, France

1. Introduction

Liquid bio-fuel production take-o., during the lastdecade, has placed Europe third behind Brasil andUS with 6% of the world volume. Biofuel produc-tion has reached a signi4cant level in France, wheremore than half of total European production of ethanoland methyl esters is produced. This development isdue to a support program, that has been implementedsince 1993, including tax exemptions of 0:35 l−1

for methyl ester from vegetable oil and 0:50 l−1 forbio-ethanol. A surface of 320 000 h has been culti-vated, mainly on land set aside, in the 1999–2000period, to supply liquid bio-fuel chains. Productionprocesses and technical coe9cients are presented inTable 1. Total production will increase according tonew agreements allocated to the industry by the Frenchgovernment (three more conversion units), to reachthe following quantities in 2002–2003 (Table 2).

� This short communication updates and develops results pre-sented by the INRA-ESR research group [1] in AGRICE meetingsof (AGRIculture pour la Chimie et l’ Energie) scienti4c interestentity grouping French Ministries of Agriculture, Industrie, Envi-ronment and Research as well as interested research and profes-sional organizations (ADEME, INRA, IFP, CGB, ONIDOL) andcompanies (Rhone Poulenc, Total4na-Elf).

∗ Corresponding author. Tel.: +33-1-30-81-53-41; fax: +33-1-30-81-53-68.

E-mail address: [email protected] (S. Rozakis).

2. A partial equilibrium model for the economicanalysis of bio-fuel chains

A partial equilibrium economic model (OSCAR) 1

has been built, based on mathematical program-ming principles, in order to assist in micro- andmacro-economic analyses of the multi-chain systemof bio-fuel industry. This approach, modeling exist-ing bio-fuel chains in France—sugarbeet and wheatto ETBE, rapeseed to RME—points out:• that a comprehensive and systemic method is re-quired, due to the bio-fuel chains interdependency,at the resource production level and also at theoutput level [2].

• that detailed modeling of agricultural supply is re-quired to take into account the diversity of the arablefarming system, agronomic constraints and produc-tion techniques. 2

• possibilities to proceed to the economic optimiza-tion of the whole system and to use multi-criteriamethods to assist policy making. 3

Each chain consists of 4ve production stages:biomass production, collection, 4rst and second

1 OSCAR: “Optimisation du Surplus Ieconomique des CarburantsAgricoles Renouvelables”.

2 Optimization model with a matrix of technical coe9cientsof 7500×6800 written in GAMS code. The agricultural sectorcomponent aggregates 700 elementary arable farm models locatedin sugarbeet and cereal production regions.

3 Decision support tools have been proposed applied to biofuels[3,4] and to bio-electricity [5].

0961-9534/01/$ - see front matter c© 2001 Elsevier Science Ltd. All rights reserved.PII: S0961 -9534(01)00007 -1

484 J.C. Sourie, S. Rozakis / Biomass and Bioenergy 20 (2001) 483–489

Table 1Technical coe9cients of biofuel production activity

Production phases Category Element Units ETBE ETBE Ester

Sugarbeet Wheat Rapeseed

Resource Agricultural landa ha 0.07 0.19 0.64Stage 1: agricultural production Input Agric. biomasse t 5.9 1.7 2.50Stage 2: Output–input Ethanol l 587.85 587.85First transformation phase Output Dra. t 0.70

Output-input Rapeseed oil t 1Output Cakes t 1.40

Stage 3: Input Methanol t 0.1Second transformation phase Input Iso-butane t 0.58 0.58

Output Biofuel weight t 1 1 1Output Biofuel volume l 1333.3 1333.3 1136.0Output Glycerine t 0.10

aAverage yields of fertile areas in the Northern part of Parisian Basin, projection 2002.

Table 2Bio-fuel production in France

Sugarbeet Wheat Rapeseed Total

Production ETBEa in t 249333 124667 374000Production RMEa in t 387507 387507

aETBE: Ethyl tertio-butyl ether. RME: Rapeseed methyl ester.

transformations, demand of bio-fuels and by-products.The model determines:• optimal biomass supply and farmer surplus, giventhe policy context and agronomic environment,

• opportunity cost of bio-fuels depending on cropsupply, industrial costs and the demand for bio-fueland by-products,

• the optimal tax exemption allocation to bio-fuelchains and agents’ surpluses in di.erent market con-texts (monopoly, cartel, etc.)

The structure of this model allows consideration of ad-ditional chains such as straw to ETBE. Environmen-tal e.ects generated by the activity can be calculatedby the model and compromises with additional objec-tives can be determined through multi-criteria decisionmaking.

3. Bio-fuel costs in the horizon 2002

The 2002 horizon has been selected as CommonAgricultural E.U. Policy which will be further mod-i4ed by then. Expected biofuel production levels, in

2002, are introduced in the model as targets to beattained by the system in order to obtain thebiomass and bio-fuel costs. 4

3.1. Opportunity cost of agricultural resource,yields and cultivated area

In order to minimize bio-fuel cost, OSCAR local-izes the production in the most e9cient farms. A min-imal farm income increase of 76 ha−1 is supposed asincentive for farmers cultivating energy crops. 5 Op-portunity costs 6 of rapeseed and wheat, calculated bythe model (in Table 3), are much lower than food

4 Bio-fuel costs and especially biomass agricultural resourcecost are increasing with increasing quantities produced.

5 With no incentive, last suppliers’ revenue increase will be toolow to compensate additional labor devoted to the cultivation ofnon-food crops instead of land set aside.

6 Opportunity costs are equal to the dual values of the biomassavailability constraints of the model.

J.C. Sourie, S. Rozakis / Biomass and Bioenergy 20 (2001) 483–489 485

Table 3Opportunity costs (2002) of resources and average yields

Yield (t) t−1 Q (kt) Surface (ha)

Rapeseed 3.9 166.9 1466 246250Wheat 9 64.8 209 23387Sugarbeet 82.8 17.7 969 17705

crop prices (175–183 t−1 et 99–107 t−1, respec-tively). This is explained by the fact that rapeseed andwheat for energy are cultivated in land set aside withvery low land rent. Active set aside land rate reaches5%. 7 Sugarbeet cost should be compared with sugar-beet category C which competes in the world market(around 15.25 t−1in 1999).Total surface to be cultivated in order to satisfy

exogenous demand for bio-fuels is set at 287,300 ha(Table 3). It is clearly lower than actual surface cul-tivated by energy crops. This is due to the high levelsof average yields resulting from optimal localizationof production. In fact, surface harvested in 2000 hasreached 320,000 ha even if actual approved quantitywas only 536,500 t (Source: ONIOL). The model se-lects 58,800 arable farms or 72% out of 81,000 farmspotentially participating in the bio-fuel program. Eachfarm cultivates 4 ha of energy crops in average. Ifprices to the producer are equal to the opportunitycost (Table 3), farm income increases about 900 perfarm.Costs of biofuels are quite di.erent, ester costs

being higher than these of ETBE (Table 4). 8 Directcosts of ETBE are 2.2–2.4 times higher than un-

7 Formal set aside rate is 4xed at 10% of the cereal and oil andprotein seed cultivated land historically, a 5% rate has been usedto take into account Quctuations of rates revised by Brussels eachyear, depending on cereal stocks and international market as wellas 4xed set aside concerning low fertility marginal land whichcan be cultivated at prohibitive costs.

8 Mass volume ratios 0.75 kg dm−3 for ETBE; 0.88 kg dm−3

for RME (Source: [6]). Wheat-to-ethanol study takes into consid-eration economies of scale for plant capacity of 300m3 per dayinstead of 100m3 per day [7]. Sugarbeet-to-ethanol conversioncost is di9cult to estimate due to overlappings among ethanol, al-cohol and sugar production processing industry. ETBE from sug-arbeet and RME conversion costs (mission Levy-Couveinhes Mai2000, personal communication [8]). Cattle cake prices increasedfrom 91.5 to 130 t−1, dra. prices from 102 to 122 t−1 whereasglycerine ones felt from 457 to 381 t−1.

leaded gasoline costs whereas RME costs 2.9 timesmore expensive than diesel fuel cost. These ratiosdecrease signi4cantly in 2000, taking into accountcurrent petroleum and dollar rates, at 1.1 and 1.6,respectively. 9

Costs include farmers’ surplus and the economic in-centive of 76 ha−1. Ethanol from wheat is producedin a plant of 300m3 per day capacity. Actually operat-ing units in France run with one third of this capacity.Industrial cost of ethanol from sugarbeet takes intoaccount synergies among sugar, alcohol and ethanolindustry. On the other hand, ester is produced in anintegrated unit of the same type as that one actuallyoperating in Rouen (120,000 t RME per year).The cost of the agricultural resource is important

for RME, which makes the chain sensitive to inputcost variations. This cost is partly compensated byco-product sales. Wheat-to-ETBE chain co-producesdra., that is rich in proteines. Co-products of ETBEfrom sugarbeet (pulp, inferior wine) have a lowmarketvalue but its industrial costs are also lower than ETBEfrom wheat ones.Minimal subsidy required for biofuel industries to

break even is presented in Table 5. Taking into ac-count hypotheses mentioned (only e9cient farmersproduce, minimum farm income of 76 ha−1 as an in-centive to the less e9cient farmers, industrial costs, oilaverage prices and dollar average value over the pe-riod 1992–2000), di.erences between actual and the-oretically minimum subsidies vary between 0.07 and0.14 l−1.The elimination of the set aside program leads to the

free competition between food and energy crops. Inthis case, opportunity cost of energy wheat or rapeseedequals respective food crop prices whichmakes energycrops more expensive, at about 0.04 l−1 (Table 6).

9 Note that adjustments have to be made also to measure thee.ect of high oil prices on bio-fuel production cost.


Table 4Cost of bio-fuels (Source: model OSCAR results for set aside rate of 5%)

Resource Conversion Co-product Biofuel Bio-fuel valuecost cost sales costs Averagea 2000b

ETBE wheat l−1 0.08 0.27 −0.06 0.29 0.13 0.27ETBE sugarbeet l−1 0.08 0.25 0.002 0.32 0.13 0.27RME l−1 0.37 0.22 −0.19 0.40 0.14 0.25

aaverage 1992–2000 FOB Rotterdam brent 18.6 per baril, $ 1 = 0:87 ; source DIMAH.bBrent $28,11 per baril, 1$ = 1:06 .

Table 5Minimal subsidization of bio-fuels (oil and dollar price averages for 1992–2000)

Biofuel value Biofuel cost Minimum tax exemption Current tax exemption

t−1 l−1 t−1 l−1 t−1a l−1a l−1b l−1b

ETBE wheat 177 0.13 390 0.29 213 0.16 0.36 0.50ETBE sugarbeet 177 0.13 429 0.32 252 0.19 0.43 0.50RME 157 0.14 454 0.40 297 0.26 0.26 0.35

a(regarding ETBE chains) results presented per t or l of ETBE.b(regarding ETBE chains) results presented per l of ethanol.

Table 6Impacts of set aside program elimination on bio-fuel costs

Resource Industry Co-product Biofuel Bio-fuel Cost/valuecost cost sales costs value ratio

ETBE wheat l−1 0.12 0.27 −0.06 0.33 0.13 2.5ETBE sugarbeet l−1 0.08 0.25 −0.002 0.32 0.13 2.4RME l−1 0.41 0.22 −0.19 0.44 0.14 3.2

ETBE from sugarbeet cost remains unchanged due tothe fact that sugarbeet do not receive any set asidesubsidies.

3.2. Induced economic bene6t by agriculturalproduction of biomass for bio-fuels

Farmers’ surplus 10 measures total rent enjoyedby farmers producing at a cost lower than the op-

10 This surplus is generated during the transaction of the agri-cultural resource between farmers and the bio-fuel industry dueto the fact that industry is not able to di.erentiate the price ofenergy corps for such a large number of farmers. In order to havea zero surplus industry should o.er each farmer its speci4c pricewhich is practically impossible due to the large number of farmersinvolved in the process.

portunity cost of the least e9cient farmer, shown inTable 3. Economic incentive (Table 7) corresponds tothe amount of 76 ha−1 given to all farmers. Becauseof biofuel yields per hectare (Table 1), this amount ismore important for RME than ETBE. 11 Economiesover set aside subsidy concern exclusively sugarbeetto ethanol, as its production for energy diminishes theamount of direct aides by farm. 12

11 On the basis of average yields shown in Table 3, RMEproduction per ha reaches 1.75m3, the one of wheat-to-ETBE,7.14m3 and that of sugarbeet-to-ETBE, 18.77m3 (0.59m3 ofethanol per t ETBE).12 Unlike wheat and rapeseed energy crops, sugarbeet for ethanol

production does not enjoy any CAP subsidy, which saves the E.U.budget by 425 per ha of sugarbeet cultivated surface.


Table 7Bene4t induced by the production of bio-fuel crops in m−3

Farmers’ surplus Economic incentive CAP savings Total bene4ts

ETBE wheat 4.42 10.67 15.09ETBE sugarbeet 4.27 3.96 22.41 30.64RME 60.22 42.54 102.76

Globally, induced economic e.ects are very impor-tant in relative terms, especially for the RME chain.The ETBE chain gets bene4t from the set aside subsi-dies. Wheat-to-ETBE chain generates theleast inducedeconomic e.ects at the agricultural production level.

4. Conclusions

OSCAR is a partial equilibrium model that allowsfor a comprehensive micro-economic analysis of bio-fuel industry applying an integrated (chain oriented)and systemic (multi-chain optimization) approach. Itcan be used for economic analyses taking into accountmicro-economic realities but also for multi-criteriaanalysis and enviromental economics approaches.Data used by this model are thoroughly detailed andallow for the implementation of parameterization oftechnical and economic coe9cients.This note has presented estimations of micro-

economic cost of bio-fuels resulting from the min-imization of the agricultural resource costs ofproduction, in the horizon 2002. This minimizationis extremely important for the RME chain becauseof the weight of the agricultural input on the totalbio-fuel cost. ETBE cost has been estimated at 0.29–0.32 l−1 and the RME one at 0.40 l−1. Optimiza-tion of industrial costs is treated in less detail dueto unsu9cient available information at this moment.Results mentioned here, should not lead to prema-ture conclusions on the relative interest of particularchains. Nevertheless, minimal tax exemption estima-tions (di.erentials of costs and values) are available,depending on oil and dollar prices. These tax exemp-tions represent expenses for the budget partly justi4edby induced economic e.ects but also by positiveexternalities generated by the biofuel activity.The agricultural resource is produced at least cost

by the most intensive farms, which makes the surfacerequired by energy crops to decrease; however, inten-

sive energy crop cultivation involves higher risk of en-vironmental pollution. Coupling this micro-economicmodel to bio-physical models (as those studied anddeveloped by INRA research teams, CERES, STICS)could contribute to cope with this question and ex-amine alternative cultivation techniques scrutinizedthrough economic and ecological lenses.

Appendix A. Model speci"cation

Indices and variablese farm indicesw relative weight of each farm in the modelal vector of food crop surfaces in haja vector of set aside land surface in hanal vector of food crop surfaces in hatr vector of variable quantities of energy crops

transformed in bio-fuels in tvt vector of bio-fuel quantities in tvc vector of co-product quantities in t

Coe7cient matricesA 1–4 sub-matrices of technical agricultural produc-

tion coe9cientsR sub-matrix of non-food crop yields in tT sub-matrix of conversion coe9cients[I ] unitary matrixsub vector of unitary subsidies to bio-fuels

Agricultural sectorA1e(ale; jae; nale) 6 wete agronomic constraints,A2e(ale; jae; nale) 6 wefe Qexibility constraints,A3e(ale) 6 weqe quota for food crops,A4e(jae; nale) 6 wese constraints of set-aside

for each e

Biomass availability, conversion process and bio-fueldemand constraints−∑

e Re nale + [I ]tr 6 0 biomass availability (I)−T1tr + [I ]vt 6 0 bio-fuel supply (II)


−T2tr + [I ]vc6 0 co-product supply (III)sub.vt6 max sub maximal subsidy to

bio-fuels (IV)

Objective function: to maximize global surplus

S=∑

e(maeale+mjaejae−cnalenale)−ctr:tr+(pvt+sub)vt + pvc:vc

ma vector of gross margins of foodcrops FF/ha

mja vector of gross margins of set asideland FF/ha

cnal vector of variable costs ofnon-food crops

ctr vector of total costs of biomasscollection and conversion tobio-fuels

pvt bio-fuel price vectorsub subsidies to bio-fuels vectorpvc co-product price vectorUAS = S∗ − S∗0 variation of surplus due to optimal

biofuel production

Appendix B. Surplus allocation to farmersand industry

Dual prices that correspond to biomass availabilityconstraints equal the opportunity cost of the agri-cultural resource. If e. denotes marginal value oftotal subsidy, this is equal to the dual value of con-straint (IV). Farmers’ surplus, or farm income increasedue to energy crop production is: V S-e.∗maxsub.

Fig. 1. Economic surpluses generated by biofuel production.

Industry surplus is then equal to e.∗maxsub. If thebudgetary constraint is not bound, global surplus isequal to farmers’ surplus (Fig. 1).

Tax exemption for biofuelsOB: biofuel marginal cost=biomasse oppor-

tunity cost+conversion cost–coproductvalue,

OA: biofuel market price (perfectly elastic de-mand),

OC: biofuel value=biofuel market price + taxexemption (AC),

OO: quantity produced in the equilibrium (bio-fuel value equal to its marginal cost),

CBB′′: producer (agricultural sector) surplus,CB′′A′′A: total cost to the government of the biofuel

support program.

Tax exemption for biofuels under budgetary con-straintCC′A′A: total budget earmarked to biofuel,OO′: biofuel quantity allowed to be produced

(agreements approved by the governmentthat depend on earmarked budget),

CC: tax exemption to biofuel (depends on bud-get, and industry lobbies),

EBB′: producer (agricultural sector) surplus,ECC′B′: industry surplus.OSCAR can minimise social cost (ABB′A′: budgetcost—agents’ surpluses) determining tax exemptionvalues per unit of biofuel volume given 4xed amountsof government expenditure.


Acknowledgements

Stelios Rozakis wishes to acknowledge 4nancialsupport from the European Commission for 4nancialsupport (DGVI FAIR BM 98 5316).

References

[1] Sourie JC, Hautcolas JC, Bonnafous P. Un bilan micro etmacro-Ieconomique des biocarburants et les perspectives derIeduction des couts. Actes du colloque Agrice. France, 22–23Avril 1997. p. 49–57.

[2] Bard JF, Plummer J, Sourie JC. A bilevel programmingapproach to determine tax credits for biofuel production.European Journal of Operational Research 2000;120:30–46.

[3] Sourie JC, Rozakis S, Garrastazu A. A multi-criteriamethodology to determine tax exemption levels for bio-fuel

production in France. In: Papadakis G, editor. Proceedings ofthe AgEnergy Conference, Agricultural University of Athens,Greece, 1999. p. 939–46.

[4] Sourie JC, Rozakis S, Vanderpooten D. A multi-criteriamethodology to determine tax exemption levels for bio-fuelproduction in France. Working paper INRA, Cahiers deGrignon 1999. p. 3–99.

[5] Saez RM et al. A multiple criteria decision tool for theintegration of energy crops into the Southern Europe EnergySystem. In: Kyritsis S, editor. Proceedings of the 1st WorldBiomass for Energy Conference, Sevilla, 2000.

[6] Levy RH. Les biocarburants, MinistXere de l’Industrie et duCommerce ExtIerieur. Paris, 1993.

[7] Herbert V. Analyse technico-Ieconomique de la productiond’Iethanol carburant de blIe, MIemoires et thXeses No. 13. INRAESR. Grignon; 1995.

[8] Sourie JC. Contributions aux travaux de la missionBiocarburants JD Levy, P Couveinhes. Unpublished document,INRA-ESR, Grignon, 2000.

bio-fuel production system in france: an economic analysis

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