Employment impacts of EU biofuels policy: combining bottom-up technology

information and sectoral market simulations in an input-output framework

Frederik NEUWAHL*, Andreas LOESCHEL,

Ignazio MONGELLI, and Luis DELGADO

European Commission, DG Joint Research Centre,

Institute for Prospective Technological Studies (IPTS)

Edificio Expo, c/ Inca Garcilaso s/n, 41072 Seville, SPAIN

Frederik.NEUWAHL@ec.europa.eu

This paper analyses economic impacts and employment consequences of policies (such

as the "biofuels directive" 2003/30/EC) aimed at the promotion of biofuels use in the

EU energy mix. The promotion of biofuels use has been advocated as a means on the

one hand to promote the sustainable use of natural resources and to reduce greenhouse

gas emissions originating from transport activities, on the other hand to reduce

dependence on imported oil and thereby increase security of European energy supply.

This paper takes into consideration two specific policy options: a non-subsidised

mandatory blending obligation (entailing increased fuel prices) and a fuel tax

exemption equivalent to the cost disadvantage of biofuels, which in turn is financed by

increasing direct taxation in order to guarantee government budget neutrality.

The employment impacts of increasing biofuels shares are calculated by taking into

account a set of elements comprising: the demand for capital goods required to produce

biofuels, the additional demand for agricultural feedstock, higher fuel prices or reduced

household budget in the case of price subsidisation, price effects ensuing from a

hypothetical world oil price reduction linked to substitution in the EU market, and price

impacts on agro-food commodities. The calculations refer to the achievement of year

2020 targets as set out by the recent Renewable Energy Roadmap (overall 20% share of

renewable energy, with 10% substitution of transport fuels with biofuels).

Direct and indirect employment effects are assessed in an Input-output framework

taking into account bottom-up technology information to specify biofuels activities and

linked to partial equilibrium models for the agricultural and energy sectors.

The Input-output model incorporated different modules, including a mixed endogenous-

exogenous variables IO model (which was used to accommodate constraints on

agricultural production), an IO price model that was used to compute a new vector of

commodity prices due to an exogenous factor price increase, and an Almost Ideal

Demand System (AIDS) model, which calculated the final demand vector

corresponding to the vector of prices and to the household budget reduction calculated

as the difference between the production cost of the biofuel and the production cost of

the conventional fuel it replaces (in the case of biofuels subsidies).A set of scenarios

was derived from the energy system models Primes and Green-X. The data was fed into

the agricultural model ESIM to calculate production levels and prices of agricultural

commodities as a consequence of the policy shock. The results of the energy system

models and the agricultural market model were then used to simulate economic and

employment impacts in the Input-output model. To this end, an aggregated Input-output

table (IOT) of 57 sectors/commodities for the EU-25 (base year 2001) was constructed

based on the GTAP6 database. 7 new sectors were then added to the IOT to describe

petrol and diesel fuels and their bio-based substitutes, bioethanol and biodiesel each

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produced by two different technologies, and a sector providing the capital goods for the

production of biofuels. The description of these sectors was derived from bottom-up

techno-economic data adapted from the Well-to-Wheels report (EUCAR, CONCAWE

and JRC).

1. INTRODUCTION

The European Union has demonstrated in recent years substantial interest in the

promotions of biofuels, as they are considered to be the only substitute to oil-derived

fuels available in the short-to-medium term in sufficient amounts at reasonable costs.

Biofules have therefore gained particular attention in the light of the perceived

precarious security of supply for oil and its potential repercussions for the transport

sector, and in 2003 the EU adopted the Biofuels Directive (2003/30/EC) with the

objective to achieve a biofuels substitution share of 2% in 20005 and up to 5.75% in

2010.

Progress in achieving the Biofules Directive targets was however uneven among the

Member States and overall distant enough from the target to generate the widely shared

opinion that the 2010 targets would be missed in the absence of additional policies (the

overall share in 2005 was 1%). One of the key factors to the insufficient progress

towards the Biofuels Directive targets has in fact been identified in the lack in most

Member States of an appropriate support system compensating for the additional

production cost of biofuels compared to the cost of producing conventional fuels.

This paper elaborates on the basis of a study that was conducted for the Impact

Assessment of the Renewable Energy Roadmap and for the Biofuels Directive Progress

Report at the Institute for Prospective Technological Studies of the European

Commission's DG Joint Research Centre (JRC). This exercise combined in an input-

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output based model information originating from different studies conducted at DG

Energy and Transport (TREN), at DG Agriculture (AGRI) and at the JRC, with the

primary purpose to estimate employment effects ensuing from the implementation of

selected biofuels policy scenarios in Europe (2020 targets). Note that this paper focuses

on the core input-output based model and does not endeavour to give a detailed account

of the bottom-up studies and of the energy and agricultural simulations that were used

to generate input data.

2. THE POLICY-TECHNOLOGY SCENARIOS

In the Renewable Energy Roadmap, the European Commission concluded that a

binding 10% substitution target was achievable. In drawing this conclusion, scenarios

having a substitution share up to 15.2% were examined. The scenarios introduce

different replacement shares of conventional fuels by four different kinds of biofuels,

which are bioethanol, produced by two different technologies, to be blended to petrol

and two different production technologies for biodiesel:

First generation bioethanol: Ethanol from fermentation of sugar and starch crops.

Domestically, it is assumed to be from a mix of cereals and sugar beet. When

imported, from sugar cane.

First generation biodiesel: Vegetable oils from crushed oil seeds (EU-grown

rapeseed, imported soybean and palm oil).

Second generation bioethanol: Ethanol from fermentation lignocellulosic feedstock.

Second generation biodiesel: Synthetic Fischer-Tropsch liquid fuel obtained from

biomass gasification.

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Although second generation technologies are today still at demonstration plant stage,

the scenarios assume that a decrease of the main biofuel conversion process cost be

feasible by the year 2020; this is implemented by introducing learning effect cost

reductions, as compared to WTW data, on capital costs, labour costs and other fixed

operating cost.

This paper analyses four scenarios for biofuels penetration in the year 2020, adapted to

be consistent with the EU energy outlook as separately calculated on behalf of the

European Commission by the energy systems PRIMES (Capros and Mantzos, 2000)

and Green X (Huber et al, 2004). The four scenarios are a business as usual scenario,

entailing modest biofuels penetration as expected in the absence of further specific

policies in addition to those already in place, and three high renewables share scenarios,

in one of which additional constraints on cost minimisation were introduced. Note that

the four scenarios listed below are but a sample of the larger portfolio of scenarios that

were analysed by the European Commission in the course of preparing the recent

Renewable Energy Roadmap and Biofuels Progress Report:

Business as Usual (BAU) scenario: 6.9% total biofuels share, mostly first generation

PRIMES Hi Res. 1st generation (PRIMES G1) scenario: 15.2% total biofuels

share, with EU production mostly with first generation technology

PRIMES Hi Res. 2nd generation (PRIMES G2) scenario: 15.2% total biofuels

share, with EU production mostly with second generation technology

Green X least cost (GX-LC) scenario: 12.3% total biofuels share, with a larger

share of imported biofuels.

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Moreover, a hypothetical case with no biofuels at all has been specified as a

reference (Zero scenario).

In the light of a growing global biofuels market, for certain sectors of the European

economy new export opportunities will arise (e.g. processing plants). It was therefore

assumed that the development of a strong European biofuels industry will result in a

competitive edge of European firms in the world market for biofuels plant technologies.

This is represented by setting an export volume for biofuels technologies (represented

as capital goods and engineering services) proportional to the EU production of

biofuels. Table 1 presents a summary of the key figures assumed in the four scenarios

and for baseline oil and fuel prices.

The impacts on feedstock prices and on the prices and produced quantities of

agricultural commodities in general were assessed by running, with the ESIM model

(Banse, Grethe and Nolte, 2005), scenarios that are consistent with the feedstock

demand for biofuels as specified in the four scenarios. All prices and quantities were

subsequently expressed as incremental values compared to those calculated under the

Zero scenario, in order to single out the impacts of each of the biofuels policy

scenarios.

Table 1

3. THE MODEL

The model structure was specified with a view to allowing the simulation of those

parameters that were deemed essential. The general aim of the modelling endeavour

was to calculate the employment impacts in the EU 25 as a consequence of attaining the

biofuels targets as specified in the policy scenarios described in the previous section,

subject to the following main drivers: contraction of the oil refinery sector, expansion 6

of biofuels production and of the biofuels industry, expansion of the agricultural sector

for cultivation of starch, sugar and oil crops, increasing prices (which have budgetary

consequences for consumers) of food products due to increased competition for

agricultural products as feedstock for fuel production, fall of crude oil price due to

diminishing EU oil demand on the world market and, finally, the financing scheme for

the biofuels policy. In this respect, in the main policy case it was assumed that the

additional cost of biofuels as compared to fossil transport fuels be fully compensated by

fuel tax breaks. Consequently, the consumer end price of blended transport fuels

remained unchanged throughout all scenarios and no reduction of transport fuel

consumption due to price increases had to be considered. The cost compensating tax

reductions are however recollected from private consumers through an increase of

general taxation of equal amount. This in turn causes a reduction in the disposable

income of consumers and therefore a general reduction in demand. An alternative

policy case was also considered, in which the biofuels targets are enforced by

mandatory blending share obligations instead of cost-compensating tax breaks. In this

case the household budget is not affected directly, but the fuel prices are allowed to

increase to bear the extra cost of the blended biofuel.

Figure 1 is a schematic block diagram of the Input-output model that was developed for

this study. The Input-output model was composed of three main modules reflecting the

logical order of the modelling exercise: an IO price model, a Demand System and a

mixed endogenous-exogenous variables IO core model. The price model was used to

translate the exogenous agricultural (and other) commodity price variations in a new

final vector of prices. The Demand System was then utilised to produce a new

household consumption vector consistent with the new vector of prices and constrained

by the total household budget. The mixed endogenous-exogenous variables IO model,

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finally, produced the new vectors of sectoral gross output quantities and employment

figures, subject to production quantities in the key agricultural sectors constrained to the

values calculated by the ESIM model for each scenario. The following sections expand

on the key data and modelling issues; section 3.4 contains a detailed commentary to

Figure 1.

Figure 1

3.1. Input-output table and satellite accounts

An Input-output accounting framework was set up to account for direct and indirect

employment effects associated with the targets specified in each scenario. This was

done using an input-output table aggregated for the whole EU25 derived from the

GTAP6 database, using the original 57 sectors classification, without further sectoral

aggregation. This classification includes 22 agricultural and food sectors and allowed

accounting explicitly for most of the agricultural commodities either used by the

biofuels industry or affected by the biofuels policy as a consequence of land

competition or in relation to price effects on certain by-products. The base year of

GTAP6 is 2001.

The primary focus of this study was the calculation of employment effects. The GTAP

database contains data on labour wages distinguishing low and high labour skills, but

not physical data on employment numbers. The input-output table was hence

complemented with labour input data adapted from the OECD's STAN database. The

classification of the STAN database can be mapped straightforwardly on most of the

GTAP sectors but not on the 22 agricultural and food sectors. Additional data for labour

inputs to different agricultural activities was then collected and adapted at IPTS.

Employment data in the agricultural sector are often expressed in AWU (Annual Work

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Units), with full-time employment equivalents assuming in this case an average of 1800

yearly hours per full time job. This equivalence construction is necessary since, more

than in other sectors, the number of people engaged in agriculture is much larger than

the number of full yearly incomes generated. This is an important issue, since one must

be aware that, whereas an increase in agricultural output can be expected to drive

additional AWU, the linkage to new physical jobs creation may be less evident.

The starting point for constructing detailed employment accounts for the different

agricultural activities was the official data on AWU per country. The latest year for

which those data were available for the EU 25 was 2003. The total AWU assumed were

9.8 million for the EU25 and 6.3 million for the EU15.

Farm Accountancy Data Network (FADN) data were then reviewed to obtain labour

input per ha and per crop as realistic as possible. This is not a trivial exercise, since

FADN, the instrument used for evaluating the income of agricultural holdings and the

impacts of the Common Agricultural Policy, is built on real farms, and real farms

produce more than only one crop. Whenever possible, the values from specialized types

of farming were used to extract labour input per ha per crop. In parallel, a literature

review and expert judgment were used to estimate the range of the maximum and

minimum number of hours per ha. Using these three figures -max and minimum IPTS

estimated values and the values derived from FADN- three different estimations of total

AWU were obtained, taking into account the acreage devoted to each specific crop. In

general the values obtained using FADN data are slightly higher than the estimated

values, but the difference were in the same order of magnitude for most of the crops. In

three cases (olives, sugar beet, vines), where differences were excessive, the FADN

AWU were adjusted taking into account IPTS estimated values. The total number of

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AWU for the EU 25 obtained at the end of this estimation procedure was checked

against the official data for AWU published by DG AGRI and found to be very close.

Since the scenarios analysed under this policy impact exercise refer to the year 2020, in

principle one should endeavour to project the input-output table to this year. However,

availability of the official macro aggregates necessary for the projection is generally

scarce or non existent for points in time farther in the future than a few years. It was

therefore decided not to include any dynamic dimension and to interpret the results not

as directly representative of a hypothetical year 2020 but only as "what if" scenarios

with no specific time label. All baseline employment figures, for instance, are "frozen"

to the 2001 levels without considering any forecasts for demographic evolution, for

overall and sector-specific economic growth, or the long standing decreasing trend in

agricultural employment.

3.2. Further specification of liquid fuels in the IOT

In the 57 GTAP sectors, fossil fuels are included in the generic "petroleum and coal

products" sector (sector 32). Two extra sectors, "diesel oil" and "petrol" were then

disaggregated from sector 32 based on the information from two sources: refined

petroleum products use (in physical units) from the GTAP satellite accounts; and a MIT

CGE study on transportation (Choumert, Paltsev and Reilly, 2006), in which

International Energy Agency energy statistics data was mapped analogously to the

present exercise.

The diesel and petrol columns and rows were further inflated in order to account for: a)

increased fuel consumption from 2001 levels to 2020 projections consistent with the

policy scenarios considered; b) increased fuel (basic) prices in accordance with DG

TREN estimates. This partial updating of the IO table was done only for the fuel 10

sectors, with the view to simplifying the incorporation of scenario data related to

production cost and production/ consumption quantity of the fossil and bio-based fuels.

The IO table that is generated should therefore not be confused with a projected IO

table for the year 2020.

5 new sectors were finally added to generate a different IO table for each scenario: the 4

biofuels sectors and a sector providing the capital goods for the production of biofuels;

the resolution of the IO table utilised in the modelling was therefore of 64 sectors (the

original 57 GTAP sectors, petrol, diesel, the 4 biofuels and biofuels capital goods). The

sale structure of the 4 fuels was assumed to be the same as that of the fuel they replace

(diesel and petrol), inflated by the ratio of the basic prices. The inputs to the biofuels

sectors, including employment coefficients, were constructed based on process chain

data derived from the Well-To-Wheels (EUCAR, CONCAWE and JRC, 2006.

Hereinafter WTW) study and feedstock prices as calculated by the agricultural system

ESIM for each scenario.

Tables 2-5 resume the parameters used for specifying the four biofuels production

technologies (sectors) in the four scenarios:

Table 2

Table 3

Table 4

Table 5

3.3. Impacts in the agricultural markets

The overall modelling framework is based on input-output methods, combining and

adapting different elements in order to be able to capture those factors that are essential

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to a useful description of the policy response chain. In a simple demand-driven input-

output model with fixed coefficients, in fact, the additional intermediate demand for

agricultural commodities as feedstock to produce biofuels would directly translate into

additional production of the same agricultural commodities. By doing this, one would

neglect a number of important factors that include price impacts, substitution effects,

impacts on traded quantities with the rest of the world, and land constraints. It is in fact

far from realistic to assume that agricultural production in the EU could expand ad

libitum to satisfy the demand of feedstock for producing biofuels without affecting the

agricultural markets.

The impacts in the agricultural sector were therefore modelled separately in detail by

DG AGRI running, up to the year 2020, European Simulation Model (ESIM) scenarios

consistent with the four energy system scenarios considered in this study, as well as the

zero biofuels penetration reference scenario. This reference scenario is not to be

regarded as an analytical scenario but only as instrumental for the IO modelling, with a

view to pinpointing the substitution effects and commodity price changes that are due to

biofuels demand rather than to other drivers.

This included modelling of an increased import of feedstock for biofuels production

(e.g. vegetable oils) and reduced import of secondary agricultural products (e.g. animal

feed to the extent it is being substituted by the protein cake obtained a by-product of

domestic bioethanol and biodiesel). For each domestic biofuel type, it was assumed that

an increased domestic production causes an increase in (world) feedstock price, a

decrease of the value of by-products and of the price of the products they substitute.

Moreover, the domestic production of feedstock for biofuels production in part

substitutes land formerly used for the production of agricultural products for export

(cereal grains and sugar), with the consequence that export activities of the agricultural

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sector decrease at higher domestic biofuels production rates. Both bioethanol and

biodiesel can also be imported as finished biofuels, and their import price was set to 5%

below to the cost of the lowest-cost domestic production of bioethanol and biodiesel.

Since the classification of ESIM commodities is different and more detailed than the

GTAP classification, aggregated parameters for the price and quantity changes of

agricultural commodities were obtained by mapping the detailed ESIM commodities to

the GTAP classification and weighing the value increases by the relative baseline

shares. Table 6 recaps the quantity and price changes, as compared to the zero biofuels

scenario, calculated with the ESIM model for agricultural and food commodities in the

aggregated EU 25 for the four policy scenarios. All values are expressed in percentage

change calculated for the GTAP commodities, i.e. the values that were directly plugged

in the successive IO calculations.

Aggregation of the ESIM commodities to GTAP classification was straightforward in

most cases. Notable exceptions are animal feed and vegetable oils. Since no separate

GTAP commodity exists in GTAP for animal feed, the price changes calculated by the

ESIM model for protein cake could not be translated in price effects on pork, poultry

and beef; livestock price changes were instead taken as calculated by ESIM. More

important was the treatment of vegetable oils, as there is no direct correspondence in

GTAP to the rapeseed oil, sunflower oil and soybean oil commodities in ESIM. In

ESIM the three vegetable oils account together for some 4 billion € output volume in

year 2001. GTAP's sector 21 [vegetable oils and fats], conversely, accounts for some 54

billion € gross output. This sector includes in fact olive oil and other relatively high

value added products. It was assumed that the price of those additional products be not

affected by the biofuels market, and average price increases throughout the sector were

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estimated by scaling down the average price increase of the three ESIM vegetable oil

commodities by the share 4/54.

Table 6

3.4. Model Structure

As summarised in Figure 1, the modelling exercise started with the definition of an

aggregated input-output table for the whole EU 25 to which employment satellites were

added, as well as separate sectors for the four biofuel types considered and for the

conventional fuels, petrol and diesel, that are partially replaced.

A different IO table was generated for each scenario. Although other options are in

principle possible, this choice was adopted since it is the most straightforward way to

account for changing intermediate fuel demand. In the different scenarios, in fact, the

fuel substitution pattern was different, and the same also held for inputs (specifically,

for their prices) to the production of each biofuel type.

The impacts in the agro-food sectors, taking into account inter alia the production

constraints, were introduced exogenously based on the ESIM results by implementing

the input-output model through a mixed exogenous-endogenous variables calculation

algorithm (see for instance Miller and Blair), in which the demand-driven Leontief

scheme and the supply-driven Ghosh approach are combined, allowing calculating

demand-driven sectoral outputs in the presence of production constraints in certain

sectors, in order to capture the occurrence of substitution between different crops as

opposed to unconstrained production expansion. In this calculation scheme, all sectoral

outputs were calculated endogenously except for the agricultural commodities listed in

Table 6, for which the output was constrained, in agreement with ESIM results, to the

given Q levels (expressed as percentage changes in Table 6). 14

An additional demand system, described in more detail in section 3.5, was included to

allow assessing different financing schemes for the promotion of biofuels. The demand

system was used to capture the consumers' substitution behaviour subject to

consumption losses due to increased direct taxation aimed to compensate the price

disadvantage of biofuels and to different price impacts ensuing from the demand shocks

related to biofuels production. In fact, increased prices of agricultural products (for all

energy and non-energy purpose) as well as reduced prices of substituted standard

products are eventually changing the private consumption basket and the disposable

income of the private consumer.

Price effects over all commodities were further calculated by means of an IO price

model. Strictly speaking, the standard price model is applicable to computing a new

vector of commodity prices arising from an exogenous factor price shock, whereas, in

the present case, the interest was to derive price impacts ensuing from the price change

not of a factor but of an intermediate input. None the less, for practical reasons it was

chosen to treat intermediate commodity price increases as if they were factor prices; to

this end the fictitious factor price change was obtained by summing together the input

coefficients of all commodities allowed to exogenously change their prices, multiplied

by their own relative exogenous price changes. The exogenous price changes that were

introduced in the price model are the following:

Agricultural commodities used as feedstock for the production of biofuels, due to

increased demand. The percentage changes are listed in Table 6.

Crude oil, due to reduced demand driven by substitution with biofuels. Crude price

drop was assumed to be 1.5% in the BAU scenario, 3% in the PRIMES scenarios

and 2.5% in the GX-LC scenario, in accordance with the energy outlook data

supplied by DG TREN.15

Fossil fuels, due to crude price drop. Oil price change was transferred to the [refined

petroleum products], [diesel] and [petrol] sectors assuming a share of ~68% crude oil

cost in the total production cost, consistent with the shares implicit in the technology

specification of the GTAP sector.

Diesel and Petrol due to mandatory blending of the more expensive biofuels. The

exogenous price shock was calculated by multiplying the scenario-dependent

blending percentage by the average relative extra production cost of biofuels

compared to oil based fuels. This extra cost is an average since two different

production technologies were considered for each fuel type (see tables 1-5). Notice

that that this extra cost is overall zero (compensated by an equal fuel tax reduction)

in the default policy case, as it envisages a full tax rebate.

Livestock. Price drops of animal fodder as a by-product of biofuel production may

be relevant. Since the GTAP classification does not have animal fodder as a separate

commodity, livestock price changes, listed in Table 6, were taken as calculated by

ESIM.

All imports, due to fuel price reduction. In accordance with the ECOTRA study

(Energy uses and COsts in TRAnsport chains), it was assumed that an average 1% of

the price of imports is transport fuel cost. Further differentiation of transport and fuel

costs for different imported commodities was not attempted as the contribution of

this aspect overall on the overall impacts of the biofuels policy interventions

considered turned out to be not crucial.

The tax exemption that needs to be financed by direct taxation was then calculated as

the difference between the production cost of the biofuel and the production cost of the

conventional fuel it replaces, multiplied by the replaced fuel quantities and summed up

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for all biofuels. This amount was subtracted from the disposable income (aggregated

consumption vector).

The consumption model, set up as an Almost Ideal Demand System (AIDS) model was

run having as input data the new (reduced) household budget and the new vector of

prices. Elasticities of the AIDS model were adapted from the GTAP model elasticities,

as described in section 3.5. Output of the AIDS model was a new (reduced)

consumption vector at market prices including also imported goods. Using GTAP

shares, the final consumption vector at basic prices for domestic goods was generated

and used as input for running the mixed endogenous-exogenous variables IO model,

from which a new vector of sectoral outputs was obtained. Although some sectors

experience an output increase, the aggregated production of the overall economy as a

consequence of promoting biofuels diminishes, since the deliberate substitution of an

input by a more expensive one entails inefficiency. Following these considerations,

before computing the new employment vector, the reduction of government income

from ad-valorem and production taxes as a consequence of reduced sectoral output was

calculated. Then, to ensure government revenue neutrality, it was assumed that direct

taxation be again increased and the households' disposable income accordingly again

decreased. With this new budget constraint the model was looped again on the demand

system/ core IO modules. This constitutes the 2nd round effect on employment. Since

this effect is relatively small (~10% of the first round effect), no further rounds were

considered.

3.5. The Demand System

Private consumption by commodity was modelled in a two stage nested model. The

first (aggregate) level of commodities comprised six goods: 1 agriculture (incl. primary

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goods), 2 food and beverages, 3 textiles and clothing, 4 fuels for transportation, 5 other

commodities and 6 services. The allocation of total private consumption to these six

broad groups was described by an Almost Ideal Demand System (AIDS) model. The

AIDS model has been proposed by Deaton and Muellbauer (1980) and has been used

intensively in disaggregated econometric models, especially in macroeconometric IO

models. The AIDS model can be described as a flexible functional form that does not

impose any a priori restrictions on income and price elasticities.

During the last decade some restrictive properties of the AIDS, especially when applied

for long run projections, have been discussed. The main issue is the violation of

regularity in the sense that with large price and income changes the budget shares come

to lie outside the [0, 1] interval. The discussion led to the proposition of modified AIDS

(see: Cooper, McLaren, 1992 and 1996) among which the "An Implicitly Direct

Additive Demand System" (AIDADS) proposed by Rimmer and Powell (1996). A

recent paper by Reimer, Hertel and Yu et al. (2002) has implemented this system

empirically for a large number of countries in order to integrate it into the GTAP model

(Hertel, Tsigas, 1997). The empirical results show that income elasticities derived from

AIDADS are significantly different from those derived from AIDS. These results have

been used for modelling private consumption in this study and have been transferred to

the AIDS model with a view to making use of the important and desirable properties of

the AIDADS model, which lead to elasticity values different from AIDS. On the other

hand, the AIDS model was used for the sake of handling simplicity. This modelling

strategy should give more plausible results in simulations than directly using elasticities

from an AIDS model. For simulations incurring large income or price changes the

properties of AIDADS would again be lost due to the use of an AIDS model.

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Yu et al. (2002) provide results for income elasticities in 1995 for EU 15, which were

used here to calibrate the AIDS model. Table 7 shows the (unweighed) average of those

elasticities. Note that the income elasticity for services was adjusted and slightly

deviates from the results of Yu et al. (2002), since the respective income parameters

cannot be chosen freely due to the algebraic restrictions of the AIDS model (additivity).

These elasticity values were used according to the income elasticity formula in AIDS

(Green, Alston, 1990) to calculate the respective income parameters.

Unfortunately, the study by Yu et al. (2002) does report own and cross price elasticities

in AIDADS. For those values we recurred to results of an AIDS model for the EU 15

countries with special emphasis on energy and fuel consumption, which is described in

Kratena, Wueger (2004) and Kratena, Wueger, and Zakarias (2004). The starting point

were the values derived from these studies for the own price elasticity for non-energy

commodities (-0.73) and the own price elasticity for fuels (-0.13) and for services (-

0.48). Then, as a representation of the agriculture and food sectors, we explicitly

assumed that food is substitute for agricultural products, and that these two commodity

groups behave as substitutes for the broad category “Other Commodities” and are

complementary to the "Services" group. On the basis of these assumptions together with

the restrictions of the AIDS model concerning the price parameters (especially

symmetry and homogeneity), we derived all the elasticity values shown in Table 8

through a calibration-optimization procedure based on the own- and cross price

elasticity formulae for the AIDS model (Green, Alston, 1990). The last step consisted in

calibrating the equations to the base year data of the input-output model (2001).

Table 7

Table 8

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4. RESULTS AND DISCUSSION

This section reports on the results in terms of net thousand people employed expressed

as full time job equivalents, in the four biofuels penetration scenarios as compared to

the reference hypothetical case in which no substitution of fossil fuels by biofuels took

place. The analysis conducted on behalf of the EC for the Biofules Progress Report and

for the Renewable Energy Roadmap was restricted to the policy option envisaging a tax

exemption equivalent to the cost disadvantage of biofuels; additional results are none

the less introduced here, related to the alternative policy option of a mandatory blending

obligation, in which case the fuel prices at the filling station would increase as the extra

cost is transferred to the consumer (rather than being borne by the taxpayer).

Although all analytical steps were conducted at the level of a 64 sector classification

(see section 3.2), for the sake of handiness the results are presented at a higher level of

aggregation that allows putting in evidence the most important factors in the build-up

of total net employment effects. The displayed aggregation level contains the following

8 macro-sectors: 1. Agriculture; 2. Energy (including Electricity and the coal, oil and

gas sectors); 3. Food (including the vegetable oils sector, as mentioned in Section 3.3);

4. Industry (including the novel [Biofuel Technologies] sector); 5. Services; 6.

Transportation; 7. Fuels (refined petroleum products including Petrol and Diesel); 8.

Biofuels.

In particular, the interest was to single out to the following components in the overall

results:

1. Reduction in conventional fuel sectors employment

2. Increase in biofuels sectors employment

20

3. Generation of employment in the biofuels technologies sector, both for EU

biofuels production and for exports; in the aggregated results presented in tables

9 and 10, this is included in the Industry sector.

4. Increase in agricultural sectors employment

5. Overall decrease of production (and related employment) due to reduced

household disposable income, in the case of the default policy case that

envisages compensating for the biofuels price disadvantage with a full tax rebate

to be financed by increased direct taxation.

6. Effects of price changes and ensuing changes in consumers' expenditure.

Therefore, in addition to the base simulation setting, four sensitivity runs have been

conducted on all scenarios, corresponding to the following assumptions:

Sensitivity run S1: total results without exports of biofuels technologies.

Sensitivity run S2: total results without crude oil price effects. This parameter

was considered particularly uncertain, as exact predictions of the consequences of

biofuels substitution in the EU on the highly oligopolistic world oil market cannot

be credible.

Sensitivity run S3: total results without considering any price changes (except, in

the case of the mandatory blending obligation policy option, the price of petrol and

diesel). This sensitivity case put in evidence the magnitude of the price effects for

the sake of transparency, as the representation of the price transmission

mechanisms in the IO price model bears significant approximations.

Sensitivity run S4: total results with vegetable oil price increase locked to the

lower level experienced by oil seeds. This sensitivity case is examined since the

21

agricultural simulation model calculates price changes that can be as high as

threefold increases for vegetable oils. Such high price changes, in part originating

in ESIM from insufficient oilseed crushing capacity in the EU, may be considered

to be unrealistic.

Table 10 shows, for the default policy case (subsidised biofuels), sectoral results

aggregated to the 8 macro sectors for the base simulation case as well as total variations

for the different sensitivity runs; table 11 reports the same figures for the alternative

policy case in which non-subsidised mandatory biofuels blending obligations cause fuel

prices to increase. Table 9 summarises, for the four scenarios, the total direct cost of

policy (default case: total tax exemption to be financed by increasing direct taxes) and

the percentage price increase of petrol and diesel due to mandatory blending of more

expensive biofuels (alternative policy case; price increases calculated from blending

shares as from Table 1 and production cost differentials as from Tables 2-5).

Table 9

The first conclusion one may draw from Tables 10 and 11 is that overall employment

effects, resulting from the balance of positive and negative contributions in different

sectors and due to different factors describing the scenarios considered, are calculated to

be modest in all cases, as they are in the range +/- 200,000 against a base of 250 million

jobs in the EU25 in the year 2001. Depending on the scenario, on the financing scheme

and on the conditions introduced by the sensitivity runs, the net effects switch sign form

slightly positive from slightly negative. The predominance is however for slightly

positive net figures not only for moderate biofuels penetration scenarios (BAU, 6.9%

replacement share) but also for the scenarios assuming a high substitution rate (up to

15.2 %). On the basis of no expected economic damage to the EU, these results

22

supported the EC in proposing, in the Renewable Energy Roadmap, the mandatory

target of 10% biofuels substitution in 2020.

One should however not forget that, as outlined in the introduction of this paper, this

modelling exercise is subject to a relatively high number of approximations, first of all

that the description of the EU economy was not meant as a representation of the EU in

the year 2020; the absolute numbers should therefore be looked at against a significant

margin of uncertainty. Indeed, assuming biofuels production costs higher than those

reported in Tables 2 to 5 (but still in the credible range) would be enough to flip the

sign of the net average results to the modestly negative range. The main message should

then be understood as the expected neutrality of overall employment effects of biofuels

substitution policies examined up to a substitution rate of 15.2%.

However, one may draw some further conclusions in addition to the quasi-neutrality of

net employment and GDP effects in the explored scenarios. The sensitivity runs

indicate in fact that crude oil price reductions of the range considered would be able to

overcompensate the negative effects due to the price increase of agricultural

commodities, as one can conclude from comparing the Base, S2 and S3 runs. The S1

runs indicate also that the positioning of European firms in the world market for

biofuels technologies is a factor to be taken into account for the overall attractiveness of

the explored policy scenarios. Furthermore, the S4 results point at the relative high

magnitude of the impacts on the price of vegetable oils and at possibly unforeseen

consequences of the policy. This may be compared for instance to the recent debate

sparked by the strong price increase experienced by maize corn in the US since the

introduction of bioethanol promotion policies, and the adverse consequences that it is

having on food security in Mexico (see for instance: The Associated Press, 2007).

23

As regards the comparative analysis of the two different financing schemes

(subsidisation vs. blending obligation, Table 10 vs. Table 11), the first and foremost

observation is that the results do not differ much. Taking into consideration the

ubiquitous penetration of fuels use in all sectors of the economy, this should not be seen

as surprising. A whole different issue is represented by welfare and social equity

considerations, which may indeed make the impacts of the two options much more

different. This could be an interesting case for further work, which would require

disaggregation of different household types and calculation of additional variables

related to welfare.

Table 10

Table 11

The overall net employment results are the balance between the following components:

Positive effects in the agriculture and (in some cases) food sectors, with those in the

food sector being mainly due to the inclusion of vegetable oils;

Positive effects in the industry sector, mainly due to the high capital intensity of

biofuels production, in particular for second generation processes;

Positive effects in the biofuels industry;

Losses in the refinery sector, due to substitution by biofuels;

Losses in the energy and transportation sector;

The largest absolute employment losses are finally in the service sectors. This can be

explained mainly by: a) absence of significant specific direct employment gains in

the service sectors; b) largest overall employment base in the service sectors.

24

Indeed, one particular caveat holds for the specific impacts in the services and

transportation sectors, and in particular for the relative magnitude of those impacts in

the two alternative policy cases (Tables 10 and 11). One would in fact expect that, in

the non-subsidised case where fuel prices increase, the impacts in the comparatively

less fuel-intensive services sector would be relatively less negative than in the

subsidised case, and that the impacts in the highly fuel-intensive transportation sector

would be more negative. This is not observed. The reason is the sectoral aggregation of

the AIDS model, where transportation is grouped together with services. This is

unfortunate, since the grouping of the most fuel intensive sector with the least fuel

intensive sector severely impairs the opportunity to conduct further comparative

analysis on the two policy financing schemes, for instance in regard to possible

additional benefits in terms of fuel savings brought about by the non-subsidised

mandatory blending option. Alas, elasticity parameters for a sectoral breakdown

featuring the transportation sector on its own were not available, to our knowledge, at

the time this study was carried out.

5. CONCLUSIONS

This paper is based on a study conducted at the European Commission's Joint Research

Centre, Institute for Prospective Technological Studies, in support of the drafting of the

EC's Biofules Progress Report and Renewable Energy Roadmap. In conformity with the

Impact Assessment obligations applying to all items included in the Commission's

Annual Work Programme, the EC was in fact interested to enlarge the knowledge basis

on the socio-economic consequences entailed by a predefined set of policy-technology

scenarios, to ensure that the proposed policy, in achieving the main goals in terms of

carbon savings and security of energy supply would not be unduly detrimental to the

EU economy at large.25

To this end, an input-output based model was built, that combined scenario information

derived from energy systems simulations, process chain data, detailed simulations of the

impacts of the feedstock demand on the agricultural markets, and a demand system that

was used to capture the consumers' behavioural responses to the expected budget and

price shocks.

The results indicate that policies effective to promote the use of biofuels in the EU25 up

to a substitution share of some 15% would not cause adverse employment effects under

the adopted assumptions of having sufficiently mature biofuels production technologies

at disposal. In the build-up of the approximately neutral net employment effects, several

sectoral and causal chain effects interact to compensate inefficiency losses with gains.

Particularly important factors that show the potential to yield positive contributions are

the development of a strong EU industry in the world market for biofuels technologies

and the possible impacts in terms of moderating world oil price through reduction of

demand. Finally, the results do not indicate major differences of net employment

impacts in two alternative policy cases envisaging either subsidising the cost

disadvantage of biofuels through increased direct taxation or mandating a minimum

biofuels blending share, in which case the fuel price at filling station would reflect the

additional production cost.

26

REFERENCES

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2003/30/EC OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 8

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transport

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COMMUNICATION FROM THE COMMISSION TO THE COUNCIL AND THE

EUROPEAN PARLIAMENT: Biofuels Progress Report, Report on the progress

made in the use of biofuels and other renewable fuels in the Member States of the

European Union. Brussels, 10.1.2007, COM(2006) 845

Commission of the European Communities, Renewable Energy Roadmap:

COMMUNICATION FROM THE COMMISSION TO THE COUNCIL AND THE

EUROPEAN PARLIAMENT: Renewable Energy Road Map, Renewable energies

in the 21st century: building a more sustainable future, Brussels, 10.1.2007,

COM(2006) 848

Choumert, F., Paltsev, S. and Reilly, J. (2006), Improving the Refining Sector in EPPA,

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9, July 2006

Miller & Blair, Input-output analysis: foundations and extensions, 1985 Prentice-

Hall ISBN 0-13-466715-8, chapter 9-3

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Capros, P. and Mantzos, L, The European energy outlook to 2010 and 2030,

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Huber et al. (2004): Action plan for deriving dynamic RES-E policies and Green-X

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Banse, M., Grethe, H. and Nolte, S. (2005): European Simulation Model (ESIM) in

GAMS: User Handbook

Cooper, R. J., McLaren, K. R. (1992), An Empirically Oriented Demand System with

Improved Regularity Properties, Canadian Journal of Economics, 25, 652 - 667.

Cooper, R. J., McLaren, K. R. (1996), A System of Demand Equations satisfying

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78, 359 -364.

Deaton, A. and J. Muellbauer (1980), An Almost Ideal Demand System. American

Economic Review, 70 (1980), 312 – 326.

Green, R. D. and J. M. Alston (1990), Elasticities in AIDS Models. American Journal

of Agricultural Economics, 72 (1990), 442 – 445.

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Hertel T.W. and M.E. Tsigas (1997) Structure of GTAP, in: T.W. Hertel (ed.), Global

Trade Analysis: Modeling and Applications, Cambridge University Press, 1997.

Kratena, K., Wueger, M. and G. Zakarias (2004), Regularity and Long-Run

Dynamics in Consumer Demand Systems, WIFO Working Paper 217, 2004.

Kratena, K., Wueger, M. (2004), A Consumers' Demand Model with 'Energy Flows'

Stocks and 'Energy Services', WIFO Working Paper 237, 2004.

Reimer, J., Hertel, T.W. International Cross Section Estimates of Demand for Use

in the GTAP Model, GTAP Technical Paper No. 23, GTAP (Global Trade Analysis

Project)

Rimmer, M. T., Powell, A. (1996), An Implicitly Additive Demand System, Applied

Economics,28, 1613 - 1622.

Yu, W., Hertel, T.W., Preckel, P.V. and J.S. Eales (2002), Projecting World Food

Demand Systems, GTAP (Global Trade Analysis Project, November 29, 2002.

The Associated Press. Mexican president signs accord to contain soaring tortilla

crises. January 18, 2007

29

Captions to Figures and Tables:

Table 1: Key parameters for the 4 examined biofuels policy scenarios

Table 2: Parameters used for production technology specification of 1st generation

biodiesel. All values in €/ ton oil equivalent except shaded fields

Table 3: Parameters used for production technology specification of 1st generation

bioethanol. All values in €/ ton oil equivalent except shaded fields

Table 4: Parameters used for production technology specification of 2nd generation

biodiesel. All values in €/ ton oil equivalent except shaded fields

Table 5: Parameters used for production technology specification of 2nd generation

bioethanol. All values in €/ ton oil equivalent except shaded fields

Table 6: Percentage quantity (Q columns) and price (P columns) changes under

different biofuels policy scenarios in the EU25 for key agricultural and food

commodities (values for GTAP sectors obtained from ESIM simulations)

Table 7: Average income and price elasticities of consumers' demand for EU 15

Table 8: Average cross price elasticities of consumers' demand for EU 15

Table 9: Direct annual policy cost in 2020 for the four biofuels penetration scenarios

(fuel tax exemption policy) and alternative percentage price increase of fuels

(mandatory blending obligation)

Table 10: Aggregated results for the four scenarios and five sensitivity cases, default

policy option (subsidised biofuels cost disadvantage)

30

Table 11: Aggregated results for the four scenarios and five sensitivity cases, alternative

policy option (non-subsidised mandatory biofuels blending obligation)

Figure 1: Schematic block diagram of the overall modelling setup, showing links of

data inputs to the key modelling steps

31

Table1:

Scenario definition BAU PRIMES G1 PRIMES G2 GX-LC

biofuel % share in total fuels consumption 6.9 15.2 15.2 12.3Total biofuel consumption in Mtoe/yr 23.0 47.0 47.0 38.0total import share in % 0.0 2.7 0.0 43.9price of imported biofuels in €/toe 717 746 755 7171st gen-to-2nd gen share of total consumption in %

80.0 66.7 33.3 46.0

petrol-to-diesel share in % 45.0 45.0 45.0 45.0oil price in USdollar/bbl 48.0 48.0 48.0 48.0total reduction of crude oil import in Mtoe/yr 25.1 51.2 51.2 41.4

price of imported crude oil in €/toe 308 308 308 308energy tax included in petrol consumer prices in €/toe

666 666 666 666

energy tax included in diesel consumer prices in €/toe

460 460 460 460

Total consumption in Mtoe/yr 10.1 17.2 8.6 2.2EU domestic production share of total EU consumption in %

100.0 100.0 100.0 100.0

Total consumption in Mtoe/yr 8.3 14.1 7.1 19.3EU domestic production share of total EU consumption in %

100.0 91.0 100.0 13.5

Total consumption in Mtoe/yr 2.5 8.6 17.2 9.6EU domestic production share of total EU consumption in %

100.0 100.0 100.0 100.0

Total consumption in Mtoe/yr 2.1 7.1 14.1 7.9EU domestic production share of total EU consumption in %

100.0 100.0 100.0 100.0

Demand in year 2020 for EU biofuels technologies from RoW in M€/yr

1250.4 1417.6 1556.7 1440.0Biofuel Technologies: Export Opportunities

General Petrol and Diesel consumption

1st generation biodiesel

All Biofuels

1st generation bioethanol

2nd generation FT-diesel

2nd generation bioethanol

32

Table2:

Scenario: BAU PRIMES G1 PRIMES G2 GX-LCoil seed price in €/t 251.2 275.4 249.0 246.3domestic share of oil seed supply in % 45.2 38.8 46.8 51.3total oil seed cost 739.5 808.1 733.6 727.3domestic oil seed cost 334.3 313.5 343.3 373.1imported oil seed cost 405.2 494.5 390.3 354.2alcohol (MeOH) cost 29.2 29.2 29.2 29.2capital costs (biofuels technologies) 23.5 23.5 23.5 23.5annual debt service 18.8 18.8 18.8 18.8labour cost at production plant 8.5 8.5 8.5 8.5other fixed operating cost 2.4 2.4 2.4 2.4electricity consumption 10.0 10.0 10.0 10.0chemicals consumption 1.3 1.3 1.3 1.3natural gas consumption 23.8 23.8 23.8 23.8credit for cake sale -145.7 -145.7 -145.7 -145.7credit for glycerine sale -3.1 -3.1 -3.1 -3.1total production cost 708.1 776.7 702.2 696.0differential cost biodiesel - pet. diesel 310.4 378.9 304.5 298.2

Inputs to EU production of 1st gen. biodiesel, €/toe

33

Table3:

Scenario: BAU PRIMES G1 PRIMES G2 GX-LCwheat-based ethanol share in total domestic production in %

79.0 80.0 78.0 0.0

wheat corn price in €/t 124.5 130.5 132.0 124.5sugar beet mix price in €/t 29.0 29.6 29.0 29.0EU share of wheat corn supply in % 100.0 100.0 100.0 100.0EU share of sugar beet supply in % 100.0 100.0 100.0 100.0total wheat corn/sugar beet cost 641.2 669.3 671.5 598.7total domestic wheat corn cost 506.6 535.5 523.8 0.0total domestic sugar beet cost 134.7 133.9 147.7 598.7total imported wheat/sugar beet cost 0.0 0.0 0.0 0.0capital costs (biofuels technologies) 81.7 81.8 81.5 66.8annual debt service 64.6 64.8 64.4 49.9labour cost at production plant 27.3 27.4 27.3 22.2other fixed operating cost 8.8 8.9 8.8 5.4electricity consumption -113.7 -115.3 -112.1 13.8diesel consumption 14.9 15.1 14.7 0.0natural gas consumption 146.4 147.3 145.4 74.5credit for cake sale -128.0 -128.5 -127.5 -88.1total production cost 743.3 770.8 774.1 743.2differential cost bioethanol - petrol 345.5 373.1 376.3 345.5

Inputs to EU production of 1st gen. bioethanol, €/toe

34

Table 4:

Scenario: BAU PRIMES G1 PRIMES G2 GX-LClearning effect cost reduction in % 3.7 12.5 25.0 14.0farmed wood price in €/t 67.5 67.5 67.5 67.5straw price in €/t 51.3 51.3 51.3 51.3% of wood in EU biomass supply 0.0 54.1 75.9 58.5% of straw in EU biomass supply 100.0 45.9 24.1 41.5EU share of feedstock supply in % 85.0 85.0 85.0 85.0total biomass cost 256.8 292.4 306.8 295.3total domestic farmed wood cost 0.0 134.4 198.0 146.8total domestic straw cost 218.3 114.1 62.7 104.2total imported wood cost 38.5 43.9 46.0 44.3capital costs (biofuels technologies) 264.6 240.3 205.9 236.3annual debt service 211.5 219.6 164.6 188.9labour cost at production plant 134.8 122.4 140.0 120.4other fixed operating cost 44.1 40.0 34.3 39.4chemicals consumption 44.9 44.9 44.9 44.9total production cost 956.7 959.6 896.4 925.1differential cost BTL - pet. diesel 559.0 561.9 498.6 527.3

Inputs to EU production of 2nd gen. BTL, €/toe

35

Table5:

Scenario: BAU PRIMES G1 PRIMES G2 GX-LClearning effect cost reduction in % 3.7 12.5 25.0 14.0farmed wood based ethanol share in total domestic production in %

0.0 54.1 75.9 58.5

farmed wood price in €/t 67.5 67.5 67.5 67.5straw price in €/t 51.3 51.3 51.3 51.3% of wood in EU biomass supply 0.0 54.1 75.9 58.5% of straw in EU biomass supply 100.0 45.9 24.1 41.5EU share of feedstock supply in % 85.0 85.0 85.0 85.0total feedstock cost 373.0 421.4 441.0 425.4total domestic farmed wood cost 0.0 212.7 298.6 230.0total domestic straw cost 303.6 139.4 73.1 126.0total imported wood cost 69.4 69.4 69.4 69.4capital costs (biofuels technologies) 192.5 168.7 142.5 165.4annual debt service 153.5 134.5 113.6 131.9labour cost at production plant 42.5 37.0 31.2 36.3other fixed operating cost 146.5 110.6 87.1 107.0electricity production credit -42.4 -63.4 -71.9 -65.1total production cost 865.6 808.9 743.4 800.8differential cost bioethanol - petrol 467.8 411.2 345.7 403.0

Inputs to EU production of 2nd gen. bioethanol, €/toe

36

Table6:

Commodity Q P Q P Q P Q PRice 0 0 0 -7 1 -7 0 -7Wheat 2 7 2 17 -2.5 16 0 9Other cereals 2 5 2 9 -2.5 13 0 5Oil Seeds 26 30 42 39 19 25 19 19Sugar crops 26 30 43 40 23 28 0 0Bovine Cattle 2 -7 3 -9 1 -6 2 -7Other Animal Products 0 -5 0 -7 0 -5 0 -5Vegetable Oils & Fats 7 9.48 12 18.22 7 5.8 3 2.8

BAU PRIMES G1 PRIMES G2 GRX-LC

37

Table7:

income own priceelasticities elasticities

Agriculture 0.414 -0,73Food, Beverages 0.610 -0,73

Textiles 0.760 -0,73Fuels 0.964 -0,13

Other Commodities 0.869 -0,73Services 1.224 -0,35

38

Table8:

  Agriculture Food Textiles Fuels Other Services

Agriculture -0.73 0.43 0.10 -0.10 0.79 -0.90

Food, Beverages 0.10 -0.73 -0.13 0.41 0.64 -0.90

Textiles 0.05 -0.34 -0.73 0.18 0.90 -0.81

Fuels -0.07 0.90 0.15 -0.13 -0.10 -0.72

Other Commodities 0.11 0.39 0.22 -0.02 -0.73 -0.84

Services -0.06 -0.21 -0.07 -0.06 -0.27 -0.35

39

Table9:

Tax exemptionDirect Policy Cost Diesel % price increase Petrol % price increase

BAU 8.4 Billion € 5.9 6.4PRIMES G1 19.5 Billion € 12.7 13.6PRIMES G2 18.7 Billion € 15.2 12.8GX-LC 15.6 Billion € 10.2 9.6

Mandatory Blending ObligationScenario

40

Table10:

macro sectors BAU PRIMES G1 PRIMES G2 GX-LCAGRI 112.505 199.173 154.674 96.821ENERGY -5.568 -19.823 -27.606 -19.978FOOD -18.212 -57.050 -58.175 -46.496INDUS 19.598 32.658 59.147 43.719SERV -107.661 -235.369 -39.395 24.410TRANSP -13.128 -22.545 -21.387 -4.677FUELS -10.528 -21.627 -22.626 -16.782BIOFUELS 16.925 40.302 69.449 34.390TOT BASE -6.070 -84.282 114.081 111.408TOT S1 -35.464 -117.271 78.238 62.926TOT S2 -54.248 -186.367 8.880 28.348TOT S3 49.522 6.490 20.097 -56.948TOT S4 114.408 169.685 179.166 134.989

AGGREGATED RESULTS ('000 full time job equivalents):

41

Table11:

macro sectors BAU PRI1 PRI2 GR-XAGRI 141.858 237.864 140.097 105.993ENERGY -11.020 -31.432 -38.668 -29.137FOOD 82.612 151.033 136.476 96.450INDUS 52.219 96.296 118.911 88.985SERV -146.797 -340.780 -216.751 -118.020TRANSP -10.848 -19.636 -23.605 -7.230FUELS -17.482 -34.830 -35.335 -26.884BIOFUELS 16.126 37.352 65.659 32.914TOT BASE 106.668 95.867 146.784 143.071TOT S1 78.280 62.893 110.960 94.612TOT S2 61.446 5.412 54.333 67.295TOT S3 87.273 48.144 16.585 -43.251TOT S4 149.434 201.991 164.463 143.071

AGGREGATED RESULTS ('000 full time job equivalents):

42

Figure 1:

GTAP 6: IO Tables for 25 EU countries

OECD STAN, FADN and own estimates

Aggregate IO Table for EU25, 57 sectors

Add Employment satellite accounts

Scenario data: Q and P for biofuels and agri

Add 4 biofuels sectors at 1 MTOE output each

Price model: new vector of prices

Data sources Energy statistics: IEA, GTAP and MIT

Process Chain Data, WTW study

Add petrol and diesel sectors to IOT

Calculation of household budget loss

New IOT including fuel substitutions

AIDS model: new final demand vector

IO core model: direct & indirect employment

Second round effect: Recollect reduced Gov. Tax revenue

After second round: final results for employment impacts

43