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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
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
2
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-
3
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.
4
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.
5
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,
7
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
8
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
9
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
11
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
12
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
13
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
16
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
17
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.
18
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
19
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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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
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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
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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.
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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