Trading schemes for greenhouse gas emissions from Europ ean agricultlrre : A comparative analysis based on different implementation options

Sammarlt A rationa/ negatiaîian strate3l f0r canting trultilateral negatiatians regarding clnnate change requires kiauleclge abaut passiltle soriol, erono,iii ond eutironntental effects of policy insTrnnenTs fr the ahatutunt af greenbrnse gas entissions. \Yith this Pilrl)lse, a entirian.r fran agrimhural saarcu in Eurctpe and .ancenîrdtet un the ap!/icatictn of a purnit trade tonuuitnrenl' ha:eline. The fficts deriud of thræ altet sharing' agreenunt lpti0n rtned os regional unissi enu$nn ch


Introduction
In October 2003 the EU adopted a proposal for a directive on CO2 emission trading to be operable by January 2005 (Council of the European Union, 200r, establishing a coordinated GHG (Greenhouse Gas) emission allowances Trading Scheme (ETS) over all Member-States (MS).Applying to a list of energy and industrial production activities covering all GHGs included in Annex A of the Kyoto Protocol (KP), the is an important point with regard to the potential extension to rhe agricultural sector.
The possible inclusion of agriculture in a carbon-baeed ETS is a controversial issue.Saddler and King (2008) highlight the current debate in Australia and stress the need to include incentives to adopt best-practice methods of emission abatement in the grounds, he justifies the introduction of Ilsh agriculture in a ETS, since methane and I The list of activities included in the Direcrive might be subjecr to furure revision.I pérezDonringtez,\V Brttz K Holn-trliillq-Rniu,af Agrictltara/antlEnaironmentalSludies 90O)'287a08 findings and draws some reflections on the relevance of this study for the European decision making process.2. Modelling emission trading in CAPRI 2.1.Overview of the CAPRI Model n Agricultural Policy Regional Impact agricultural sector model with a focus on t covering global trade with agricultural Developed since 1996, it is now used by iuropean Commission, for policy impact aesessment.From a technical Pefspective, CAPRI is split into two maior modules' The supply module consisrs of about 250 independent ^aggregate optimisation models t.ptËt.ntingall regional agricultural activities as defined by the-Economic Accounts ior Agriciltur., .Lh modil representing the aggregate choices of farmers in a Nuts 2 regio,"n (EuroStat regional claisification).These supply models combine a Leontief tech"nology for interm'ediate inputs covering a low and high yreld variant for the different prodîction activities with a non-linear cost function which captures the effects of labour and capital on farmers' decisions.This is combined with constraints relating to land availability, animal requirements, crop nutrient needs and policy ,.rtri.tlons (production quoiu, and set-aeide restriction).The non-linear cost function allows for p.ife.t calibration of the models and a smooth observed bihaviour 2. The supply models feature a high activities (28 crop and 13 animal activities), capture the p in high detail and use an expected utility approach from the EU sugar quota regime.
The market module consists of commodity model for 40 primary and 40 countries or country blocks in 18 tradrn :ï,ï ï:ffi ,ff "y; lL:ï1,îl:,1''*''' As the supply models are solved independently at fixed prices, the link between the supply Àà'market modules is baeed on an iterative procedure.After each iteration, during which the supply module works with fixed prices, the constant terms of the behavioriral functionr fot supply and feed demand of the market module are :ï:i ::iliil ï"'ffi ,TÏÎ,"lJfff'îl:i: i] lasticiries are used, which are ae far ae possible I PérezDoningtez.tX/,Brir;.K Holnrtrliiller-ReriruofAgrin/tmalandEnt,iromrcntal Stillic.t.90(3).287-J0gcalibrated to the results of the regional aggregate programming models aggregared ro MS level.S-olvrng the market module then deLvers new prices.A weighted aùrage of the prices from past iterations then defines the prices used in the nexi rteration o"f the supply module.Equally, in between iterations, cAp premiums, differentiated according to the different decoupling schemes adopted by MS, are re<alculated to ensure compliance with national ceilings The specific srrucrure of GAPRI renders it especially surtable for agà- environmental analysis.The regionalized programming models .uprur.Lnks betw"een agriculrural production activities in detail, and allow, based on rhè differentiated lists of production activities, inputs and outputs, to define environmental effects of agriculture in response^to changes in the policy or market environment.They allow for the integration of different rypes of environmental policy instrumenrs (pollurion
The methodological approach proposed in this paper builds on features of the last two groups.on the one hand, the emission tradrng scheme analysed in the paper includes different GHGs calculated from a bottom-up p.rrp..iiu.for differànt agricultural processes (e,g.production activities), based -on spècific interactions of agriculture technological chorce (e,g.currenr emenr, ferti practices).\Whereas energy und re widely u rrade of GHG emissions Tiou agricultural also play an important role here, especially linked to the potential extension of existing emission rruding schemes to this sector (opt-in clause, article 30 of rhe Kp). on rhe other hand, the methodoiogy presented lftli.lan explicit emission trading scheme based on marginal abarement cosr curves (MACC) Inked to regional emission constraints.Srmilar approaches can be found in Holtsmark and Maestad (2002, p 208), Jotzo and Michaeiowa (2002, pp. 1g2-lg3), Lôschel and zhang (2002, p.120), srevens and Rose (2002), Jorgenson et at.(2009', p 366).In this type of models, emitters interact with each o,h.i in order to find a market clearing point where prices for permits are equal to marginal abarement costs.
are also included in the decision-taking process.
The new module consisrs of two eleme nts.Firstly, a new constraint in the supply model defines GHG emissions from agriculture at regional level accordinél to the 4 It is considered ùalisric ro assume lower transaccion costs in the first case since crade between emitcers 'within a country' is comparably cheaper in rerms of the administrative burden Permits Supply Feed Demand 29r Prices Global \_=__.-l.Pérez Dominguez, \{/.Britz, K. Holm-Nijller -Rnieu, ol'Agrinltaral and Enaironntental Sndies.90 (3), 2g7_30t1 UNFCCC 5 emission accounring scheme.By setting an upper bound on GHG emissions, effects of a standard or permit distribution fo; GHG emissions on agricultural supply and intermediate demand at regional level can be simulated.The related marginal abatement cosrs are derived ae the shadow prices of the constraint.
These abatement costs enter in each iteration the second element: the newly developed permit trade module.Baeed on a second order approximation of rhe marginal abatement cost curve, it deûnes market clearing priceJ for emission permits and their regional distribution in the 8U27.Clearly, the iterative feedback-from the slobal market module allows simulating impacts on demand, trade and prices of the em"ission ceilings, but also, impacts on the marginal abatement coscs in agriculture resulting from price changes.
In qh9 permit trading module, interregional rrade of permit allowances is simulated by maximising the rotal rent from trading under a consranr sum of regronal permits.At the market clearing point TC should accounr for the remaining d.iffer.nces in regional permit prices6, which should reflect the regional marg,nal abatement costs.For the modelled multi-regional case, the permit truding module is analytically constructed as a maximisation problem: Subject to the following restrictions: PmnttP! = d,I p, x ,+ilut,n!\7here: -)blt = surplus from emission trade -q, f = intercept and slope of rhe regional permit demand function t United Nacions Framework Convention on Climare Chanee.I PérezDomingrtz.V.Brirz.K,Hotnt-ltliiller-Rerieuof AgrinltwdlandEtrirznnrentalStildiet,90(3),287-308   -AllowPi = initial drstribution of permits (initial upper-bound imposed on emissions) -AllouPf = final distribution of permits for the region (after trading) -PernitPi = initial permit price (shadow price of the emission restriction) -PmniPf = final permit price (after trading) -Buysln = permits bought by region r from regions in the same MS -Bays)ut = permits bought by region r from regions in other MS -Salesln = permits sold by region r to regions in the same MS -Sales)ut = permits sold by region r to regions in other MS -VarTC-lnst = per unit TC linked to the pre-implementation and implementation of the scheme (institutional TC) -VarTCln = per unit TC directly linked to trade within the same MS (e.g. brokerage fees) -VarTC1ut = per unit TC directly Iinked to trade with regions in other MS (e.g. brokerage fees) e sum of the areas below the initial and the final mar by moving away from the tion AllaaP!.The area change below the permit demand functions is comprehended by the objective function and divided in two terms: (0.5*(PumttP;-PermitP/)x(AllatP/ -Allat'P;)) and (Allat,P!-AlluaP)x PennitP/ ).Variable TC are charged to the permit buyers and are subtracted from the obtained rent.
The constraints of the problem are: l.Equation (2): rhe total amount of permits allocated to a region in the market hae to be equal to the initial allocation plus purchases and sales, inside and outside of the MS.
2. Equation (3): total permit sales from regions in other MS has to be equal to total permit purchases from other MS (international permit trade balance).
). Equation (4: total permit sales and permit purchaees between regions in the same MS have to be equal (national permit trade balance).
4. Equation (J): the initial regional permit price lays on rhe permit demand function and is defined through the intercept, the slope and the initial permits allocated to the region.5, Equation ( 6): the permit demand function has to pass through the simulated regional permit price, which is defined through the intercept, the slope and the new amount of permits allocated to the region.

This approach is
Agricultural producers d given by the individual attached to the emission emission allowances must lead to income gains or at least to no change in income in each region compared to a no-trade situation.
)91 edder t: through linear permir demand fiinctions, designed to go in each iterarion through the initial regional permit pûce (PunùtP,;) which resuhs from rhe application of rhe uniform regional emission standard at the starting point and that estimared in the tnal situation (PenaitP,l).Therefore, che optimal demand for permits per region (z,e.convergence to a point of the real Marginal Abatement Cost Curve, MACC) is achieved wrthrn an iterative approach.
During the first iterarion, permit allowances are disrributed to regions based on an equal percentage reduction of the GHG emissions in the baseline, and the shadow price on the emission ceiling derived by solving the regional models deliver one point on the regional MACC to which the permit trade models need to be calibrated.
Therefore, the permit trade module works during this first iteration with a vector of assumed parameters u,. and 0r1 of the MACC.
In the second and following iterations, the information delivered by the trading module in the form of regional permit allowances (AllwP,!) is used in the regional supply modeis to update the emission cerlings and calculate an updated vecror of shadow values.rX/ith this information, intercepts and slopes for rhe linear permit demand functions can be updated, since at least two equilbrium points coming from the supply model (points on rhe real MACC) are available.This is shown in the following equation.F, = ([ti.*|-,-lli'|,-r) I (erttissiontr'!-tI uais.rion'f'!-r)._.
A,, = P'fel'-t -Br* emissi,ntkl)-r \l) The iteration process is repeated until no noticeable price changes are observed between the resLrlts delivered by the supply model and the permit trading module for a vector of permit allowances (the value of the objecrive firnction of the permit trading module is at its maximum), At thrs srage, rhe final equilibrium is achieved.

Scenario construction
3.1.Baseline CAPRI combines expert judgements and trend analysis to provide a scenario baseline, used as comparison point for counterfactual analysis.The baseline may be interprered as a projection in time covering the most probable future development of rhe European agricultural sector under the stalas qua policy and rncluding all future changes alrèady foreseen in the current legislation.Expert data on future rrends are obràrned from internationally reliable sources doing forecasting research ar EU level (Commission of the European Communities, 2005) and for non-Eu regions and exogenous drivers (FAO, 2003).This informarion and own rrend projections using time series from the currenr CAPRI database are fed into an estimator which chooses the most likely combination of forecast values subject to a larger set of consisrency restricrions (e.g.closed afea and mafket balances, feed requirements, Production quotas, set-aside restriction, composition of cattle herds).
Similar to CGEs, calibration of the non-linear programming models is based on the definition of a paramerer set fulfilling firsr-order conditions at the pre-defined baseline results, including both parameters of the non-linear costs function and technical coefficients.On rhe market side, the projection results at ErJ27 level plus Norway and lWestern Balkans are taken as given for calibration.In the calibration step bilateral import and export flows from these countries to other trade blocks are defined, as well as development of agricultural production, feed use, processing acivities and human consumprion.In this paper the CAPRI baseline comprehends all the changes foreseen in the Common Agricultural Policy until 2013.common Market organisarions lm]t:l-9::*TÎ1f:";1ing to the expert forecasts (commission ot rlle L.urr)pcan L()mmLtnlilrs! l{,{,)) Trade policy Final implementation of tlre 1!!4 Uruguay rouncl plus some fiirther elements as NAFTA.

Emission restriction scenarios
In rhis scenario block, an 8% emission reduction on GHG emissions from agriculture is analysed.This emrssion reduction target covers allEU2T MS and is projected to happen in year 2013 (end of the firsr commitment period of the Kyoto Protocol) on rop of the current legislation.In order to implement it, two policy options have been selected: -A regional homogeneous emission standard of B% wirh respecr to rhe baseline.The reduction is equal in relative terms in all European regions, and thus independent from differences in abatement costs (see column 2 of table 2).
-In order to find a more suitable solution for balancing the burden of emission abatement, an ad hoc ! e (BSAA) is proposed for the EtJ2l , MS, but are identical for regions ins a regio-nally differentiated emission ith different measure in terms of welfare is increased.

J.3. Trade of emission permits
In this scenario block, the scenario analysis is enhanced by the explicit implementation of a European market of GHG emission permits from agriculture.\7ith this purpose, informatiàn on TC 8 related to existing trading schemes is explicitly considered, since rhey are meanr to have an important effect on the economic performance of such an instrument.Similar to Jorgenson et al. ( 200), two policy implementation options based on different scopes for trading are considered here: -Unrestricted emission trading.In thrs scenario, an 8% emission reduction year with ) years of amortisation).These are also assumed to be paid by permit tuy.r, and iherefore distributed over transactions.They are defined based on 8 Transacrion cosrs are those costs that arise from initiating and completing transa(ions, such ae finding parrners, holding negotiations, consulring with lawyers or other experts.monitoring agreemencs, erc. (Coase, 1937).
) ct'7 I Pétz:Dournguez \V.Bùtz K Holn-Àliiller-Ra,icu'of AgrinlturalandEntiratnental St1cli1.90(J),2u7-308information found in rhe lirerature for clean development mechanism (cDM) and joinc implementation ÇI) projecs in different economic secrors and size of the markets (compilation by Eckermann et a\.,2003, pp.6-S;1. -Restricted emission trading.In this scenario, an B% emission reduction rarget is enforced for all regions within the EU27 but rrade is only allowed within countries.The idea is to mimic existing rrading schemes in the gLI (es different trading schemes of milk quotas).The origrnal permit distribution remains the same as in the previous case.

J.4. Model assumptions
The results provided by this emission tradrng model are linked ro some general model assumptions.First of all, full rational behaviour of regional agriculrural producers is assumed in CAPRI.\Whereas agricultural profit is maximized sLLbject to economic and agronomic consrraints, supply and market balances in an open economy have to be cleared out.Secondly, the calculation of GHG emission indicators roor in (a) the basic economic behaviour of the modei, i.e. optimal cropprng parrerns ar regional level, (/) a balanced nitrogen flow model based on explicit energy requiremenrs and deliveries per agricultural activity, and (c) a set of emission factors derived from rhe literature (IPCC,   least, TC are assumed to be additional costs for agriculrural producers and are hnked to the size of the modelled emission trading scheme, as esrimared by Prce\X/aterhouse- Coopers (2000).

Selected Results
v The reader should be arvare of the [act that the inicial ,listriburion of rhe permits has an impact on the final distriburion if transaction costs are taken into account, so rhar thË Coarse Theorem àoes nor hold.This theorem implies that all parries in a trading scheme can achieve the social oprimum if the transaction cost is low, properry rights clearly deÀned ancl enforced and everyone hae full information.
grass and fallow land), the inrensity of production and the regional cost structures as the main drivers for results.
The supply effects resulting from the reduction in GHG emissions have a sizeable impact especially on agricultural prices where (a) demand elasticities are low, (D) EU boider protection is high and market access falls under a tariff rate quota, aûd (d GHG emissions per unir of product are high.All these factors are found in beef markets, where price increases by 16% at EU level in the scenanos.This price increase is also due to rhe facr rhlr methane emissions from ruminants are an important part of the overall GHG emissions from agricuiture, leading to a drop in beef production by around -5t/o.The srrong impacts for the beef production chain are also due to the milk quota system, i.e.mllk production and dairy cow herds do not change, insread, milk quota pnces drop.
lVhereas adjustments for sheep and goat are somewhat smaller, with production falling rn the range of -3%, pork production remains almost stable and poultry production even increases due to substitution effects on the demand side.Supply of cereals also droos in the ranse of -6%: On the one hand due to a reaction of lower demand *hen heat output drops tnd, on the other hand, due to high opportunity cosrs for fertilization, The high global warming potential of nitrous oxide, emission resrrictions put a hrgher burden on production of fertilizer-intensive crops.Moreover, the reduced number of ruminants leads to an extensification effect on grasslands, with yields dropping by -1.2%,ard a reduction in silage maize producùonl>y -6%.
In map 1, the regional abatement costs attached ro the imposition of a regional homogeneous emission standard of 8% with respect to the baseline are pfesented.
\)fith a homogeneous emission standard at a regional level, the ave:m'ge marginal abatement cost for the Ell2l is 91 euros per tonne (€/t) of CO2-equivalents (CO2'a).
The regional costs vary between 30 and 270€ltCO2's.These results are consistent with the literature.Under similar assumptions, De Cara et al.( 2005) estimate for the EU15 marginal abatement cosrs of 123€ltCO2ut for an 8% emission standard on methane and nitrous oxide emissions from agriculture in year 2001.By introducing emission taxes their estimates go down to 55.8 €/tCO2'c (1.e.results comparable to an unrestricted permit trading scheme without TC, see table 3).
r)7irh a burden sharing agreemenc at a MS levei (see map 2), rhe average marginal abarement cost for the EU27 drops to 77 €ltCO2"t.The regional variation in marginal abatement cosrs is reduced with respect to the previous case, as the burden sharing was defined based on marginal abatement costs If permit trading is introduced, the average marginal cost for the abatement of a CO2eq srni55lon in the EIJ2J varies between 73 € (in the case of no TC), and 89 € in the case of intra-national trade.In table 3, the convergence of prices at MS level is presented.Increases beyond rhe burden sharing solurion are due to takrng TC of the trading scheme inro account.
This is achieved after several iterations and with the consideration of endogenous marker prices for agricultural products.Slight differences in total abatement in rhe first column of rable J, are due to a lack of full convergence between the emission ceilings implemented in the programming models and the allocation of permits generated by the permit trade module.These differences, however, were considered minimal and did not affect the results.The third column of table 3 actually shows rhe differences in marginal abatement costs amongst countries (i,e.here convergence of permrt prices is only achieved withrn the Nuts 2 regions of a MS).For instance, the EU15 presents consrderably higher margital costs than the EU10 (90€aasus 70 g, what is dampened when trade of permits is allowed (77 € aasus 73 €, see column 2, ta6le 3).If emission trading is introduced under explicit consideration of TC, the purchases and sales of permits go up to 8.2 MM tco2ec.Of this amount, around 90% of the purchases come from abroad and I0% due to trade within national borders, which is explained by the higher differences in margrnal abatement costs between different MS.
As shown in map 3, most of the regions in France, Netherlands, Denmark and Italy are a net permit buyers, whereas Eastern European regions are permit providers (due ro lower marginal abatement costs).This picture changes when permit trade is restricted and only agricultural producers within a MS are allowed to trade (see map 4).In this case only 3.2 MM tCO2e9 are traded, what also leads to a much less smooth convergence of permit prices between regions (see column 3 in table 3), along with higher average abaremenr cosrs for the EU ae a whole.
Map 2 Marginal abatement costs with a burden sharing agreement for agriculture (in thousand ÛtCO2"l1 5. Limitations of the srudv and further research There are several effects which are not covered in the current analysis worth to be menrioned.Firstly, emission abatement in our model is related striccly to agriculrural direct emissions l0 and does neither cover indirect €missions, like e,g.related to fertilizer production, nor emissions from other pollutanrs, like e.g.SO2, nor changing carbon sequestration resulting from changes in land management techniques and introduction of alternative crop rotations (as in Lal, 2004; Reilly and Asadoorian,   2007 , p. 178).Secondly, the anaiysis is restricted to agricultLrral GHG emissions in EU2l , excluding emission leakage due to changes in production in other parts of the world substiruting reduced EU production (as in Laurijssen and Faaij, 2009), Accordingly, due to our restriction to agriculture changes in the forestry or energy sectors resulting from adjustment in agricultural producion are noc consiclered (as in Bôhringer, 2000, p.780;Truong et a/., 2007).Moreover, agricultural processing activities For explicit mirigation of GHG emissions, e,g.biofuel or biogas producion (Gielen u a/.,200), pp. 179-180; Pathak et al.,2009, p.408)  Therefore, in our model emission abarement is mostly related to changes in the farm production program and not ro improved process management.The analysis hence builds on a rather simple and straightforward emission accounting scheme and nor on on-farm measurements of emissions or more elaborated emission coefficients depending on single processes (as in Moran et al., 2009).However, gtven the high numbers of agents involved, the high control and administration costs to include in Pan-European Legislation, the presented simplified approach could be more suitable for agriculture (opt-in solution for agriculture, so that farmers or groups of farmers can take part in the climate coûtrol schemes based on a rather simple accounting scheme).This is in line with Monni et al. (2007, p.530), who warn of increasing uncertainties and marginal emission reduction costs linked to a too complex extension of the current EU-wide ETS to other sectors and sases.
I PûezDon)nguez,\V.Btirz.K.Holn-AIiilb-RniruofA{rirulnra/andEnyironuentalStudies.90(3),287-108Map 4. Purchases of emission permits at the regional level with resrricred trade (trade of permits only allowed within resolurion available in the CAPRI modeling system and, cherefore, used in rhis scudy.A higher resolution in space (e.g.Nuts 3) could improve the analysis if reliable informarion would be available.However, EuroStat did not provide time series on yields and activity levels at NUTS 3 levei at the time when the model was oarameterized.
agreement already exists for the EU at economy wide-scale.The burden sharing agreement analyzed here for agriculture is based on economic efficiency solely, Le. country with low abatement costs must achieve higher reductions.In real world negotiations agreements, other aspects such as "fairness" (e.g.regarding chances for economic growth for the poorer MS) and historical events (e.g.different industrralization processes in Eaetern European countries) play an at least equally important role.
Our analysis sheds Iight on three different aspects of the GHG emission abacement debate, The first one adds an agricultural perspective ro the general discussion.It has been shown that at leasr when applying a rather simple accounting scheme, abatemenr costs in agriculture areina similar or even higher magnitude than in other sectors (70-90 fltCO2's for an B% emission reduction).
The second important aepect relates to the economic consequences of including agriculture in any GHG abatement scheme.Given tn many cases prohibitive most favourite nation (MFN) import tariffs for agricultural and food products, environmental legislation forcing large-scale reduction of agricultural production in Europe leads to sizeable price increases for agricultural products, given a ra;ther inelastic demand.An EU-wide GHG emission ceiling for agriculture, ae simulated in our analysis, acrs as an implicit supply concrol for agricultural production ae a whole.
Analogously to a quota regime, it may lead to a positive quota rent for farmers and agricultural profits could hence increase.The latter will typically come at the cost of an even higher increase in the food bill due to net economic welfare losses from the addirional marker interventions.
GHG abaremenr in agriculture via emission restrictions let production costs and food prices increase.With their high expenditure share for food, the highest relative burden will be carried by poorer households.Alternatively, abatement measures in agriculture might be financed by tax regimes, e.g. in form of support to farmers for implementing GHG saving technologies.Effeccs on households of such programs wrll depend on the direct and rndirect tax shares of household income, and probably put a higher burden on richer households.
The increaeing effect on price and farm income may be dampened at higher world prices for agricultural products observed during the so-called food crisrs in 2001 12008,  where EU border protection is no longer such a definitive factor.Equally, the EU agricultural tariffs are to a larger extent specific ones which are slowly being eroded by inflation.Even if thus probably small, the farm income increasing effect should render it attractive to include agriculture in GHG emission strategies ae long as suPporting farming income is still a major policy obfective of the CAP.Design of GHG legislation for agriculture is further eased by the fact that we deal wrth a global externality linked to many different economic activities.It can hence draw on comparing agricultural MACC to those in other sectots already operating under a trading scheme.
The third aspecr relares to implemenration issues of policy abatement measures.
\7hereae there is no doubt that market solutions are superior ro standards in a hypothetical world without implementation costs, the picture is far less clear when public and private TC come into play.For the analysis at hand, it is obvious that the

Figure l .
Figure l.CAPRI model flow with explicit consideration of regional emission permit trading for MS have been cross-checked ex posc for year 200I (calibration point of the version ofCAPRI used for rhis study) and the development ofemissions up ro yEàr 201 ] (baseline) directly tinJ<ed to exogenous drivers (see table 1) affecting activity data and emission laccors Therefore, emission invenrories in the baseline might deviate from historival trends on emlsslons.Source:CAPRI Modelling Syscem; projection to year 2013
standards/taxes and technical emission abatemenr optrons).As opposed to many other region , rhe transparenr [nk with ihe large-scale global e rest of the world (Wieck u-at., ZdO6).t specrfic market module for young animal trade ensures a plausible mix of pig and cattle activities.
Member State borders) (in thousand units) this paper an EU-wide trading scheme of GHG emission permits from agriculture is proposed.The characteristics are: (l) full coverage of EU21; (2) distribution of permits between handed out to agriculural producers free of charge and linked to historical emission records, i.e. grandfarhering; (J) regional ll emission trading only within each country, l.e.restricted trade, or also across MS borders, l.e.unrestricted trade; (4) explicit consideration of TC in trading; and (r) no enforcement penalties considered.Vith this purpose apartial equilibrium model CAPRI has been used.Moreover, in this paper a burden sharing agreemenr between the EIJ27 Members in order to meet a certain target for agricultural GHG emissions in 2013 at EU27 level is explicitly simulated, i.e. different emission reduction rares per country.Such an tl Here it is importanr to remind the reader that the Nurs 2 sparial aggregation level is rhe highest In