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Energy Strategy Reviews 1 (2012) 76e84

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Energy Strategy Reviews

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ANALYSIS

Model-based analysis of decarbonising the EU economy in the timehorizon to 2050

Pantelis Capros*, Nikolaos Tasios, Alessia De Vita, Leonidas Mantzos, Leonidas Paroussos

National Technical University of Athens, Department of Electrical and Computer Engineering, 9 Iroon Politechniou Street, 15773 Zografou Campus, Greece

A R T I C L E I N F O

Article history:

Received 25 January 2012

Received in revised form

7 June 2012

Accepted 26 June 2012

Available online 1 August 2012

Keywords:

Energy policy

Climate policy

Energy analysis

Energy modelling

* Corresponding author.

E-mail address: kapros@central.ntua.gr (P. Capros).

2211-467X/$ e see front matter � 2012 Elsevier Ltd. Al

http://dx.doi.org/10.1016/j.esr.2012.06.003

A B S T R A C T

This paper describes the methodology of using the PRIMES energy system model toquantify various scenarios accompanying the “Roadmap for moving to a competitive low-carbon economy in 2050” published in March 2011 by the European Commission. Thepaper focuses as well on emission and cost implications.The model based analysis finds that the decarbonisation of the energy system is possible

with technologies known today; the power generation sector reduces emissions the most,but also demand side sectors reduce their emissions considerably. Despite considerablerestructuring towards using electricity, transportation shows residual emissions by 2050mainly due to the long-distance road freight transport and aviation. The energy systemcosts for decarbonisation were found to represent between 0.24 and 1.63 percentagepoints of cumulative GDP over the time period 2010e2050 higher than in a Referencescenario case which obtains the Climate and Energy package targets in 2020 and a long-term target of 40% emission reductions compared to 1990. The cost range depends onthe timely availability of certain decarbonisation options (e.g. CCS, electrification intransportation) and on the extent of emission reduction actions worldwide.

� 2012 Elsevier Ltd. All rights reserved.

1. Introduction

In February 2011 [1] the European Union (EU) confirmed its long-term objective to reduce greenhouse gas (GHG) emissions by 80e95%

compared to 1990 levels in 2005; the trajectory of such emissionsshould allow keeping global average temperatures below 2 �C provided

the strong emission reducing actions are also adopted worldwide [2].Towards more specific decisions for implementing this emission

trajectory, the European Commission proposed to Member-Statesa model-based quantification of the distribution of emission reduc-

tion by country and by sector of activity. The PRIMES energy systemmodel [3] has been employed for this purpose with the aim to quantify

a least-cost distribution of emission reduction efforts under alternativeboundary conditions which have assumed different degrees of avail-

ability of certain emission reduction options and also different

commitments of other world regions in reducing emissions. Among thepolicy questions emphasis has been given to estimating total emission

reduction costs under the various assumptions. This paper presents the

l rights reserved.

methodology of PRIMES model use, the distribution of emissionreduction as suggested using the model results and the cost effects.

The European Commission has communicated these results in the formof an impact assessment study accompanying the “Roadmap for moving

to a competitive low-carbon economy in 2050” [4].

2. Scenario definitions

The so-called “decarbonisation” scenarios are assumed to achieve80% GHG1 emission reduction2 domestically in the EU by 2050. The

decarbonisation scenarios have an equalised carbon budget (i.e.cumulative GHG emissions over the period 2010e2050) among them to

allow for comparability.The decarbonisation scenarios are built as variants of a Reference

scenario, which is a projection of the EU energy system until 2050 basedon an update of the model database carried out in 2009 [5]. The

Reference scenario assumed full implementation of the Climate andEnergy package for 2020 as well as including all policies approved until

1 GHG are all greenhouse gases covered by the Kyoto protocol.2 From 1990 levels.

P. Capros et al. / Energy Strategy Reviews 1 (2012) 76e84 77

March 2010 which concern legislative measures for energy efficiency,

car regulations, fuel quality directives, the buildings directive (asagreed in 2009) and additional eco-design measures. Through the

implementation of these bottom-up measures the Reference scenarioachieves domestically the 20% greenhouse gas emissions reduction

target in 2020. No additional emission targets are assumed for theperiod beyond 2020. The Emission Trading Scheme (ETS) legislation is

assumed to continue until 2050 with allowances decreasing throughoutthe time period; borrowing allowances from the future is not allowed,

whereas banking of allowances is allowed to take place from phase 2until 2050. ETS is the main strong policy in place beyond 2020 and is the

main driver for the continued emission reductions. The Referencescenario projects ETS carbon price reaching 16.5 V/tCO2 in 2020, 36

V/tCO2 in 2030 and 50 V3/tCO2 in 2050. The carbon value in the non-ETS sectors is maintained at the very low value (5.3 V/tCO2) found

necessary to achieve the non-ETS emission reduction target establishedby the Effort sharing decision for 2020. The policies and measures

implemented in the Reference scenario are sufficient to achieve the20% emission reduction target and the 20% target for renewables in

2020. The trends projected for the Reference scenario lead to 40%emission reduction in 2050.

The objective of the EU for 2050 goes beyond the 40% emissionreduction achieved in the Reference scenario and aims at achieving an

80%e95% emission reduction objective, as was confirmed by theEuropean Council in February 2011 [1].

The decarbonisation scenarios are therefore constructed so as toobtain an 80% reduction in internal EU GHG internal emissions

compared to 1990 levels. It is assumed that sectors covered by the EU

ETS are subject to carbon prices, and the purchase of allowances onauctioning. For the non-ETS sectors a carbon value is assumed which

does not imply direct payments by consumers and represents theshadow value (marginal cost) of non-identified measures additional to

the Reference scenario. In line with economic efficiency consider-ations, the non-ETS carbon value is assumed to have the same values as

the ETS carbon price, in all scenarios analysed in this paper unlessexplicitly mentioned. The level of the carbon value/price is calculated

endogenously in each scenario until the model-based projectionsachieve the targeted carbon budget defined for the period 2010e2050.

The macro-economic and demographic assumptions for thescenarios remain identical to the Reference scenario. On the other

hand two different assumptions were taken for international fuel pricesassuming two different contexts for the actions related to climate

change undertaken by the rest of the world; these contexts have beennamed global and fragmented climate action. The global climate action

context assumes that all world regions act in order to avoid climatechange, therefore achieving world wide a 50% emission reduction

compared to 1990 levels. In this context the prices of internationalfossil fuels are assumed to decrease due to lower demand at a global

level; by 2050 oil prices are 45% lower than in the Reference, gas prices50% and coal prices 30% lower. The fragmented climate action scenario

assumes that although the EU continues to pursue the 80% emissionreduction, in line with the global reduction of 50%, other world regions

only comply with their pledges under the Copenhagen Accord to 2020and do not intensify their efforts thereafter. Under the fragmented

scenario the demand for fossil fuels is assumed to continue to grow andtherefore the international fuel prices are assumed to be the same as in

the Reference scenario.Numerous decarbonisation scenarios were quantified under both

global and fragmented climate action. Among them a decarbonisationscenario with “effective technologies” was constructed representing

a policy environment which allows for the development of all major low

or free carbon technologies: energy efficiency and renewable energy

3 In constant euros of 2008.

sources (RES) in all sectors, carbon capture and storage (CCS) and

nuclear in power generation, and electrification of transport. Tech-nological progress is assumed to develop beyond Reference scenario

levels for some technologies: solar PV is assumed to decrease in costsmore than in the Reference, CCS is assumed to be successfully

demonstrated and become commercially available after 2020, nucleardevelopment is assumed to continue as planned in November 20094

with increased public acceptance and higher safety for nuclear oper-ations; batteries for electric vehicles are assumed to improve in terms

of cost, energy density, faster recharging, weight, longer lifetime,therefore allowing higher ranges for electric vehicles. It is assumed

that smart grids will be widely developed so that the electrification ofroad transport at a large scale can massively develop from 2030

onwards without adverse effects on electricity supply. Electrification isassumed to remain difficult for trucks, buses and long inter-urban

distances, due to technical constraints. Further RES incentives areassumed to be available representing facilitation of deployment of RES

including specific additional policies, land use planning, developmentof energy crops and construction of infrastructure. Scenarios with the

same technological and policy assumptions were quantified both underglobal and fragmented climate action.

Numerous sensitivity analyses were quantified under both globaland fragmented climate action trying to verify the impact of the delay

or non-availability of a decarbonisation option, the delay in imple-menting emission reduction policy, as well as regarding international

fuel prices; a summary of the scenarios described within this paper areavailable in Table 1.

Sensitivities for both the global and fragmented climate action were

carried out relating to: delay of climate action at EU level to beyond2030 (Reference scenario values are kept until 2030); a delay in the

availability of CCS by 10e15 years compared to the “effective tech-nologies” scenario; and a delay in the availability of electric vehicles

for transport. A sensitivity scenario under fragmented climate actionwas also quantified with lower effort for the energy intensive industries

that are subject to international competition. Further the effect ofa fossil fuel price shock was analysed both in the context of a decar-

bonisation scenario (fragmented climate action) and of a Referencescenario to verify the difference in impacts of such a price shock.

All decarbonisation scenarios share the same technology learningcurves but their timing depends on the scenario assumptions about

possible delays in policy actions and technology development. Thefollowing graphics illustrate key assumptions about the cost dynamics

of renewable technologies and of battery development as used for themain decarbonisation scenario which does not involve delays

(Figs. 1 and 2).

3. Methodological remarks

3.1. Introduction

The decarbonisation scenarios quantified using the PRIMES energysystem model all need to achieve strong GHG emission reductions,

additional to the Reference scenario. For the inclusion of non-CO2

emissions, which are not endogenously covered by PRIMES, marginal

abatement cost-curves based on the GAINS model [6,7] with support ofthe CAPRI model [8] and produced on a consistent basis with the PRIMES

reference scenario are used. The emission constraint is imposed on theEU wide level: the scenarios all have to comply with the constraint and

the changes influence the energy market equilibrium. All individualdecisions of the agents (demanders and suppliers of energy) are

4 The scenarios were quantified before the Fukushima accident in March 2011;

therefore changed assumptions about public acceptance and higher risk factors were

not taken into account.

Table 1

Summary of main scenario characteristics.

P. Capros et al. / Energy Strategy Reviews 1 (2012) 76e8478

influenced and so are the energy market prices. The model simulatesa least cost distribution of effort among countries and sectors

compliant with the different costs and potentials available by countryand by sector.

Additionally to the 80% domestic GHG emission reduction target in2050, to ensure the comparability of effort between the scenario

variants quantified, cumulative emissions are equalised betweenscenarios. This implies that two constraints are applied for emissions:

a cumulative constraint and an 80% reduction of domestic GHG emis-sions in 2050.

Fig. 1. Development of capital costs RES power plants in decarbonisation scenarios.

3.2. Drivers

To implement theemission reduction in themodel several drivers areintroduced in the scenarios. To achieve the GHG constraint the main

drivers are the dual variables (shadow prices) associated with the GHGemissions, termed as carbon values. The carbon values act on the

decision making of the agents: in the non-ETS sectors carbon values donot represent a cost to the consumer but they increase the perceived

costs to the consumers associated with the use of different energyforms, particularly the more polluting ones. For the ETS sectors the

Fig. 2. Development of battery costs for electric vehicles in PRIMES.

P. Capros et al. / Energy Strategy Reviews 1 (2012) 76e84 79

carbon value is equivalent to the price of ETS allowances and therefore

increases their cost. The power utilities, part of the ETS sectors, pass onthe higher carbon prices to the consumer thus further influencing

consumer choices. The carbon values which are equal for both the ETSand the non-ETS sectors are determined endogenously in the model

through iterations until the constraint of emission trajectory and thecumulative emission budget is met. Impact assessment conclusions are

drawn by comparing the results of the scenarios, which remain compa-rable due to the assumption of equal carbon budget between scenarios.

To facilitate and increase the penetration of RES the so-termed RES-value is increased compared to the Reference scenario. The RES-value is

a modelling tool used to represent facilitating policies for RES thatcannot be explicitly modelled such as higher availability of RES loca-

tions, facilitation of licencing and other benefits for employment andlocal economies. The RES-value is expressed in EURO per MWh produced

by renewableand influencesagents’decisionswhoperceive this valueasa marginal benefit from renewable energy. The RES-value can be

interpreted as a marginal value associated to a renewable obligation.Further RES deployment is also facilitated by the higher carbon values in

the model which act both in the power generation sector and in thedemand side (direct use of RES in final energy demand).

The entrance of specific technologies into the market such as CCSon the supply side and electric vehicles in the demand side is driven by

the assumption of strong technological improvements, as well as theavailability of a conducive environment for the introduction of such

technologies into the market (e.g. the availability of infrastructure forCO2 transportation and storage or the availability of smart grids and

recharging infrastructure for electric vehicles).

Energy efficiency is driven only by the higher carbon values, nofurther specific demand side policies are assumed. Nonetheless, the

model does simulate higher penetration of more efficient technologies,renovation of buildings, etc., assuming that unit costs of efficiency

improve over time, reflecting economies of scale.

3.3. Technology portfolio

The PRIMES model represents a large variety of technologies both forthe demand and the supply side. Under the effective technologies

scenario assumptions, all technologies are supposed to penetrate themarkets, become commercially mature and perform economies of scale.

Thedevelopmentof commerciallymaturepowerplantsequippedwith

carbon capture is only expected after 2025; the power plants assumed tobe constructed before 2025 in the scenarios are only pilot plants. The

captured carbon is assumed to be stored underground; the costs for thetransportation and storagearebasedonnon-linear cost curves dependent

on the amount of stored CO2, which are country specific. The regulatinglegislating and permitting is assumed to be timely for the development of

such power plants. Carbon capture can either be used on new powerplants or can be retrofitted on existing power plants.

For nuclear power plants the existing country plans and future devel-opmentsas ofend2009are taken intoconsideration5;nucleardevelopment

is onlyallowed incountries thatacceptnucleardevelopment.Developmentof new power plants on new sites is considered to be more expensive than

developing power plants on site or refurbishing old power plants.RES are assumed to improve their techno-economic performance,

as considerable cost reduction potential is assumed to be available forsome technologies (e.g. solar PV, wind offshore, tidal/wave, concen-

trated solar power). It is assumed that the higher deployment of RESinduced by the higher carbon values and facilitation through the RES-

value leads to scale effects and therefore cost reductions. The

5 Belgium and Germany are assumed to phase out nuclear; Austria, Cyprus, Denmark,

Estonia, Greece, Ireland, Latvia, Luxembourg, Malta and Portugal do not develop

nuclear; Italy and Poland develop nuclear.

availability of sites is assumed to increase in the decarbonisation

scenarios compared to the Reference, due to facilitating policiessimulated by the RES value. However, the model considers that

exploiting the RES potentials to their limits entails non-linearlyincreasing costs.

To limit the need for RES curtailment and facilitate the storage ofRES electricity, it is assumed that additional RES production is trans-

formed into hydrogen in moments of low demand and injected into thenatural gas pipelines; it is assumed that hydrogen can bemixed with gas

up to a maximum of 30%. There are several benefits from the conver-sion of additional RES electricity into hydrogen: the possibility of

storing excess RES electricity without curtailing RES power plants, thereduction of the emission factor of distributed gas which allows for

lower emissions in sectors where substitution with other fuels is diffi-cult or very costly such as small scale CHP, decentralised heating etc.

To achieve strong emission reductions also demand side end-useenergy efficiency needs to occur. In the decarbonisation scenarios full

implementationof all legislationpromotingenergyefficiency is assumedand further it is assumed that public promotion of the most advanced

energyefficient technologyoccurs reducing thebarriers for theirmarketpenetration over time. The high carbon values found in the scenarios

lead to a shift towards carbon free energy carriers in the demand sidesuch as electricity, RES and distributed heat; further the anticipation of

the availability of carbon free electricity further promotes the shifttowards electricity also in heating through the use of heat pumps.

For the transport sector it is assumed that electric vehicles in the formof battery electric vehicles become widely available due to the strong

improvement in the techno-economiccharacteristics of batteries reducing

costs, increasing energy density, and therefore increasing the range ofvehicles. Electric vehicleswerechosenas thedominant solution for carbon

free road passenger transport complemented by biofuels. The use ofbiomass is strongly enhanced in the scenarios, with second and third

generation biofuels, based on lignocellulosic feedstock, becoming avail-able beyond 2020; it is nonetheless assumed that given the limitations in

the availability of biomass their use should focus on transport modes inwhich the penetration of electro-mobility is currently considered infea-

sible such as aviation, shipping and long distance heavy road transport.The large scale electrification of transport, both with pure electric

and plug-in hybrids is assumed to occur alongside the development ona large scale deployment of smart grids and meters, as well as

advanced electricity grids. The higher deployment of intermittent RESrequires advanced electricity grids with storage possibilities as well as

a strengthening of the grid to allow for more deployment of decen-tralised RES; further due to the higher demand for electricity driven by

the electrification of transport it is necessary for the transmission anddistribution grid to provide the right incentives for demand reduction

and smoothing of the demand curve. The smart grids are necessary tospread the additional load throughout the day as without such a struc-

ture the additional demand for transport could lead to problems in thegrid stability.

Reductions of emission of GHG other than energy related CO2 aresimultaneously handled in the PRIMES model by considering marginal

abatement cost curves, provided by IIASA based on the GAINSmodel. SoGHG emission reduction is optimally distributed by emission origin.

4. The PRIMES model approach for decarbonisation

In this section we specify a stylized energy systemmodel in order to

illustrate the basic modelling features of PRIMES which are relevant foranalysis of decarbonisation scenarios.

Let us assume an economy with a representative consumer anda representative energy carrier producer. Consumers get utility using

energy and non-energy goods and services. The energy goods are dividedin three groups: fossil fuels which imply GHG emissions, energy carriers

such as electricity, distributed heat or hydrogen, and clean energy forms

P. Capros et al. / Energy Strategy Reviews 1 (2012) 76e8480

(e.g. renewables) without emissions. Producers of energy carriers mix

through a production function fossil fuels and clean energy forms (e.g.renewables, nuclear or carbon capture and storage) to produce the

amounts demanded by consumers. It is assumed that prices of energycarriers reflect total production costs, and that consumers are price

takers. The primary energy sources, which are the fossil fuels, the cleanenergy forms used by consumers and those used by energy carrier

producers, are all priced through cost-supply curveswith a positive slope.The consumers of primary energy forms are assumed to be price takers.

The stylized economy can be formulated mathematically as follows:Consumers maximize utility (U) under budget constraint (r denoting given

disposable income) and choose the mix of final energy (FE e the energybundle), further split in fossil fuels (FFE), energy carriers (EC) and clean

energy forms (CFE), and non-energy inputs (NE), the latter priced at pNEassumed to be given. Consumers perceive emission costs depending on

a carbon price (cp), which is the dual variable of the emission constraint,but they do not actually incur carbon payments (this corresponds to the

concept of carbon value, as opposed to carbon price). Technical possibil-ities are described by fe a production function which sets the substitution

possibilities between fossil fuels, energycarriers andcleanenergy formsatthe consumers’ level and fc a production function mixing fossil fuels and

clean energy forms to produce energy carriers. The prices of fossil fuelsused by final consumers and by energy carrier producers, respectively pFFEand pFEC are determined by cost-supply curves for fossil fuels. Unit costs ofclean energy resources as used by final consumers and by energy carrier

producers, respectively cFE and cEC, are determined by cost-supply curves.Energy carrier producers minimize production costs (C) to meet given

demand (EC) by mixing fossil fuels (FEC) and clean energy forms (CEC)

through a production function and tariff pEC energy carriers at averageproduction cost (which under perfect competition coincides with (long-

term) marginal cost and which under regulated monopoly corresponds toperfect regulation). Tariffs include the passing through of carbon costs to

consumer prices, depending on carbon price (cp) which is the dual vari-able of Eq. (4), as producers are assumed to incur carbon payments.

� MaxFFE;EC;CFE;NE

U ¼ u½feðFFE; EC;CFEÞ;NE�pFFEðFFEÞ$FFE þ pEC$ECþ cFEðCFEÞ$CFE þ pNE$NE � r

(1)

� MinFEC;CEC

C ¼ pFECðFECÞ$FECþ cECðCFCÞ$þ CECþ cp$eFEC$FEC

fcðFEC;CECÞ � EC

(2)

pEC ¼ C=EC (3)

eFFE$FFE þ eFEC$FEC � XE (4)

Total greenhouse gas emissions depend on unit emissions (eFFE, eFEC)of fossil fuel consumption by consumers and producers, and in total

they have to be lower than a fixed upper bound (XE). The formulation ofa single emission constraint Eq. (4) corresponds to an assumption that

equal carbon prices apply on final energy consumption and onproduction of energy carriers, as a means of obtaining least cost

emission reduction.The above system Eqs. (1)e(4) can be solved by transforming the

optimization problems Eqs. (1) and (2) into equivalent mixed comple-mentarity problems and by concatenating them with Eqs. (3) and (4).

Thus, a system of complementarity conditions is formed, as follows6:

lu$

�vpFFEvFFE

$FFE þ pFFE

�þ cp$eFFE � vu

vfe$vfe

vFFEtFFE � 0 (5)

6 The complementarity t symbol signifies that one of the two conditions is binding in

the optimum, and the other is not binding.

lu$pEC � vu

vfe$vfe

vECtEC � 0 (6)

lu$

�vcFEvCFE

$CFE þ cFE

�� vu

vfe$vfe

vCFEtCFE � 0 (7)

lu$pNE � vu

vNEtNE � 0 (8)

pFFEðFFEÞ$FFE þ pEC$ECþ cFEðCFEÞ$CFE þ pNE$NE � rtlu � 0

(9)

vpFECvFEC

$FECþ pFEC þ cp$eFEC � lc$vfc

vFECtFEC � 0 (10)

vcECvCEC

$CECþ cEC � lc$vfc

vCECtCEC � 0 (11)

fcðFEC;CECÞ � ECtlc � 0 (12)

pEC$EC ¼ pFECðFECÞ$FECþ cECðCECÞ$CECþ cp$eFEC$FECtpECfree

(13)

eFFE$FFE þ eFEC$FEC � XEtcp � 0 (14)

The conditions Eqs. (5)e(8) signify that marginal utility derived by

a consumption itemhas to be equal tomarginal cost of this item to allow

consumption to be strictly positive. Similarly, the conditions Eqs. (10)and (11) signify that marginal productivity of an input has to be equal

to marginal cost of this input to allow input consumption to be strictlypositive. The multipliers lu and lc denote marginal utility from income

and marginal cost of meeting demand for energy carriers, respectively.Assuming usual convexity conditions for problems Eqs. (1) and (2),

the consumers exhaust disposable income and producers exactly meetdemand. Thus, conditions Eqs. (9) and (12) are met with equality and

the associated multipliers are strictly positive. In addition, beyonda certain emission target, condition Eq. (14) becomes binding and the

carbon price is strictly positive. As all cost-supply functions (p, cFE, cEC)are monotonically increasing, it is possible that consumers and

producers use all inputs, depending on relative fixed costs associatedwith the primary resources. In such case, all conditions Eqs. (5)e(14)

are met as equalities in the optimal solution and the associatedunknown variables are strictly positive. In the context of decarbon-

isation, fossil fuel prices decrease and unit costs of clean resourcesincrease, as use of these resources comes close to maximum potential;

the gradients of change are anticipated by consumers and producers intheir optimized behaviour.

Electricity prices are set through Eq. (13) at a level sufficient torecover all costs, including carbon payments. According to Eq. (9) the

consumers do not pay directly for carbon emissions, but they do takeinto account carbon charges on fossil fuels, through Eq. (7), to deter-

mine their energy mix. When the carbon price is strictly positive,consumers change input mix because of the carbon payment term

entering condition Eq. (5). Thus, they indirectly incur additional costsand the purchasing power of income decreases; hence utility level

decreases. To compensate for this utility loss, additional income wouldbe necessitated, which is considered as a valuation of disutility costs.

If decarbonisation has lower marginal costs in production of energycarriers than in final consumption, then the consumers will tend to use

more energy carriers to the detriment of fossil fuels: this holds true if

the change in energy carrier price (pEC) driven by carbon price is lowerthan the increase of marginal cost of clean energy forms (cFE) used

directly by final consumers.

Table 2

Summary of ET_GCA (effective technology under global climate action) scenario

results.

2020 2030 2040 2050

Carbon price EU-ETS V(2008)/tCO2 25 60 78 190

Carbon value non-ETS V(2008)/tCO2 25 60 78 190

GHG compared to 1990 in % �25 �40 �62 �80

Reductions in ETS GHG emissions

from 2005 (%)

�24 �45 �71 �90

Reductions in non-ETS GHG emissions

from 2005 (%)

�15 �27 �48 �68

RES share in gross final energy (%) 20 26 37 52

Primary energy consumption (Mtoe)a 1619.5 1553.3 1407.6 1258.6

Reduction compared to 2005 (%) �5.2 �9.1 �17.6 �26.3

Energy Intensity (primary energy over

GDP- toe/MV05)

122.7 99.5 77.8 60.2

Percentage change from Reference

scenario (%)

�1.7 �2.5 �12.1 �22.2

Share of fossil fuels in total primary

energy (%)

70.6 62.2 51.1 39.4

Percentage difference from Reference

scenario (%)

�1.6 �5.5 �13.7 �24.4

Carbon Intensity of fossil fuel mix

(tCO2/toe)

2.62 2.38 1.84 1.15

Percentage change from Reference

scenario (%)

�1.2 �8.0 �20.3 �47.2

a Excluding non-energy uses.

P. Capros et al. / Energy Strategy Reviews 1 (2012) 76e84 81

Since the marginal costs of using clean energy forms increase with the

volume deployed, all clean energy forms will have to be used to achievelowest overall cost. The optimalmix is obviously determined by equalizing

the marginal costs of the clean energy forms and those of substitutionpossibilities.This applies separatelyonconsumersandonproducers,which

are coordinated through the prices which balances the energy markets.Energy efficiency improvement is reflected through substitution

between the energy bundle and the non-energy input to utility. Ifsubstitution to non-energy is less costly than substitution within the

energy bundle at the final energy demand level, then energy savingsdominate and so the decarbonisation possibilities in energy carrier

production are of less importance. Conversely, if substitution withinthe energy bundle is flexible enough and if decarbonisation of energy

carrier production is also flexible, then the energy carrier gets a highershare in final energy demand and helps decarbonisation. The absence

of flexibility in the substitution between energy and non-energy at finaldemand level may lead to very high compliance costs, as employing

only substitutions within the final energy bundle and within energycarrier production may imply use of clean resources coming close to

maximum potential. This emphasizes on the importance of employingall means of decarbonisation, including energy savings.

5. The main effects of decarbonisation7

5.1. Decarbonisation scenario under effective technologies and

global climate action

The main scenario analysed in this paper is a decarbonisationscenario with the facilitation of all low carbon and carbon free tech-

nologies including energy savings. We assume that the “effectivetechnology” scenario develops in the context of global climate action

(ET_GCA). This scenario foresees the equalisation of the ETS and non-ETS carbon values as needed to deliver the carbon budget, a strength-

ening of the policies facilitating RES, the availability of commerciallymature CCS from 2025 onwards, the development of large scale

infrastructure for transport electrification from 2030 onwards includingthe necessary grid infrastructure changes, as well as the strong

improvement in the techno-economic performance of batteries.In 2050, the scenario achieves the target of 80% GHG reduction

compared to 1990: for the ETS sectors emissions reduce by 90% comparedto 2005 levels and the non-ETS emissions are found to decrease by 68%

compared to 2005 levels. As the model operates under perfect foresightthe anticipation of higher ETS carbon prices leads to higher emission

reductions than in theReferencealready in 2020: emissions reduceby 25%in the ET_GCA (and other decarbonisation variants) instead of the 20%

achieved in the Reference scenario. This emissions trajectory is found tobe more cost effective and additionally is justified in the context of

pursuing the 2 �C trajectory, as it leads to lower cumulative emissions.The share of RES in gross final energy demand rises from 20% in 2020

to 52% in 2050; primary energy consumption excluding non-energy usesdecreases by 26.3% compared to 2005 levels, whereas in the Reference

scenario primary energy consumption (excluding non-energy uses)slightly increases over time.

As shown in Table 2, the decarbonisation of the energy system isa result of lower energy intensity, lower use of fossil fuels and lower

average emissions from the remaining fossil fuels.The majority of the remaining emissions in 2050 are due to non-CO2

GHG emissions and to the transport sector (Fig. 3); some transportmodes, aviation, navigation and heavy road transport, remain linked to

oil based fuels although these are partially substituted with biofuels.

The emissions remaining in the other sectors are lower and are mainlyrelated to remaining natural gas use in these sectors.

7 All results are shown for EU27; results are also available by Member-State.

5.1.1. Decomposition of emission reduction

To facilitate the understanding of how the decarbonisation takes place

within the “effective technologies under global climate action” (ET_GCA)scenario a decomposition of the emission reductionwas calculated ex-post

from the PRIMESmodel results andwas quantified based on the differencescompared to a business as usual scenario. Energy efficiency accounts for

between 30 and 34% of the emission reduction in the ET_GCA scenariothroughout the time period; this efficiency is roughly equally divided

between energy efficiency occurring already in the BAU which can be

characterised as market driven energy efficiency progress, in other wordsan autonomous technical progress and policy induced energy efficiency

driven by policies implemented beyond the BAU projection. The largestpart of the emission reduction is caused by shifts towards low carbon or

carbon free technologies namely RES, nuclear andCCS. The increase of RESin primary energy consumption makes the largest contribution to emission

reduction. Changes in the fuel mix account for between 8 and 16%; thisfactor accounts for the reduction in theemission factor of the fossil fuelmix

and becomes more important over time when the emission reductionconstraint becomes more stringent and all options need to be exploited.

The reduction of emissions in the fossil fuel mix in the ET_GCA is driven bythe substitution towards gas, into which hydrogen produced from RES is

injected.

Fig. 3. Evolution of GHG emissions by sector.

Table 4

Decomposition in % of reduction in CO2 emissions at final energy demand in the

ET_GCA, relative to the business-as-usual.

% 2020 2030 2050

Energy efficiency 77.3 70.3 40.5

Use of carbon free energya 23.0 27.0 56.6

Change in fossil fuel mix �0.2 2.7 2.9

SUM 100.0 100.0 100.0

a From a final energy demand perspective, electricity and heat distributed do not

imply emissions.

P. Capros et al. / Energy Strategy Reviews 1 (2012) 76e8482

As can be seen by this decomposition the reduction in emissions is

caused roughly to a third by energy efficiency, a third by increase use ofRES and a third based on shifts towards nuclear and CCS or within the

power generation sector. The role of nuclear and CCS combinedaccounts for approx. 23% of the total reduction cumulatively over the

time period (Table 3).The final energy demand sectors, where electricity and steam/heat

are considered as carbon free energy carriers, shows that energy effi-ciency is the dominating factor to achieve emission reductions Table 4.

Energy efficiency is achieved through investments in electric appliancesand investments in improvements of buildings, as well as changes in

behaviour. The shift towards carbon-free energy carriers includes shiftstowards electricity and heat as well as increase in direct use of RES in

final demand sectors; such possibilities are more limited in the shortterm and becomehigh in themid and long-termwhen the electrification

of road transportoccurs.Thechanges in the fossil fuelmix imply the shifttowardsa less emitting fossil fuelmix,mainly a shift towardsnatural gas;

thepossibilities are limited for further shifts as the shareof natural gas infinal energy demand is already high in the Reference scenario.

6. Costs and prices

As described above the PRIMES model is a partial equilibrium model

that explicitly determines energy commodity prices; well functioningmarkets, with prices at sufficient level to recover all costs (including

RES supports, stranded investments and payments for allowances) areassumed in the long run. Prices are therefore above marginal costs.

From a macroeconomic perspective, total energy system cost isdefined so as to include all types of costs incurred by consumers to get

the required useful energy services, including annual costs of capitalfor equipment, energy savings and appliances, as well as payments for

purchasing energy commodities.Assuming that auction revenues from ETS emission allowances

return back to consumers as income, after an assumed economically“perfect” use of such revenues by the state, total energy system cost

should not include auction payments.

6.1. Carbon prices/values

The PRIMES model uses, as described above, carbon values to influ-ence the choices of the decision agents. The carbon values are equalised

in the decarbonisation scenarios between the ETS and non-ETS sectorsfrom 2015 onwards with the exception of the delayed climate action

scenarios where they are equalised only beyond 2030 when climateaction resumes, remaining at Reference scenario values before.

The trajectories of the different scenarios (Table 5), relatively lowcarbon values in the starting years and high to very high carbon values

in the end periods, are consistent with a least cost approach. Thesupply side sectors in the model operate under perfect foresight

therefore the future carbon prices influence the decision-making inearlier years, thus potentially avoiding technology lock-in. In the

demand side foresight at a shorter time horizon is assumed.

Table 3

Decomposition in % of reduction in CO2 emissions at primary energy, relative to

a business-as-usual scenario.

% 2020 2030 2050 2011e2050

Energy efficiency 30 29 34 31

Use of carbon free energy sources 60 58 50 56

Nuclear 18 16 8 13

Renewables 39 37 27 33

CCS 3 6 14 10

Change in fossil fuel mix 10 13 16 13

Sum 100 100 100 100

The carbon prices in the scenarios with higher fossil fuel prices,reflecting fragmented climate action at a worldwide level, have

consistently lower carbon prices, as the higher prices act as a limitingfactor to energy consumption.

The highest carbon prices in the end time period are found in thedelayed CCS scenarios where prices peak in the end time period; in the

delayed climate action and delayed electrification scenarios thecarbon prices are consistently high in the last decade to recuperate the

delay in the technological development and/or policy action.

6.2. Total energy system costs

Total energy system costs are the aggregation of all bottom-up costs

encountered for by the different final energy users. They are calculatedfrom amarket actor perspective and can therefore not be equated with

societal costs. The model associates capital costs to investment andvariable costs to operation. As the model keeps account of the vintages

of equipment, the technical characteristics of equipment built ina specific time period are maintained throughout the lifetime of the

equipment. The model includes costs for non-energy services such asinvestments for energy savings purposes, e.g. building insulation. For

the transport sector assumptions in themodelling are different becauseit is difficult to divide energy from non-energy costs. The following

approach has been taken: all the additional costs compared to a frozentechnology at 2005 levels are accounted for as energy costs. The

“energy” costs relating to transport may therefore be overestimated.For capital costs the PRIMES model uses discount rates reflecting

weighted average cost of capital (WACC) which are specific for eachsector or groups of sectors and does not use social discount rates.8

The total energy system costs as shown below include: capital costs(including capital costs related to energy efficiency and equipment

purchasing by final energy users), energy purchasing costs and disutilitycosts. Capital costs in energy supply sectors are recovered by energy

commodity prices and are included in energy purchasing costs incurringfor final energy users. ETS auction payments are excluded as they

correspond to a transfer payment, assuming that the state recycles therevenues back to the economy. Disutility costs are a concept used for

the estimation of losses of utility for the different actors; these maycorrespond to changes in behaviour due to the adaptation of users to

higher prices or as a response to policy. Disutility costs are calculated inPRIMES through the income compensating variation methodology; it

represents the additional income needed to render consumers’ usefulenergy consumption levels at those in the chosen baseline scenario

while consumer energy prices are as high as in the policy scenario. Thelimitation of such an approach is that the quantification of the disutility

is based on the comparison of two scenarios (i.e. a baseline witha decarbonisation scenario) and it is assumed that the habits or tastes

(utility) remain unchanged. This approach is commonly used in general

equilibrium modelling, but the use of such an approach over long timehorizons is discussible due to the high uncertainty concerning possible

8 The discount rates used in PRIMES can be found in Annex 1 of [9].

Table 5

Carbon values in the different scenarios.

2010 2015 2020 2025 2030 2035 2040 2045 2050

ET_GCA V’08/tCO2 11 15 25 38 60 64 78 115 190

D_GCA V’08/tCO2 11 14 17 20 36 78 170 278 340

%Diff to ET_GCA �9% �34% �47% �40% 22% 118% 142% 79%

DEF_GCA V’08/tCO2 11 15 25 42 57 62 92 136 245

%Diff to ET_GCA 0% 0% 11% �6% �3% 18% 18% 29%

DCCS_GCA V’08/tCO2 11 15 25 39 62 69 100 218 370

%Diff to ET_GCA 0% 0% 4% 3% 9% 28% 90% 95%

ET_FCA V’08/tCO2 11 15 25 34 51 53 64 92 147

%Diff to ET_GCA 0% 0% �9% �15% �16% �18% �20% �23%

D_FCA V’08/tCO2 11 14 17 20 36 65 131 207 250

%Diff to ET_GCA �9% �34% �47% �40% 3% 67% 80% 32%

DEF_FCA V’08/tCO2 11 15 25 37 51 55 75 111 182

%Diff to ET_GCA 0% 0% �2% �15% �14% �3% �4% �4%

DCCS_FCA V’08/tCO2 11 15 25 35 53 59 80 185 340

%Diff to ET_GCA 0% 0% �7% �12% �7% 3% 61% 79%

P. Capros et al. / Energy Strategy Reviews 1 (2012) 76e84 83

behavioural changes due to e.g. the availability of new technologies.The cost estimates heavily depend on the future costs of energy

technologies which are assumed to be known today and evolve towardscommercial maturity. Should other technology developments take

place, cost estimates would be different.Cumulative total energy system costs are 41 billion V average

annually or (0.24% of cumulative GDP) higher in the ET_GCA scenariocompared to the Reference. Under the assumption of unchanged fuel

prices (ET_FCA) the decarbonisation scenario becomes significantly

more expensive, but the costs still remain in the order of magnitude ofaround 1% of GDP (Table 6). The impact of global decarbonisation on

world fossil fuel prices is of course uncertain; the results shown forET_CGA should be considered as an optimistic lower bound of cost

effects of decarbonisation.The decarbonisation scenarios imply a significant change in the

structure of energy costs: in all cases, capital and fixed costs increaseand variable costs decrease. This change is observed both for final

consumers and for energy suppliers. The decarbonisation options aremore capital intensive than current practice.

Of the ET_GCA sensitivities the delayed CCS scenario is found tohave the least additional cost; this is due to the availability of other

decarbonisation options, among which nuclear and RES, which havebeen found to make significant additional contributions to the decar-

bonisation scenario even in scenarios with the availability of CCS. Theabsence or delay in CCS therefore causes only limited cost changes to

the energy system in cumulative terms.The highest effect in terms of cost on the decarbonisation is found to be

in the delayed transport electrification scenario; this additional cost is dueto the higher cost of electrification assumed in this scenario aside from the

delay. The more limited availability of electrification also leads to theincreaseofeffort inother sectorsbeyondthe levelsof theET_GCAscenario.

The results also show that a pause in additional action for climate change in

Table 6

Cumulative total energy system costs excl. auction payments.

Average annual costs 2011e2050 Cumulative

costs as % of

cumul. GDPBn.

V’08

Diff.

(Bn. V’08)

% Diff. to

Reference

Reference 2618 15.40%

Effective technologies e

global action

2659 41 1.6% 15.64%

Effective technologies e

fragmented action

2811 194 7.4% 16.54%

theEUbetween2020and2030 leads toa largecost increase; themajorityofthese additional costs are carried by the transport sector as also in this case

a delay in the development of electrification had a major effect (Table 7).Theeffects on the total energy systemcosts in the fragmentedclimate

action sensitivities are more limited than in the global climate actionscenarios. This is due to the fact that a part of the effort for decarbon-

isation is induced by the higher fuel prices; the effort for decarbonisationis therefore lower and the impacts from delays in technology availability

are smaller. The increase in costs in the different delay scenarios is

analogous to the scenarios in theglobal climateaction scenarios (Table 8).In case of an oil price shock the model results show that the

decarbonisation scenario reduces the effects of the shock in terms oftotal cost of the energy system. If an oil price shock is applied in the

Reference scenario it leads to a cost increase of 16% or approx. 17trillion V; if the same oil price shock is applied to the decarbonisation

scenario (with fragmented climate action) the price increase is moremoderate with a 10% increase or approx. 11 trillion V. The decarbon-

isation therefore has a hedging role (amounted to 6 trillion V incumulative terms) against fossil fuel price increases as dependence on

fossil fuels decreases in the long term (Table 9).

6.3. Macro-economic implications

The PRIMES model is a partial equilibrium model therefore the

effects on overall GDP can only be calculated in combination with

a general equilibrium model. The overall costs calculated by PRIMEScompared to the Reference represent the additional costs that the rest

of the economy needs to pay to obtain the required energy service.In the course of the preparation of the Impact Assessment for the

“Roadmap for moving to a low-carbon economy in 2050” [4] analysiswas also carried out with the general equilibrium model GEM-E3 [10] to

Table 7

Average annual energy system costs excl. auction payments for the ET_GCA

sensitivities.

Average annual costs 2011e2050 Cumulative

costs as % of

cumul. GDPBn.

V’08

Diff.

(Bn. V’08)

% Diff. to

ET_GCA

Effective technologies e

global action

2659 15.40%

Delayed climate action 2762 104 3.9% 16.25%

Delayed electrification

in transport

2784 125 4.7% 16.37%

Delayed CCS 2677 18 0.7% 15.74%

Table 8

Average annual energy system costs excl. auction payments for the ET_FCA

sensitivities.

Average annual costs 2011e2050 Cumulative

costs as % of

cumul. GDPBn.

V’08

Diff.

(Bn. V’08)

% Diff. to

ET_FCA

Effective technologies e

fragmented action

2811 16.54%

Delayed climate action 2845 34 1.2% 16.73%

Delayed electrification

in transport

2895 84 3.0% 17.03%

Delayed CCS 2817 5 0.2% 16.57%

Table 9

Cumulative total energy system costs excl. auction payments for the oil price shock

scenarios.

Average annual costs 2011e2050

Bn.

V’08

Diff.

(Bn. V’08)

% Diff. to

Reference

Reference 2618

Prise rise 3046 429 16%

Effective technologies e

fragmented action

2811

Price rise 3090 279 10%

Decarbonisation gain 150

9 http://www.stanford.edu/group/MERGE/.10 http://www.pik-potsdam.de/research/sustainable-solutions/models/remind.11 http://www.globalchange.umd.edu/models/gcam/.

P. Capros et al. / Energy Strategy Reviews 1 (2012) 76e8484

analyse the impacts of the decarbonisation on the overall economy

based on the PRIMES scenarios and therefore calculate the effects onoverall GDP of the decarbonisation.

The analysis found that the additional costs required to obtain theenergy services, as quantified by the PRIMES model, are similar to the

overall losses in GDP as quantified by the GEM-E3 model. This impliesthat the positive and negative drivers deriving from moving towards

decarbonisation compensate each other, leaving only the additionalcosts for the energy system.

From the analysis undertaken it can therefore be concluded thatthe additional costs as percentage of GDP as quantified by the PRIMES

model can be considered a good proxy for the overall loss in GDP, underthe analysed circumstances.

7. Conclusions

The PRIMES model is a partial equilibrium hybrid model which

combines bottom-up engineering detail with a micro-economic foun-dation of economic decisions by agent. The modelling approach used

and described within this paper shows a different approach from other

models such as global linear optimisation models like TIMES/MARKAL[11] or long-term models used for analyses of climate impacts.

ThePRIMESmodel is able to reproduce behaviour of decision agents ata detailed sectoral level ensuring consistency with explicit price setting

operated through market equilibrium, contrary to global linear optimi-sation models in which consumer behaviour is only driven by implicit

marginal costs. The PRIMES model by construction has RamseyeBoiteuxpricing for energy carriers and grids which therefore includes also fixed

costs in the determination of commodity prices. Therefore the PRIMESmodel using explicit pricing is able to perform substitutions both on the

supply and demand side with more realism taking into account behav-ioural aspects and all kinds of fixed costs. Unlike linear programming

basedmodelswhere technologiesandabatementoptionsareusedtotheirmaximumpotential beforepassing toa further option, inPRIMESdifferent

options can coexist as all costs derive from non-linear relationships.

Long-term models for energy system analysis such as MERGE,9

REMIND10 or G-CAM11 are energy economy growth models in which thecost of emission reduction strongly depends on elasticities of substitution

assumed, on growth assumptions as well as on the technology develop-ment assumed; these models due to the longer time-horizon have a more

implicit representation of how the restructuring of the energy systemtakes place. These models contrary to PRIMES have a full inter-temporal

approach and are therefore able to construct emission pathways. Thesemodels, being different in scope, calculate the global boundary condi-

tions for thePRIMESmodel suchas theemission timeprofile, the targets tobe delivered domestically and the world distribution of efforts related to

climate change mitigation which allows for the fuel prices to be derived.The PRIMES model was used to analyse how decarbonisation can

occur in the EU energy sector, the implications on the different sectors,their resulting emission reductions and the restructuring of the energy

system required which is explicitly described in Ref. [11].The decarbonisation scenarios, as modelled with the PRIMES model,

show that the decarbonisation of the EU economy is possible in the timehorizon to 2050, with technologies which are known today.

From an energy system cost perspective, the decarbonisation wasfound to cost between0.24and 1.63percentagepoints of cumulativeGDP

over the time period 2010e2050, above costs incurring under theassumptions of a Reference scenario, which is supposed to implement the

Energy and Climate Package for 2020 without further emission reductiontargets after 2020. The cost range crucially depends on the feedback

effects of decarbonisation on world fossil fuel prices. The lower costbound corresponds to decarbonisation action developing worldwide to

meet the 2 �C target if also assuming that world fossil fuel prices drop

significantly from Reference scenario levels. The upper bound corre-sponds to decarbonisation action pursued only in the EU, with the rest of

the world pursuing weak climate policies of the order of Copenhagenaccord pledges. The analysis confirmed the importance for the costs of

ensuring that all decarbonisation options are timely available; lack ofcertain options or delays in development of technologies or supporting

policies would imply higher costs for equal emission reduction.The analysis also verified that decarbonisation policies provide

significant hedging benefits against risks of security of energy supplyand possible oil price shocks.

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