[pic] | COMMISSION OF THE EUROPEAN COMMUNITIES |

Brussels, 10.1.2007

SEC(2006) 1719

COMMISSION STAFF WORKING DOCUMENT Accompanying document to the

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 IMPACT ASSESSMENT

{COM(2006) 848 final}{SEC(2006) 1720}{SEC(2007) 12}

TABLE OF CONTENTS

1. PROCEDURAL ISSUES AND CONSULTATION OF INTERESTED PARTIES 3

2. PROBLEM DEFINITION 5

3. OBJECTIVES 6

4. POLICY OPTIONS 6

4.1. Future energy policy: business-as-usual or strong renewable energy policy? 7

4.2. Options for instruments for a strong renewable energy policy 8

5. ANALYSIS OF IMPACTS 13

5.1. Impact of different levels of renewable energy use: business-as-usual vs. ambitious share in 2020 13

5.2. Impact of different options for renewable energy targets 24

6. COMPARING THE OPTIONS 28

6.1. Comparison of options for levels of renewable energy use 28

6.2. Comparison of options for renewable energy targets 28

7. MONITORING AND EVALUATION 29

Annex 1: PRIMES methodology and modelling approach 30

Annex 2: Green-X methodology and modelling approach 36

Annex 3: Details of PRIMES and Green-X scenarios used in the impact assessment 48

Annex 4: The ASTRA employment and GDP model 52

Annex 5: Input-output modelling of employment and GDP effects of biofuels policy 54

Annex 6: The POLES/PACE Hybrid approach 55

Annex 7: The potential for exports of renewable energy technologies 60

PROCEDURAL ISSUES AND CONSULTATION OF INTERESTED PARTIES

On 8 March, 2006, the European Commission adopted an Energy Green Paper, entitled A European Strategy for Sustainable, Competitive and Secure Energy [1] . The Green Paper responded to the calls from Heads of State and Government in October (Hampton Court) and December 2005 for the Commission to present new proposals on a common energy policy.

The European Council debated the Green Paper at its meeting in March 2006. In the Presidency Conclusions from the meeting[2], the Commission was asked to put forward a Renewable Energy Roadmap and to look into the option of a 15% target for renewable energy in 2015. The European Parliament, for its part, has called for a mandatory target for renewable energy of 25% of overall energy consumption in 2020 (together with mandatory sectoral targets)[3].

In its 2004 communication “The share of renewable energy in the EU”[4], the Commission noted that EU policy had been guided since 1997 by the objective of a 12% share for renewable energy in 2010. It acknowledged the importance of providing a longer-term perspective. Before deciding on adopting targets beyond 2010 and taking a position on the 20% target for 2020 proposed by the Parliament, the Commission declared its intention of carrying out an impact assessment to examine the feasibility and the economic, social and environmental implications of renewable energy.

In the light of these strong political messages, this impact assessment aims to shed light on the question of whether the EU should adopt quantified targets for the share of renewable energy in 2020, and if so, for what amounts and in what form. It is based on a variety of inputs and analyses including studies carried out by external experts – notably exercises using the PRIMES and Green-X models.

The various scenarios using the PRIMES and Green-X models have been carried out for EU25. However, to take account of the enlargement of the European Union on 1 January 2007 to include Bulgaria and Romania, a model run on the EU27 using the PRIMES model[5] was also carried out.

An interdepartmental Commission group was set up to coordinate this work. It met seven times between April 2005 and November 2006[6].

The main issues addressed in the roadmap were debated in the public consultation on the Energy Green Paper and the Strategic European Energy Review between March and September 2006. This process included consultations with Member States, the European Council, the European Parliament, citizens, stakeholder groups, civil society organisations, NGOs and consumer organisations, discussions on various forums, a web page created on the Europa website including a questionnaire and a mailbox for unresolved questions[7].

The questions raised in the public consultation on the Green Paper included whether renewable energy can contribute to ensuring access to energy at reasonable prices in Europe; whether it can contribute to diversification of the energy mix and sustainable development in the EU; and whether defined long-term targets and an action plan to promote renewable energy are important for the further development of clean and renewable energy sources in the EU.

The main messages that emerged from this consultation are as follows:

- The renewable energy targets proposed in the Presidency conclusions of March 2006 are welcomed. Clear and quantified targets beyond 2010 are useful, taking regional circumstances into account. There was wide support for a longer-term target for renewable energy, with suggestions ranging from 20% in 2020 to 50% and more by 2040/2050. The use of obligatory targets was widely supported, as was the internalisation of external costs.

- Three quarters of respondents to the questionnaire saw an increase in the share of renewable energy as the most important step to take to diversify the energy mix.

- Two thirds of respondents considered that the best way to fight climate change is through renewable energy sources and energy efficiency.

- The wider use of renewable energy was also favoured as a means to protect the environment, support technology development and jobs and pave the way to a post-oil era.

- Energy technology development should be innovative and should be backed by a Strategic EU Energy Technology Action Plan. The majority of respondents emphasised further development of renewable energy technologies (solar, wind, biomass).

- Two thirds of respondents considered renewable energies the best option to ensure that all Europeans enjoy access to energy at reasonable prices.

- More than two thirds of respondents believed that the EU should incorporate renewable energy in its external energy policy.

- Discussion of Europe-wide optimisation and possible harmonisation of framework conditions is needed.

- A regional approach, taking into account local circumstances, should be a key element in devising a renewable energy policy.

Complementary consultation exercises were conducted, including consultation of the European Energy and Transport Forum. This is a consultative body set up by the Commission in 2001[8] with 34 full members directly appointed by the Commission to represent operators (energy producers, carriers, and manufacturing industry), managers of networks and infrastructure, users and consumers, unions, environmental protection and safety organisations, and academics. A large majority of the Forum concluded that the European Commission should propose mandatory targets for 2020. The level of the 2020 targets should be based on ambitious and realistic assessments of national renewable potentials.

At the same time, the Commission consulted stakeholders on the review of the biofuels directive and on renewable energy in heating and cooling.

PROBLEM DEFINITION

The European Union faces major challenges concerning climate change, security of energy supply, and the need to increase market competitiveness. Energy demand is steadily increasing and dependence on fossil fuels from outside the European Union is growing, at a time of fiercer competition on the global energy markets, inevitably pushing up energy prices. The main factor driving the underlying growth in energy demand is economic growth.

Current policy analyses show that greenhouse gas (GHG) emissions are rising. The risks and costs related to climate change are many: increasing natural disasters worldwide, flooding, and countries being submerged. The costs of catastrophes are high and the costs of adaptation are potentially large. Combating climate change will require substantial reductions in GHG emissions, which means switching to low-carbon energy and reducing energy consumption.

The challenge of climate change is coupled with the challenge of increased dependence on imports of fossil fuels. The current rate of import dependency is expected to rise from about 50% to 70% over the next 30 years. Import dependency[9], combined with the increasingly volatile world energy market, increases uncertainty of energy supply and the risks of supply disruption. These developments are inflationary and economically destabilising and have an adverse impact on economic growth and investment. The instability of the world energy market has geopolitical consequences and imposes costs on energy importing countries.

Despite numerous ongoing energy efficiency efforts[10], energy consumption growth is still a cause of concern.

Renewable energy sources have the potential to tackle these environmental and economic problems. However, renewable energy is not developing as fast as hoped. Since 1997, the EU has been working towards a target of a 12% share of renewable energy in gross inland energy consumption by 2010. In 1997, the share of renewable energy was 5.4% and in 2004 it had reached 6.5%. It is likely that only a 9-10% share will be reached by 2010[11].

OBJECTIVES

The overall objective of European energy policy is to contribute to sustainability, competitiveness and security of supply. Given the immediate threat of climate change and the increasing dependence on fossil fuels, the point of departure for a common energy policy as stated in the Strategic Energy Review[12] is "the dual objective of limiting the EU's dependence on imported hydrocarbons and combating climate change through a progressive transformation towards a highly energy efficient and low CO2 European Economy". A key strategic energy objective for the EU is to reduce CO2 from energy use in the EU by 2020 by at least 20% compared to 1990 levels in a manner compatible with its competitiveness objectives.

The development of renewable energy sources has been and will continue to be a central aim of European Union energy policy. Renewable energy sources are indigenous, they do not contribute to the build-up of greenhouse gases and they are predominantly decentralised. A strong policy for renewable energy deployment and energy efficiency will not only contribute to the dual objective of limiting EU's dependence on imported hydrocarbons and combating climate change, it can also contribute to competitiveness and Lisbon goals, in particular through the creation of high-quality jobs in Europe and in maintaining Europe's technological leadership in a rapidly growing global sector. Furthermore, developing renewable energy sources will lead to a number of new producers entering the market, which will have a positive impact on the competitiveness of the energy markets.

In the context of, and following on from, the problem analysis in section 2, this impact assessment looks into two questions:

Firstly, should the promotion of renewable energy play an important part in the Community's future energy policy?

Secondly, if renewable energy is to be promoted, what is the best way to do this?

POLICY OPTIONS

This chapter defines two sets of policy options. The first set is designed to crystallize the choice between a business-as-usual approach and an ambitious policy of renewable energy promotion; the second consists of options for tools to implement such an ambitious policy.

Future energy policy: business-as-usual or strong renewable energy policy?

The single most important choice facing the Community is whether to adopt a "business-as-usual" attitude to the development of renewable energy, or to adopt a coherent policy stance in its favour.

It should be underlined that opting for a business-as-usual approach does not mean opting to rely on conventional energy alone. The models used here[13] suggest that even under business-as-usual conditions, the share of renewable energy will grow to between 10.4 and 12.6% in 2020, compared with 6.5 % today. The Community needs to decide whether this is enough, or whether a more ambitious approach is needed.

There is a convergence of views between the Parliament, Commission and Council on defining a policy option to embody this more ambitious approach. In 2004, the European Parliament called for a target of a 20% share of renewable energy in 2020[14]. Also in 2004, the Commission agreed to "thoroughly assess the impacts of RES resources, notably with regard to their global economic effects before deciding on adopting targets beyond 2010 and before taking a position on a 20% target for the share of renewable energy in 2020"[15]. And in 2006, the spring European Council asked the Commission to look into a 15% target for renewable energy in 2015. The scenarios the Commission has devised to illustrate the impact of a significantly higher share of renewable energy in 2020 all imply the achievement of a higher share in the region of 15% in 2015. In the light of this, the ambitious policy approach chosen for comparison with the business-as-usual approach is one that would deliver a significantly higher renewable energy share in 2020.

To compare the business-as-usual approach with the ambitious approach, the following impacts are examined in section 5.1:

- feasibility and achievability risks ( 5.1.1 );

- costs ( 5.1.2 );

- benefits ( 5.1.3 ):

- greenhouse gas (GHG) emissions

- security of supply

- employment, GDP and export opportunities

- biodiversity impacts

- regional development and rural economy.

The section also includes a sensitivity analysis examining the consequences of aiming for a 16%, 18% or 22% share instead of the 20% share that reflects the positions taken by the Parliament and Council.

Options for instruments for a strong renewable energy policy

Although the cost of renewable energy sources is falling, most types of renewable energy still cost more than their conventional counterparts. Renewable energy is also disadvantaged by the existence of market failures (external costs, imperfect information, capital market failure) and strong barriers to further development. The wider costs of energy, in particular the external cost of using conventional and nuclear energy, are not fully reflected in today's energy prices. This has an adverse effect on investment decisions in renewable energy and leads to sub-optimal levels of renewable energy production. In addition, numerous administrative barriers exist to further deployment of renewables. For these reasons, renewable energy will only develop if efforts are made to promote it.

A wide range of tools is available to promote renewable energy. They can be divided into non-regulatory, price-related and quantity-related measures, as discussed in the following sections.

Feasibility of relying entirely on non-regulatory measures

This section looks into three types of non-regulatory measures: voluntary agreements; consumer information; and research and technological development (RTD). Non-regulatory measures have always played a part in EU and national work to promote renewable energy. They will continue to do so. The policy question here is not whether this should be the case, but whether this should be the only type of measure used. This section therefore examines whether the option of relying entirely on non-regulatory measures will be enough to achieve a significantly higher share in 2020.

Voluntary agreements

In various policy areas (most recently the "ACEA agreement" on CO2 emissions from cars), market failures can be addressed by industry or other stakeholders publicly entering into voluntary commitments to achieve a certain goal. Such an approach is useful where a regulatory approach is likely to be especially burdensome and where the stakeholders involved convincingly demonstrate their commitment.

For example, stakeholders could conclude a voluntary agreement to increase their use of renewable energy sources with existing energy suppliers (e.g. electricity generators promoting solar power or biomass electricity generation).

Voluntary agreements may be appropriate as a "soft", non-regulatory approach to addressing any given problem. They are useful if they are credible: that is if they do, in the end, provide an effective means of attaining a certain objective. In the context of renewable energy, it is not obvious that voluntary agreements are a feasible policy option. This is because the market players (energy producers, suppliers, distributors) are often direct competitors with producers of renewable energy sources. As a result, cooperation to boost renewable energy growth is unlikely to occur. In the car sector (where the ACEA agreement is the main example of voluntary agreements as a policy tool), agreement was possible because vehicle manufacturers agreed to work to the same goal and were each undertaking technological efforts in this area. In the energy sector, market incumbents are generally conventional energy producers who compete directly with renewable energy producers. The structure is thus unlikely to lend itself to a successful voluntary agreement.

Consumer information

One reason why renewable energy is not growing faster is the poor and asymmetric flow of information between actors, not least between equipment suppliers and consumers. One policy option is clearly to take measures to provide information to the market, boosting consumer awareness of the costs and benefits of using renewable energy. Such measures may include information campaigns, labelling and product certification.

A great deal of effort is already made to increase consumer awareness (through labelling campaigns, certification and standard setting, and R&D into consumer-related aspects of renewable energy policy). Such measures normally imply a regulatory approach and, hence, additional costs. Through Community legislation, consumers now have the right to know what proportion of the electricity they consume is generated from various sources[16]. The evidence[17] from Member States that have made such information available for some time, along with a right to switch to a renewable energy supplier, is that these measures do indeed encourage a shift towards renewable energy. However, the scale of this shift is too small to suggest that such measures could play a central part in achieving an ambitious Community target share for renewable energy in 2020.

Research and Technology Development (RTD)

When there are technological barriers that have to be overcome or when there are other unknown aspects regarding the development and deployment of a certain product or technology, it is appropriate to promote research and development in the area, either directly or indirectly.

The European Union finances considerable research and development and technology deployment in the field of renewable energy through the 6th Framework programme on Research and Technology Development, addressing technological, economic, social, environmental, distributional and fiscal barriers to market development. There are other Community instruments addressing economical and social barriers, such as the Intelligent Energy for Europe (IEE) Programme.

Renewable energy RTD at Community and national levels already represents a huge effort. Further demands on the limited Community budget, coupled with the limited effectiveness of RTD's ability to address market failures, mean that, whilst it is an appropriate tool for supporting medium- to long-term technology development and deployment, it is not a tool with the power to solve the full range of market failure problems that will have to be tackled if the Community is to reach an ambitious renewable energy objective.

Conclusion

While non-regulatory measures such as voluntary agreements, consumer information and RTD can play a helpful role, it is clear that they cannot be expected, on their own, to lead to the achievement of a renewable energy share compatible with the ambitions expressed. To do this, a regulatory approach is needed. The option of a purely non-regulatory approach is therefore not considered further in this impact assessment.

Feasibility of ensuring that prices in the European energy sector internalise external costs and benefits

Current energy prices do not fully take into account the external costs of conventional and nuclear energy and the benefits of renewable energy. A system of pricing that correctly reflected these externalities could be expected to lead to a significant increase in the share of renewable energy.

Traditional fossil fuels (coal, oil and natural gas) exhibit the highest external costs for electricity-generating technologies. Non-internalisation of external costs is a clear market failure. It provides an implicit subsidy to conventional energy. Explicit subsidies are also provided to many segments of the energy sector (for example, through low tax rates or state aid)[18]. Figure 1 shows the distribution of energy subsidies in the EU15, which were estimated at around €29 billion in 2001, most of which is directed towards fossil fuels. Such subsidies distort prices and thus impede the efficient allocation of resources.

Figure 1 Indicative estimate of the distribution of energy subsidies in the EU-15, 2001

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Source: European Environmental Agency (EEA), 2004

The corresponding "corrective" policy option is to "get the prices right". In the context of renewable energy, a range of such instruments have been tried or are in place.

Europe has taken some steps towards internalising external costs of fossil energy. Member States levy taxes under the Energy Taxation Directive[19] (CO2 taxes, energy taxes or excise duties) and are also allowed to exempt renewable energy sources from taxation (should they in certain cases become taxable under the Directive). In a similar way the European Emissions Trading Scheme (EU ETS) establishes a price for carbon. Such efforts are ongoing, but progress is slow, and insufficient to achieve the Community's renewable energy objectives. Further progress could nevertheless be achieved by reviewing the European Emissions Trading Scheme.

In the absence of correct prices and a well functioning market, subsidies or other support measures are commonly used to level the playing field. Such measures, generally applied at Member State or regional level, tend to be sector-specific.

Options for renewable energy targets

Setting targets for meeting objectives is a common means of establishing a policy framework. Targets serve as a public commitment on the part of the government or other authorities to maintain a certain policy stance, which will form the basis of and justification for a range of implementing measures. These commitments, when credible, can provide stability to the sector and give the industry more confidence to invest in and develop a market.

Targets have been used since 1997, when the Community began working towards an overall, "political" target of 12% renewable energy in 2010. This was implemented by a sectoral target laid down in law to achieve a renewable energy share in electricity consumption of 21% by 2010[20] and a biofuel share in petrol and diesel consumption of 5.75% by 2010[21].

The chart below gives a picture of the rates of development in the different renewable sectors. It shows how the use of renewable energy has progressed rapidly in electricity, where the EU introduced a system of national indicative targets and a requirement to take appropriate steps to achieve them. Progress has clearly begun in renewable energy in transport, following the introduction of a European system of national indicative targets (but without a requirement to take appropriate steps to achieve them). By contrast in heating and cooling, which is not covered by European legislation, little progress has been made.

Figure 2 Growth of renewable energy in the electricity, heat and transport sectors (Mtoe), 1990-2004[pic]

Source: Eurostat

The developments outlined above, notably the variations in progress between sectors, suggest that targets have had a positive effect where they have been used. It is therefore appropriate for this impact assessment to examine whether the EU should retain a system of sectoral targets for 2020, or set a single target for all types of renewable energy. This question is addressed in section 5.2.2.

ANALYSIS OF IMPACTS

Impact of different levels of renewable energy use: business-as-usual vs. ambitious share in 2020

The starting point for this part of the impact assessment is scenarios developed using two models: PRIMES (designed to analyse developments across the whole energy sector) and Green-X (giving more detail on renewable energy). Annexes 1 and 2 describe the methods they use.

A business-as-usual scenario was developed using each model. In addition, three scenarios were developed, each with an overall share of 20% in 2020, but with a different breakdown of renewable energy between sectors. The three different "20% renewable share" scenarios are:

1. A PRIMES " high renewables and efficiency " scenario[22], under which a 20% share is achieved by reinforcing renewable energy above all in the sectors that are already subject to Community legislation (electricity and transport). In this scenario, renewable energy is developed more slowly in heating and cooling.

2. A Green-X " least cost " scenario, which focuses on cost variations between renewable energy technologies and the countries in which they are deployed. It is assumed that cost optimisation is done across all sectors (heat, electricity and transport), all technologies and all countries.. This results in a higher share of renewable energy in heating and cooling than the PRIMES high renewables and efficiency scenario; a lower share of renewable energy in transport; and a similar share in electricity generation[23].

3. A Green-X " balanced " scenario, designed to illustrate a case in which the potential of renewable energy is fulfilled through similar efforts across each sector and across technologies. This results in a share of renewable energy in heating and cooling that is higher than in the least cost scenario; a share in transport that is similar to the PRIMES high renewables and efficiency scenario; and a smaller share in electricity generation than in either of the others. A variant of the balanced scenario is used to illustrate the effect of excluding the most expensive electricity generation technologies from the mix.

Although the three 20% renewable share scenarios provide for exploring the impacts of differing the mix of renewable energy, in this part of the impact assessment, the main focus is on understanding the range of impacts that can be expected if business is left to proceed as usual, as compared with setting a target of a 20% renewable energy share. Annex 3 contains a summary of the main costs and benefits under the three specific scenarios developed for the impact assessment.

Feasibility

Renewable energy offers an increasingly credible alternative to conventional energy sources in all energy-using sectors. Nevertheless, it would be unwise for the EU to commit itself to the 20% objective, tripling the contribution of renewable energy over less than 15 years, without reviewing whether this is really an achievable result. It is frequently suggested, for example, that the limited availability of biomass, the limited ability of the electricity system to absorb variable power, or the slow turnover in equipment and long lives of power stations are constraints on ambitious renewable energy scenarios.

The question of feasibility has two main elements:

(1) Will enough biomass be available?

(2) Can the electricity system cope with the necessary volume of variable power?

Biomass

Among the "20%" scenarios, the highest biomass contribution anticipated is 230 Mtoe. This includes a maximum of 63 Mtoe that would have to come from agricultural crops (if all biofuel's contribution had to come from first-generation biofuels).

On the conservative assumption that 15% of the biomass used is imported[24], the contribution that would have to come from the EU would be a maximum of 195 Mtoe. The Green-X and PRIMES models are both built on a thorough assessment of biomass availability. To check this a comparison was made with the European Environmental Agency's estimate, which show that the EU-25 will be capable in 2020 of supplying 235 Mtoe of bioenergy, including 96 Mtoe from agricultural crops, without environmental damage[25]. Since this EU-25-based assessment does not take into account Romania and Bulgaria, which both have low domestic energy consumption and a high potential for bioenergy production, it can be concluded with some confidence that biomass requirements are not a feasibility constraint on any of the scenarios.

Electricity from variable sources

It is sometimes suggested that the limited ability of the electricity system to absorb wind power and other forms of variable power is a constraint on ambitious renewable energy scenarios.

Under the PRIMES high renewables and efficiency scenario and the Green-X balanced scenario, variable power is expected to contribute around 15% of the total electricity supply. Under the least cost scenario, this figure rises to 18%[26]. There are a number of comprehensive national studies, some ongoing, related to the feasibility and cost of wind integration into the European power market[27]. These studies show that it is technically possible to integrate these levels of variable power use into the power market.

Although technically feasible, the limits of integrating large amounts of variable capacity in power systems will depend on socially and economically acceptable costs. Keeping in mind the stochastic nature of demand and supply in the grid systems, the power market has been designed to cope with varying and uncertain demand plus unexpected outages. The power market can be further developed to cater for greater use of variable energy with its characteristic fluctuations. Greater use of variable energy is therefore a question of economics and regulatory rules rather than of technical constraints. As stated in COM(2005) 627 The support of electricity from renewable energy sources , the following issues are especially important to increasing the penetration of wind energy into the grid system: forecasting wind production, the timing of gate closure of the spot electricity market and the charging of balancing costs. Intelligent design of the market system and regulatory rules would help wind energy to be included appropriately as well as reduce the costs of integrating larger shares of variable energy into the power market.

Investment cycles and the speed of uptake of renewables

The PRIMES and Green-X models simulate the growth of different technologies in all three energy sectors. They start with the existing energy capital base and simulate its evolution based on the costs of the different technologies and the rate at which the technologies can be replaced[28]. Thus, for both the business as usual and the 20% scenarios, the lifetimes and investment cycles of the sector are reflected in the analysis.

Costs

In the absence of a full internalisation of external costs and benefits, most forms of renewable energy cost more than the conventional alternatives. The difference is expected to narrow but not disappear by 2020. It follows that the cost of the "20%" scenarios will be higher than that of the business-as-usual scenarios. The models have investigated investment needs and additional production costs[29] for renewable energy under the different scenarios.

Under Green-X, the cumulative investment needed to increase the share of renewable energy consumption from 6.5% in 2005 to 12.6% in 2020 is projected to be €317bn (business-as-usual case). The cumulative investment needed to increase the share from 6.5% in 2005 to 20% in 2020 is in the range of €600–670bn (€2005).

Under PRIMES, which works in detail with the electricity sector, investment needs in this sector are calculated to be about €160bn in the business-as-usual case (renewable share across all sectors: 10.4%) and some €280bn to reach 20% by 2020 in the PRIMES high renewables and efficiency scenario. In comparison, the Green-X model projects, for the power generation sector, an investment cost of €232bn for renewable energy in the business-as-usual scenario and a range of €285–414bn in the 20% scenarios. In the Green-X scenarios, the cumulative additional production costs for new renewable energy plants in the period 2005-2020 are around €123bn under business-as-usual conditions, compared with a range of €210–290bn (with energy price assumption of $48/bbl) in the "20%" scenarios.

Both models provide the additional production cost in the year 2020 of achieving a 20% share for renewable energy. They give additional production costs in a range of €24–31bn[30] in the year 2020. The PRIMES model estimates the lowest cost (€24bn) and the Green-X model the highest costs (€26bn for the least cost scenario, which is based on a cost minimisation approach and brings into the market the cheapest renewable technologies, and €31bn for the balanced scenario, which reflects the need to develop a portfolio of longer-term technologies). The difference between the cost of the balanced scenario and that of the least cost scenario is due in large part to the different assumed shares of more innovative (and expensive) renewable energy technologies. Under a variant of the balanced scenario with a lower share of innovative technologies in the electricity generation sector, the additional production cost in 2020 would fall to €26bn, the same as for the least cost scenario. When looking at the cumulative additional production costs for the whole period, these will still be lowest in the least cost scenario (€210bn). However, the variant of the balanced scenario shows that lowering the share of innovative technologies will lead to a reduction of the cumulative additional productions costs from €290bn, which has been projected under the balanced scenario, to €250bn.

The additional production costs are highest in the year 2020 due to the costs rising over time as more renewables are developed. The average yearly additional production costs are projected to be €8bn in the business-as-usual case, compared to the range of €13–18bn in the policy case in the period 2005–2020.

Table 1 Comparison of cost ranges of achieving a business-as-usual share of 13% versus achieving a 20% share of renewable energy in 2020[31]

€bn | Renewable energy share of 13% in 2020 | Renewable energy share of 20% in 2020 |

Cumulative investment needs for renewable energy in the period 2005–2020 | 317 | 600–670 |

Cumulative additional production costs in the period 2005–2020 Sensitivity case: high energy price (oil price of $78/barrel instead of $48) | 123 - | 210–290 125[32]–170 |

Average additional production costs per year | 8 | 13–18 |

Additional production cost in the year 2020 Sensitivity cases: 1) high energy price 2) high CO2 price[33] (emissions allowances at €50/t instead of €20/t) 3) combined high energy price and high CO2 price[34] | 13 - - | 24–31[35] 0-11 14–21[36] 5 |

Source: Green-X, PRIMES

The costs are influenced by key parameter assumptions, such as energy and CO2 price assumptions. A sensitivity analysis of changing these assumptions was undertaken in the least cost and balanced scenarios. The sensitivity analysis looked at how the cumulative additional production costs for the period 2005-2020 and the additional production costs in the year 2020 are affected by increasing the energy and CO2 prices. The energy price assumptions in these two scenarios are based on an oil price of $48/boe in 2020). A sensitivity analysis shows that high energy prices with an oil price of $78/boe in 2020 reduces total additional production costs by 41%, to a range of €125–170bn and the additional production costs in the year 2020 to an annual amount of €0–11 billion.

The sensitivity analysis also shows that costs are highly sensitive to CO2 allowance prices. In the Green-X balanced scenario, results show that increasing the allowance price from €20/t to €50/t will lead to a reduction of additional production costs by 31%. This implies additional production costs in the range of €14–21bn instead of €24–31bn in the year 2020.

These cost estimates take into account the fact that the unit costs of renewable energy, like other innovative technologies, tend to fall over time as practitioners gain experience. If the volume of use of a particular technology grows more rapidly, experience will be gained more rapidly and costs will fall more rapidly. The chart illustrates the estimated rate of unit cost reduction forecast in the balanced scenario in the electricity sector.

Figure 3: Estimated rate of unit cost reduction for renewable electricity generation technologies.

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Source: Green X balanced scenario

Benefits

GHG emissions

The PRIMES and Green-X models allow changes in the level of CO2 emissions to be calculated[37].

According to the Green-X model, increasing the share of renewable energy from 6.5% in 2005 to 12.6% in 2020 (business-as-usual scenario) would lead to CO2 emission savings of 430 Mt in 2020[38], whereas an increase from 6.5% in 2005 to 20% in 2020 would lead to an annual saving in the range of 700–900 Mt of CO2 emissions in 2020. Under PRIMES, the equivalent saving can be estimated at just over 600 Mt CO2 emissions[39].

The range of emission savings, from 600–900 Mt CO2, in the two models, can be explained by the variation of technologies used and the different shares of electricity, heat and biofuels considered under the different scenarios. Higher CO2 savings in the least cost scenario compared to the balanced scenario can be explained by the fact that the latter scenario uses more renewable energy in the heating sector and less in the electricity sector. The average emissions factor is higher for the electricity sector than it is for the heat sector.

Air quality

The relevant air pollutants include the following: sulphur dioxide (SO2); nitrogen oxides (NOx); non-methane volatile organic compounds (NMVOC); and particulate matter (PM)[40]. Replacing fossil-fired electricity generation with renewable energy has generally favourable air quality effects, especially where the fuel replaced is coal. Replacing conventional transport fuels with biofuels has minimal air quality effects, because of the strong controls on pollution from road transport. Replacing conventional heating with biomass heating can have an adverse air quality effect if poor quality equipment is used.

Security of supply

In addition to concerns about environmental sustainability, securing energy supply is one of the major reasons for promoting the development of renewable energy sources. Increasing the share of renewable energy by 2020 will have an impact on fossil fuel demand and fossil fuel imports. This can be expected to have beneficial effects on security of supply. It is difficult to put a value on these benefits.

Oil is the fuel posing the most serious security of supply problems, especially in transport. A scenario with higher biofuel shares would do most to address the EU's most serious security of supply problem. Security of supply is also an important issue in the heating sector, given its high reliance on oil and gas, and as is the case with the biofuels sector, albeit to a lesser extent, a scenario with a higher share of heating and cooling based on renewable energy improves security of supply.

The Green-X and PRIMES policy scenarios show that increasing the share of renewable energy consumed would enable the EU to avoid consuming fossil fuels in the range from around 234–300 Mtoe/year[41] from 2020, including approximately 200 Mtoe per year of imported fuels. Oil imports from the Middle East and CIS can be expected to be at least 50 Mtoe lower[42].

In monetary terms, calculated in the Green-X model, increased capacity for renewable energy production in the period 2005–2020 would account for around €50–57bn per annum in fossil fuels avoided[43]. High energy prices (an oil price of $78/boe in 2020) would increase the value of fossil fuel avoided over the period by 40-57%.

Employment, GDP and export opportunities

PRIMES and Green-X are partial equilibrium models focusing on the energy sector. This means that they fail to provide sufficient analysis of the wider economic effects of increasing the share of renewable energy. Additional studies which focus on the GDP and employment implications of increasing the share of renewable energy were commissioned. These studies have been performed by feeding energy portfolio results from PRIMES, GREEN-X, POLES or the ESIM agricultural model into models of the whole economy. They take into account, inter alia, the price changes that can be expected to result from the promotion of renewable energy.

The ASTRA model was used to assess the employment and GDP impact of the achievement of a 20% renewable energy share[44]. It aggregated the effects in all three energy sectors. Details are given in annex 4. It found that GDP would be a little more than 0.5% higher than under business-as-usual conditions[45] and that employment would grow by around 0.3%, which amounts to about 650 000 additional jobs[46].

In order to assess the wider economic effects of increasing the share of renewable energy in the electricity sector, model runs combining the energy system model POLES and the extended version of the general equilibrium model PACE were carried out[47]. Details are given in Annex 6[48]. The model was used to assess the welfare[49] impact of achieving a 35% share of renewable energy in the electricity sector in 2020 through the introduction of a subsidy for renewable energy[50]. The estimated impact was a welfare loss of 0.05%.

For biofuels, the Commission's Institute for Prospective Technology Studies (IPTS) constructed an input-output (I/O) model, using inputs from the Commission's ESIM agricultural market model, to estimate the GDP and employment effects. Details are given in Annex 5. Achieving a 14% share of biofuel by 2020, if primarily through domestic production, was estimated to lead to employment in the EU being up to 144 000 higher than it would otherwise have been – assuming that oil behaves like other commodities, so that changes in demand affect its price. (If changes in demand for oil are assumed to have no effect on its price, the figure would be -32 000). On the same assumption, EU GDP would be an estimated 0.23% higher than it would otherwise have been.

These figures are in line with current realities. The European Union is already the global leader in renewable technologies, which account for a turnover of €20 billion and employ 300,000 people[51]. Further opportunities to create employment will arise from the export of renewable energy technology.

An active renewable energy policy creates substantial potential for European manufacturers to export this technology, which would grow with domestic production. Annex 7 considers this question.

Biodiversity

Climate change is the major threat to biodiversity in the world today. After energy efficiency and energy savings, renewable energy is one of the most effective tools available to tackle climate change. While no comprehensive indices of biodiversity exist, it is certain that the effect of the "20%" scenarios on biodiversity is substantially positive, relative to business-as-usual conditions.

Nevertheless, it is important to take into account the fact that energy production facilities – both conventional and renewable – can have substantial local biodiversity impacts, independent of the positive global impact of renewable energy.

Careful study of two prominent forms of renewable energy – wind power and biofuels – shows that in general, the biodiversity effects of their production processes, while present, are minor. In both cases it is possible to avoid production processes which have a negative biodiversity impact: for example, avoiding siting wind turbines in locations through which migrating birds are obliged to pass, or avoiding felling rain forest to permit the production of palm oil to make biodiesel[52].

The biodiversity impact of a 20% renewable energy share is substantially positive, even if it is assumed that conventional energy production has no biodiversity impacts. In fact, conventional energy production has substantial biodiversity impacts (oil spills are just one high profile example) which have, unfortunately, not been studied using the systematic approach that has been applied in the case of renewable energy. The risk related to these impacts will fall because a high share of renewable energy will avoid the use of equivalent amounts of conventional energy – thus reinforcing the already positive biodiversity impact of such a policy.

International aspects

The European Union is the global leader in many renewable energy technologies. In wind power, for example, it has a 60% world market share. The global wind power market is growing strongly, with significant Asian market growth (especially from India) and a strong increase in the rate of North American installations. A strong renewables policy will enable the Union to keep up its position as world leader in this market. (See Annex 7).

European demand for biomass, and especially biofuels, can contribute to improving trade relations with the European Union's trading partners, in particular with developing countries, which have potential for producing and exporting biomass and biofuels at competitive prices. Renewable energy sources offer major opportunities for job creation and rural development in developing countries. A strong renewables policy in the EU can therefore be regarded as an important tool in our policy towards developing countries. It should also be mentioned that renewable energy, especially biofuels, could represent an important negotiating element for the successful conclusion of ongoing free trade area negotiations.

Finally, it will be difficult for the EU to maintain its leadership in combating climate change if it relaxes its efforts in renewable energy.

Sensitivity analysis

It is necessary to be certain that the chosen share of renewable energy reflects an acceptable balance between the costs and benefits of reaching the target. In order to study the effect on costs and benefits of varying the share of renewable energy, a sensitivity analysis was carried out looking at the impact of achieving 16%, 18% and 22% shares, using the 20% share as a benchmark. This analysis was carried out using the Green-X least cost scenario. These effects are summarised in Table 2 below.

As the table shows, the cost of increasing the share of renewable energy grows more sharply after reaching a share of 20% renewable energy. While this is most obvious in the case of investment costs, the effect is also present in the case of additional production costs. By contrast, the benefits (CO2 emissions avoided and fossil fuels avoided) tend to grow at a constant rate, or even grow less rapidly after 20% (in the case of emissions avoided).

These data tend to reinforce the wisdom of not aiming at a share above 20%.

Table 2 Changes in costs (cumulative investment needs in the period 2005-2020) and benefits (cumulative avoided CO2 emissions and avoided fossil fuels) under changing renewable energy targets

%Δ from 20% renewable share | 16% | 18% | 22% |

Avoided CO2 emissions | - 19% | -9% | +7% |

Avoided fossil fuels | -24% | -12% | +12% |

Investment costs | -23% | -12% | +26% |

Source: Green-X (least cost scenario)

A sensitivity analysis was also carried out on the effect of changing central model parameters, such as energy prices, CO2 prices, energy efficiency and rates of technological learning.

Figure 4 Additional RES generation costs under changing parameters (average for 2016-2020)

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Figure 4 shows that the dominant factor influencing the cost of promoting renewable energy is the conventional energy price. When the oil price increases from $48/bbl to $78/bbl the additional production cost falls 99% from from €26 bn to €0 bn in the year 2020. Note that this does not necessarily mean that the 20% share of RES would be achieved at nearly no additional cost[53] nor that RES technologies would penetrate the market without any further policy support. In the case of higher CO2-prices in the electricity sector (€50 per ton), additional production costs are reduced by 43%. CO2-prices are clearly an important factor concerning the cost-effectiveness of renewable energy technologies vis-à-vis their fossil fuel alternatives.

Impact of different options for renewable energy targets

There are several choices as to the type and scope of targets. These include whether targets should be voluntary or mandatory (legally binding), sectoral or global, technology- or emissions-based. In broad terms, as described in section 4.2.3, targets form a public commitment to a certain policy stance which, when backed by implementing measures, helps create a more stable investment climate. Thus their effectiveness depends on how credible a policy environment they create.

Mandatory or indicative targets

The legal strength of a target largely determines its credibility, as stronger targets mean that efforts will be made by governments to ensure that targets are met. This in turn means that the market has greater certainty for planning and investment. Current examples include the mandatory national Stability and Growth Pact and Kyoto targets (where Member States are bound by Community legislation) and the voluntary targets of the ACEA agreement on CO2 reductions from cars. In all cases, the targets are set on the basis of best available analysis and negotiations with the parties involved.

The existing European framework for renewable energy is based on indicative targets rather than mandatory targets. In the electricity Directive (2001/77/EC) and the biofuels Directive (2003/30/EC), Member States are required to set indicative national targets based on the reference values in the annexe to the Directives. The electricity Directive requires, in addition, that Member States take steps to attain their objectives. The biofuels Directive simply states that Member States should ensure that a minimum level of biofuels is placed on the market in line with their national indicative targets. Most recently, the energy services Directive (2006/32/EC) requires Member States to adopt and aim to achieve an overall national indicative energy savings target.

The ACEA agreement on CO2, the two renewables Directives and the Community Kyoto commitment (Council Decision 2002/358/EC) illustrate the spectrum of target "strength" and at the same time illustrate the effectiveness of such targets: Member States (the EU-15) have taken a range of actions to ensure that the mandatory national Kyoto targets are met, and together with Community-wide measures (such as the ETS) every effort is being made to meet the targets. The electricity Directive, which requires action commensurate with the targets, has induced rapid growth in renewable electricity, but as this effort has been uneven across Member States, the target is unlikely be completely reached. In the biofuels Directive, neither target nor actions are mandatory, and whilst there has been some rapid growth in biofuels in a limited number of Member States, all but two have failed to take sufficient measures to achieve their targets. Finally, the voluntary agreement with industry did lead to improvements in CO2 emissions from cars, but again, the target will not be reached.

These examples suggest that an effective policy and the attainment of policy goals require mandatory targets.

Sectoral targets vs. a single renewable energy target

Another key policy choice is whether to retain the current system of national sectoral targets for renewables, or to change to a system with a single renewables target for each Member State.

Establishing a single target has an intuitive appeal. Once one target is established and support measures put in place, the market is allowed to choose technologies and other cost effective means of achieving the goal. This should result in the cheapest solution, reducing the overall cost of the policy and reducing the administrative burden associated with managing multiple targets.

However, the practice in Member States, and the consensus amongst the renewable energy industry, suggests that a single broad target is too unfocused and would fail to provide sufficient guidance and certainty to businesses operating in a specific sector of the market. In addition, a single target promotes current cost effective technologies, which could result in uneven development of the technologies in different sectors (e.g. most growth in onshore wind power). This could maximise short-term reductions in CO2 and minimise short-term costs, but could slow down technology development and innovation. Such a result could mean that abatement costs rise and the growth of new technologies, industry and jobs is delayed.

Alternatively, consideration could be given to measures designed to deliver a balanced result across sectors and technologies. Such measures could be developed with the specific conditions in the electricity, heating and cooling and transport sector in mind.

For biofuels in particular, it is clear that the market alone will do little to develop the sector. Left to choose between all renewable energies, efforts will first be directed towards electricity and heating. This is due to the high current costs and the high development costs associated with second generation technologies. And yet, progress must be made in the transport sector: it is the sector where fuel choice is negligible (oil constitutes 98% of transport fuels), where greenhouse gas emissions are growing most strongly and where fuel supply and price is least stable. In order to address these serious problems in the transport sector, stronger efforts could be made to promote biofuels, specifically. .

The staff working paper[54] accompanying the biofuels progress report[55] looks into the consequences of biofuel shares of 7% or 14% for 2020. It shows that these shares are achievable in agricultural terms. It also shows that they have positive environmental effects (in terms of greenhouse gas savings) and neutral or positive economic effects (in terms of employment, GDP and security of supply), and that the positive effects of the 14% scenario are greater than those of the 7% scenario.

The three overall renewable energy scenarios examined here (Green-X least cost, Green-X balanced and PRIMES) judge the optimum renewable energy share at 12, 14 and 15% respectively within an overall renewable energy share of 20%.

There is therefore good reason to believe that – on present knowledge – the optimum share of biofuels in 2020, within an overall renewable energy target of 20%, will be in the region of 14%.

However, in fixing a minimum target, and one which should be binding, a more cautious approach should be adopted.

In particular, it should be taken into account that a 14% share will only turn out to be the optimum share if two conditions are fulfilled.

First, achieving the 14% share will require several million tons of imports of vegetable oil for the production of biodiesel. As in the European Union, imported vegetable oil can be produced in compliance with environmental standards; but it is also possible to produce it in ways that have negative environmental effects, especially if land harbouring diverse natural ecosystems is converted to crops for biofuel production. As stated in the renewable energy roadmap, the Commission intends to bring forward a proposal for the differentiation of biofuel support. One effect of this proposal should be to minimise the risk of such environmental damage happening. But no such system is yet in place. In the light of this, it makes sense to aim in the minimum target at a level of first-generation biodiesel consumption that would not require the use of significant amounts of imported palm oil. This is consistent with first-generation biodiesel consumption of 9.7 Mtoe.

Second, achieving the 14% share will require new measures to ensure that biofuels can enter the fuel market. This can be done in three ways:

a) Increase the amount of low-blend biofuel that may be blended in ordinary fuels

Current rules limit biofuel content to 5% ethanol in petrol and 5% biodiesel in diesel (both by volume). The European Standardisation Committee (CEN) is already working on a Commission mandate for a 10% share of biodiesel – sufficient to accommodate the volume of biodiesel referred to above. In ethanol, the highest blend currently available on a mass market is the 25% blend used in Brazil. It would make sense to limit the minimum target to the quantity consistent with a lower (20%) blend. This implies ethanol consumption of 18.8 Mtoe.

b) Increase take-up of special vehicles capable of using high-blend or pure biofuel, and of the special pumps needed to serve them

This could potentially be a powerful strategy. However it is more difficult to put into practice than a strategy based on low blends. It should not form part of the minimum target.

c) Bring Fischer-Tropsch diesel (BTL) to market

Efforts to develop second-generation biofuels focus on BTL and cellulosic ethanol. Cellulosic ethanol is chemically identical to first-generation ethanol and does not open up new ways for biofuel to enter the fuel market. By contrast, BTL is superior both to biodiesel and to existing diesel, and is not caught by rules limiting biofuel content in diesel. This is one of the reasons why BTL is supported through Community research programmes, and why efforts need to be intensified to bring it to market.

However, BTL is not as close to market as cellulosic ethanol. Numerous technical problems remain to be solved. While substantial progress can be expected by 2020, it would not make sense to base the minimum target on more than a small contribution from BTL. The suggested amount is 2.5 Mtoe.

It should be taken into account that, as shown in the staff working paper on biofuels[56], a 14% share would lead to higher prices for agricultural commodities in the EU unless accompanied by charges to the rules for governing agricultural markets in the EU and internationally.

This analysis suggests that the minimum target for biofuels in 2020 should be approximately 31 Mtoe, equivalent to a 10% share of the petrol and diesel market.

Based on interpolation from the effects of a 7% and 14% share, the approximate impact of this target could be annual extra average costs of €5.3bn, reduced oil imports from Middle East and CIS rising to 31 Mtoe in 2020, extra employment in the EU rising to 120 000 and greenhouse gas emission reductions rising to 68 MtCO2eq. Finally, GDP benefits would be expected to reach 0.17%.

Renewable energy targets or CO 2 targets?

Energy policy has been developed so far with overall CO2 targets under the Kyoto Agreement, together with targets for energy efficiency improvements and sectoral renewable energy targets, to speed up the development of technologies that address our security of supply concerns as well as climate change. This separate and distinct effort in one part of the energy sector has also been driven by the need to kick-start technology development from a very low base.

If climate change and CO2 emissions were the sole goal of energy policy and the renewable energy sector were a mature and well-functioning market, then a single CO2-based target (reached through ETS for example) would be an appropriate policy approach. But this situation is still a long way off. Until the renewable energy sector becomes a mainstream component of the energy sector, is able to compete fairly in the internal energy market and is of a scale large enough to develop and deploy new technologies, it requires targets and specific policies of its own.

COMPARING THE OPTIONS

Comparison of options for levels of renewable energy use

The impacts described in section 5.1 can be summarised as follows:

The EU's total energy bill is expected to be about €350bn in 2020. The annual cost of achieving a 20% share of renewable energy is likely to reach €24-31bn per year in 2020 (€2005). This cost will vary with the mix of renewable energy technologies used and, to a greater extent, with the conventional energy price. For example, under the balanced scenario with a barrel of oil at $78 (rather than $48), the extra cost of renewable energy would fall to €11bn.

In exchange for this cost, the EU would obtain major benefits.

Annual greenhouse gas emissions would be 600-900 Mt lower. From the point of view of security of supply, fossil fuel consumption would be 235-300 Mtoe per year lower in 2020, including 50-55 Mtoe less oil imports from the Middle East and CIS, and 85-90 Mtoe less gas imports from those regions. This energy would mostly be replaced by domestically produced renewable energy. There would be substantial biodiversity benefits.

Some studies estimate that this strategy would lead to a GDP increase, others to an even smaller decrease. In all studies, the effect would be small (0.5% or less). Estimations show that there would be a small net increase in employment. The EU would be well placed to maintain its leading role in renewable energy research, and would continue to benefit from opportunities for renewable energy technology exports.

Comparison of options for renewable energy targets

The question of the type and scope of targets in section 5.2 can be summarised as follows: The legal strength of a target largely determines its credibility, as stronger targets mean that efforts will be made by governments to ensure that targets are met. This in turn means that the market has greater certainty for planning and undertaking investments. This clearly favours mandatory over indicative targets.

Whilst setting a single target has an intuitive appeal and gives the market flexibility to choose a cost-effective way of deploying technologies, sectoral measures are likely to be needed to create the forward-looking confidence needed to induce investment in a broad range of renewable energy sources. This is particularly important for biofuels if Europe is to achieve any reduction in fossil fuel consumption, emissions and import dependency in the transport sector.

Based on this analysis, and depending on the political weight placed on these various factors, the recommendation is to combine the EU's future renewable energy target with a sectoral target of 10% for biofuels.

MONITORING AND EVALUATION

The core indicator of progress towards meeting the objectives is data on the quantity and share of renewable energy consumed.

This will be obtained by way of national reports and data collected by Eurostat. As national reporting requirements by Member States and data collection by Eurostat are already in place, the additional administrative costs this will generate are not considered to be of any important size. Progress towards the overall objective of promoting renewable energy will be evaluated every two years as part of the regular series of Strategic European Energy Reviews.

Annex 1: PRIMES methodology and modelling approach

Overview of the PRIMES model

The PRIMES model is a modelling system that simulates a market equilibrium solution for energy supply and demand. The model determines the equilibrium by finding the prices of each energy form such that the quantity producers find best to supply matches the quantity consumers wish to use. The equilibrium is static (within each time period) but repeated in a time-forward path, under dynamic relationships.

The model is behavioural, but it also represents in an explicit and detailed way the available energy demand and supply technologies and pollution abatement technologies. The system reflects considerations about market economics, industry structure, energy/environmental policies and regulation. These are conceived so as to influence the market behaviour of energy system demanders and suppliers (agents). The modular structure of PRIMES reflects a distribution of decision-making among agents who make individual decisions about their supply, demand, combined supply and demand, and prices. Then the market integrating module of PRIMES simulates market clearing.

The PRIMES model is a general-purpose energy system model. It is conceived for forecasting, scenario construction and policy impact analysis. It covers a medium to long-term horizon. It is modular, making it suitable either for unified model use or for partial use of modules to support specific energy studies.

Modelling of Renewables Penetration

Assumptions for baseline scenario

The PRIMES baseline energy scenario up to 2030 reflects trends under the current state of knowledge of policies in place and in the process of being implemented, restructuring in progress and improved energy technologies. Renewable energy technologies are assumed to improve over time under the baseline scenario. This improvement (reduced capital and fixed costs, higher efficiency rates and lower biomass fuel costs) reflects both “autonomous” technical progress (as a result for example of long-term research effort) and “endogenous” progress, the latter driven by what is expected to be an increasing volume of new installations in response to high energy prices and a growing concern for the environment (under baseline trends).

Regarding the numerical assumptions about technological progress of renewables under the baseline scenario, the PRIMES model relies on a technology dataset prepared under a series of research projects[57] which collected detailed techno-economic data per technology and linked these data with historical explanatory drivers, such as research effort, deployment in scale, and past investment. It is known that for energy technologies, the correlation between technology progress and the changing pattern of explanatory drivers is at a global market level and not on a local or even a regional level. For this purpose, the research projects have mostly used world energy market models (such as Poles[58], Prometheus[59] and Message[60]) to prepare baseline and alternative scenarios under which technology progress has been made partly endogenous, linked with research spending and cumulative investments.

Consequently, in preparing the baseline scenario, the PRIMES team used the techno-economic dataset on future technology progress that corresponds to an “average” world baseline scenario, as conventionally used in the above research projects. As it happens, this dataset coordinated well with the results of the Prometheus energy model, which is linked to Poles model projections and fully incorporates endogenous energy technology progress, in addition to autonomous progress.

The dataset used for the PRIMES baseline scenario involves substantial technology progress for renewable energy, as summarised in the table below:

Table 1: Capital Cost of Energy Technologies assumed for the PRIMES baseline

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Of course, the competitiveness of a particular technology for power generation depends not only on capital cost but also on the price and availability of the resource used as input (e.g. biomass cost, availability of wind and solar energy) as well as on heat efficiency, in the case of combustion. When considering those factors, technical progress in terms of capital cost places wind power in the top ranks of competitiveness, a trend which is in line with expectations of the massive deployment of wind power under the baseline scenario. In other cases, like solar photovoltaic, the impressive improvement in terms of capital cost is not enough to generate massive deployment until 2030 under baseline trends. The same holds true for fuel cells and partly for new biomass, because their deployment crucially depends on the conditions and infrastructure of supply of input fuels (crops and hydrogen respectively); regarding infrastructure, the baseline scenario involves provides for little development until 2030.

High renewable energy scenarios

Basic Methodology

A High Renewable Energy Scenario for the EU aims at quantifying the energy system implications of imposing a certain mandatory numerical target for the penetration of renewable energy in the EU energy system at a future time.

Supposing that the quantum and the time dimension of the target are set for the EU as a whole, the PRIMES model is used to work out the target per EU country and sector of activity (electricity-steam generation, transport sector, other final demand sectors). According to economic theory, optimality is obtained by specifying different target levels per country and sector in such a way that every country and every sector faces the same level of marginal cost associated with its individual target, and at the same time the target levels are such that globally the EU achieves the overall target for renewables penetration.

To do this calculation with the PRIMES model we need to do iterative model runs. Instead of imposing directly an overall target for renewables, it is assumed that a certain positive monetary value is associated with any unit of energy produced by a renewable energy source. A monetary value does not involve direct payment transfers (as taxes would do), but its presence alters the economic optimality calculations of the agents (either demanders or producers of energy). This monetary value could be interpreted as a “virtual” subsidy and appears in calculations as a negative unit cost (or a positive unit gain). In mathematical terms the monetary value is the dual variable associated to the global quantity constraint, signifying the marginal value in terms of global system costs of relaxing the quantity constraint by one unit. Let us call this monetary value a “renewables value”[61]. Being a virtual subsidy, the renewables value does not make energy cheaper, but merely influences the optimum fuel mix as considered by each economic agent. Compared with the baseline scenario, energy would generally become more expensive, since the presence of the renewables value alters the optimality of decisions in just the same way as if the global renewables quantity constraint was added.

Model iterations proceed as follows: start with a certain level of renewables value, insert this value into the economic objectives of each agent and each country as in the model, and run the model for all EU countries; calculate the level of overall penetration of renewable energy in the EU and compare with the target for the EU; if total renewables quantity is below target, then increase renewables value, otherwise decrease; repeat this cycle and compare against target until convergence is achieved. At convergence, the resulting scenario is identical to the baseline except for the renewable value. By comparing with baseline, it is possible to assess the energy systems implications of the overall EU renewables target. The marginal cost of the renewables target is exactly equal to the renewables value; if plotted against different levels of targets, a marginal renewables penetration cost curve is obtained. Total energy system cost under the high renewables scenario is expected to be higher than at baseline, as explained above.

As usual, the comparison of renewables scenario to baseline will provide details about how incremental renewables penetration is realised and which of other energy forms and technologies are displaced; details are given by country, sector and technology.

ENDOGENOUS TECHNOLOGY PROGRESS in the Renewable Scenario

If, in the high renewable scenario, techno-economic and fuel supply assumptions were the same as for the baseline scenario, there would be no additional technology progress. This means that despite the higher deployment of renewables, unit costs of renewable technologies remain to the same as the values in baseline.

One argument against this assumption is that the higher deployment of renewable technologies may facilitate greater technology progress (lower capital costs and higher efficiency) as compared with baseline trends. This would make it possible to achieve the renewables target at lower overall costs, hence yielding a lower renewables value.

This is usually called technology learning-by-doing. If these effects are linked to certain model variables, such as levels of deployment of a technology, the learning is called endogenous. Because of modelling and measurement difficulties, this effect usually includes increasing returns to scale, which might be attributed to the scale of industrial deployment rather than to learning. Within the research projects mentioned above, a series of learning-by-doing curves[62] for individual and clustered energy technologies have been estimated econometrically on a historical time series.

Available learning curves relate capital cost reduction and heat efficiency improvement to total installations of an energy technology on a global geographical scale. Therefore, expectations concerning higher penetration of an energy technology, for example in a scenario like the high renewables one, drive technological progress, which further facilitates technology penetration and reduces the marginal costs of policy targets.

To do this on a European scale, we have to bear in mind that the EU market accounts for quite a small fraction of the world market for an energy technology. Therefore to see significant effects of learning-by-doing, we have to combine the EU scenario with assumptions about the penetration of energy technologies at world level. For the baseline scenario this has been done, as explained before, by considering technology improvement trends on a global scale, through linking the PRIMES model with Poles and Prometheus baseline scenarios.

For the EU high renewables scenario, two options are possible: a) assume that on a global scale too, a high renewables scenario will take place and so consider total world installations as a driver of learning-by-doing on top of the baseline scenario; b) assume that high renewables policies are put into effect unilaterally at the EU level, so additional learning-by-doing effects would be somewhat limited in magnitude. In the PRIMES modelling in this exercise, the second option has been chosen so as to avoid overestimating the learning-by-doing effects . Although renewables action is assumed in this analysis to take place only in the EU, the rest of the world benefits from economic spillovers driven by lowering energy technology costs, leading other countries (albeit less so than in the EU) to invest more in renewable technologies. The Prometheus model was used to estimate the additional reduction of capital cost driven by a high renewables scenario for the EU. The results were used for the PRIMES modelling of the high RES scenario.

Endogenous change of biomass supply infrastructure in the high renewables scenario

A fuel supply curve relates the price or unit cost of a fuel to total actual or expected demand. To obtain market equilibrium, either directly through matching supply against demand or indirectly through optimisation, the fuel supply curve must have an upward slope at least beyond a certain level of demand. This feature can be justified by exhaustion of a resource, or simply related to the economic mechanisms of supplying demand by using cheaper supplies before more expensive ones.

Such a mechanism is already incorporated into the PRIMES model not only for energy resources but also for all action of a cumulative nature, like investment to make up for exhausted sites or non-linear risk factors.

For various biomass fuel types, the current PRIMES model and dataset use fuel supply curves per country that are calibrated to the data provided by ECN and are derived from a detailed bottom-up model which calculates the potential of renewables per country under various economic and technical assumptions.

On this basis, PRIMES includes biomass supply curves and similar curves for other renewables. Beyond a certain quantity of biomass these curves are upward sloping; this means that under a high renewables scenario, prices and costs of biomass will be higher than under baseline. However, there are also arguments that under a high renewables scenario the prices of biomass might be lower than in baseline. These arguments refer to the development of large-scale infrastructure needed to collect and prepare/condition biomass, in particular where the biomass market is a big one. Within a high renewables scenario, ambitious expectations about the size of future markets for biomass might drive significant investment in large-scale infrastructure for collecting and conditioning biomass products, generating a considerable reduction in biomass unit costs, given that the costs of collection and product conditioning are significant elements in the final price of biomass. The unit cost of land use for biomass will be upward sloping with higher biomass quantities, as more productive land is expected to be used first before less productive land is deployed. Moreover, the price of biomass might include higher rents or opportunity costs when suppliers serve increasing demand. However, under a high renewables scenario it is possible that the cost-lowering effect due to massive new infrastructure will dominate over other factors, causing price and costs to increase with the higher demand for biomass.

This mechanism is illustrated in the following diagram, which is also relevant for other resources, like oil/gas exploration and production, or new technologies like hydrogen. The diagram shows that although the short/medium-term supply curves (with generally constant basic infrastructure) are upward sloping, the long-term supply curve, which incorporates the effect of massive new infrastructure, might be downward sloping, at least up to a large demand quantity.

Figure 1: Fuel supply curves (first graph as in PRIMES, second graph including the effect of new infrastructure)

By taking into account the above long-run supply curve for biomass, in other words by considering exogenously the building of large-scale biomass infrastructure in the high renewables scenario, we obtain a fuel cost reduction effect similar to the learning-by-doing effects explained above. This is equivalent to assuming that in the high renewables scenario, renewables policy is backed by appropriate agricultural policies (land use, subsidies and financing of infrastructure development) that help biomass supply to attain industrial maturity. Clearly, this assumption will further facilitate the penetration of biomass into the high renewables scenario and induce a lower renewables value (i.e. the marginal cost of achieving the renewable target). This approach has been followed by allowing for a shift to the right of the biomass supply curves, yielding a somewhat conservative estimate of the cost-lowering effects of more substantial biomass penetration.

Annex 2: Green-X methodology and modelling approach

The Green-X model facilitates a comparative, quantitative analysis of interactions between RES, conventional energy and combined heat and power (CHP) generation, demand-side management (DSM) activities and CO2-reduction, both within the EU as a whole and for individual Member States. The model forecasts the deployment of RES under various scenarios in terms of supporting policy instruments, the availability of resources and generation technologies, and energy, technology and resource price developments.

The Green-X model matches demand and supply of energy sources. Demand is based on the EU energy outlook[63]. Supply is described by means of a cost-resource curve build up in two parts:

- A static cost-resource curve that describes the relationship between available technical potentials and the corresponding costs of utilising this potential.

- A dynamic cost-resource curve, which is based on the static cost-resource curve but incorporates such dynamic parameters as technological change (using the concept of experience curves or expert judgment) and the dynamic barriers to implementation, determining the yearly available RES potential. The dynamic curve is endogenous to the model and is determined annually.

The figure provides an overview of the Green-X model.

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Figure 2 Overview of the computer model Green-X (electricity sector)

The Green-X model calculations are complemented by simulations from the Green Net model that determines the additional costs for system operation and grid extension resulting from variable RES-E.

Modelling approach

Least-cost scenario

The key approach in the modelling calculations is that the European energy market optimises the additional generation costs for RES against the background of a 20% RES target in 2020 as shown in Figure 3. This overall optimisation is modelled by comparing the difference between RES generation costs and conventional reference prices across all sectors (heat, electricity and biofuels), all technologies and all countries. Results are presented in terms of additional costs, that is, the total costs of generation per energy output minus the reference cost of energy production per unit of energy output. The optimisation exercise is conducted across all three sectors (RES-E, RES-H and RES-T). As biomass may play a role in all sectors, the allocation of biomass resources is a key issue. Consequently the overall optimisation across sectors includes an integrated optimisation of the distribution of biomass among the sectors. Of course, this may result in overestimating the flexibility in the use of biomass from energy crop plantations as the use of land is not entirely flexible and cannot be changed overnight. However, land use restrictions in terms of total availability of land per type of use are included in the derivation of primary energy potentials for energy crops.

[pic]Figure 3 Definition of least-cost scenario

In the political discussions RES targets are based on the EUROSTAT method, while the cost optimisation in the Green-X model is based on an extended substitution principle. The main difference between the two methods is in their treatment of RES-E. Under the substitution method, RES-E counts for about 2½ times as much primary energy as under the EUROSTAT method, because the conventional fuels substituted are taken into account. The main difference between the classical substitution method and the applied extended substitution method is the treatment of biomass: the classical substitution method takes account of primary biomass energy, while the extended substitution method uses substituted fossil fuel. This means under the classical substitution method a biomass plant with low efficiency would contribute more to an RES primary energy target than a highly efficient one. As regards comparability of the different methods, the 20% target in the Least-cost scenario based on EUROSTAT convention corresponds to 24.8% based on the classical substitution method, and to 22.3% based on the extended substitution principle. By using the extended substitution principle in the model, the amount of conventional energy saved for a given target is maximised: this is a very good proxy for the key energy policy objectives of the EU concerning security of supply and climate change.

Balanced scenario

In this scenario it is assumed that technology-specific support provides opportunities for the deployment of the most efficient technology options within each renewable energy source category. The key criterion in this scenario is to request a comparatively similar effort from all three sectors (heating and cooling, electricity and transport). This means that for the electricity sector, effective and efficient policies are implemented, leading to compliance with the RES-E Directive (2001/77/EC) in 2010 at EU level, and a continuation of efforts up to 2020. Similarly for the biofuels sector, effective and efficient policies are assumed that lead to compliance with the Biofuels Directive (2003/30/EC), with a continuation of efforts up to 2020. For the heating and cooling sector, it is assumed that renewable policies are implemented in all Member States, which is presently not the case. Such renewable heating policies provide sufficient incentives for the deployment of all relevant RES-H technologies across the EU. In this way the level of effort for the heating sector assumed for the balanced scenario is similar to the electricity and biofuels sectors.

Conventional supply portfolio

The conventional supply portfolio, i.e. the share of the different conversion technologies in each sector, has been based on the PRIMES country-specific forecasts. These projections of the portfolio of conventional technologies impact particularly on the calculations made in this study on the avoidance of fossil fuels and CO2-emissions. The following assumptions are made concerning the displacement of conventional energy by renewable energy: Keeping in mind that fossil energy represents the marginal generation option that determines the prices on energy markets, it was decided to stick, at the country level, to the sector-specific conventional supply portfolio projections as provided by PRIMES. Sector- and country-specific conversion efficiencies, derived on a yearly base, are used to calculate the amount of primary energy avoided. Assuming a constant fuel mix, avoidance can be expressed in units of coal or gas replaced. A similar approach is applied to the avoidance of CO2-emissions, where yearly changing average country- and sector-specific CO2-intensities of the fossil-based conventional supply portfolio from the basis for calculations.

Figure 4 shows the dynamic development of average conversion efficiencies as projected by PRIMES for conventional electricity generation and for grid-connected heat production. Conversion efficiencies are shown for both the PRIMES baseline and PRIMES efficiency case. Error bars indicate the range in country-specific average efficiencies between EU Member States. For the transport sector, where efficiencies are not explicitly expressed in PRIMES results, the average efficiency of the refinery process to derive fossil diesel and gasoline was assumed to be 95%.

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Figure 4 Country-specific average conversion efficiencies of conventional (fossil-based) electricity and grid-connected heat production in EU-25 (source: PRIMES scenarios)

The corresponding data on country- and sector-specific CO2 intensities of the conventional energy conversion system are shown in figure 5. The bars again illustrate the variation between countries.[pic]

Figure 5 Country-specific average sectoral CO2 intensities of the conventional (fossil-based) energy system in EU-25 (source: PRIMES scenarios)

Note: The differences between the PRIMES efficiency and baseline case for non-grid heat and transport are very small and therefore not shown.

Fossil fuel and reference energy prices

National reference energy prices used in this analysis are based on the primary energy price assumptions as used in the EU energy outlook. Compared to current energy prices the price assumptions in the PRIMES energy efficiency and baseline scenario are low for the later years up to 2020. The reference oil price for instance goes up to $48 per barrel while actual world market prices in the last year have fluctuated between $55 and 78 per barrel. A sensitivity analysis is therefore conducted for Higher energy price assumptions, taken from the PRIMES high energy price scenario. Table 2 and figure 6 set out the development of energy prices assumed in both cases.

Table 2 Primary energy price assumptions in $2005/boe (source: PRIMES scenarios)

Baseline | 2005 | 2010 | 2015 | 2020 |

Oil | 54 | 44.59 | 44.95 | 48.08 |

Gas | 30.31 | 33.86 | 34.22 | 36.99 |

Coal | 13.32 | 12.53 | 13.38 | 14.1 |

High price | 2005 | 2010 | 2015 | 2020 |

Oil | 54 | 61.86 | 67.58 | 77.61 |

Gas | 30.31 | 36.84 | 44.71 | 53.03 |

Coal | 13.32 | 13.63 | 14.19 | 16.29 |

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Figure 6 Primary energy price assumptions (source: PRIMES scenarios)

Reference prices for the electricity sector are taken from the Green-X model. Based on the primary energy prices, the CO2-price and the country-specific power sector, the Green-X model determines country-specific reference electricity prices for each year in the period 2005-2020. Reference prices for the heat and transport sector are based on primary energy prices and the typical country-specific conventional conversion portfolio. Reference prices for grid-connected heat supply from district heating and CHP plants do not include the costs of distribution – they represent the price directly at the plant. All reference prices are set out in Table 3.

Table 3 Reference prices for electricity, heat and transport fuels

in €/MWh output | 2005 | 2010 | 2015 | 2020 |

Electricity price | 52.1 | 54.9 | 49.6 | 48.6 |

Heat price (grid) | 28.3 | 29.3 | 30.3 | 30.6 |

Heat price (non-grid) | 50.5 | 51.2 | 51.6 | 53 |

Transport fuel price | 42 | 40.1 | 37.8 | 41 |

CO 2 prices

The CO2-price in the Least-cost scenario is exogenously set at 20 €/t, again similar to existing EU scenarios. Actual market prices (for 2006 EU Allowances) fluctuated between 7 and 30 €/t in the period January-July 2006, with averages fluctuating roughly between 15 and 20 €/t. Prices are however expected to rise in the coming years if stricter caps are put in place, which depends largely on the post-2012 framework. A sensitivity case is defined with a Higher CO 2 -price of 50 €/t. In the model, it is assumed that CO2-prices are passed through to electricity prices. This is done fuel-specific based on the PRIMES CO2-emission factors.

Increased RES deployment can have a CO2-price-reducing effect as it slows the demand for CO2-reductions. As RES deployment should be anticipated in the EU Emission Trading System and the CO2-price in the Least-cost scenario is set exogenously, this effect is not included, which is a somewhat conservative approach.

RES potential

A broad range of renewable energy technologies exists today. Obviously, to get a full picture of the future development of RES it is of crucial importance to take a detailed look at the country-specific situation – e.g. the potential of RES in general as well as their regional distribution and the corresponding generation cost. Major efforts have recently been made within the FORRES 2020 study to make a comprehensive assessment of Europe’s RES resource base. Consequently, this project builds directly on these consolidated outcomes as presented in the Commission’s Communication ‘The share of renewable energy’[64].

Within the model Green- X , supply potentials for all main technologies for RES-E, RES-H and RES-T are described in detail.

- RES-E technologies include biogas, biomass, biowaste, onshore wind, offshore wind, small-scale hydropower, large-scale hydropower, solar thermal electricity, photovoltaics, tidal & wave energy, and geothermal electricity.

- RES-H technologies include heat from biomass – subdivided into log wood, wood chips, pellets, and district heating –, geothermal heat and solar heat.

- RES-T options include traditional biofuels such as biodiesel and bioethanol, advanced biofuels, and the impact of biofuel imports

The potential supply of energy from each technology is described for each country analysed by means of dynamic cost-resource curves . Dynamic cost curves are characterised by the fact that the costs and the potential for electricity generation / demand reduction can change each year. The magnitude of these changes is given endogenously in the model, i.e. the difference in the values compared to the previous year depends on the outcome of this year and the (policy) framework conditions set for the simulation year.

Realisable mid-term potentials form the basis for the overall approach. This potential describes the maximal achievable potential assuming that all existing barriers can be overcome and all driving forces are active. This ensures that general parameters such as market growth rates and planning constraints are taken into account. It is important to mention that this potential must be seen in a dynamic context – i.e. the realisable potential has to refer to a certain year: for the purpose of this study 2020 has been chosen.

The following figures illustrate – taking the electricity sector as an example – the potential contribution of RES in the electricity sector within EU-25 up to 2020 by considering specific resource conditions in each country. Thereby, in accordance with the general modelling approach, a clear distinction is made between existing RES plants (installed up to the end of 2004 – i.e. the achieved potential in 2004) and future RES options – the additional mid-term potential . More precisely, Fig 7 depicts the achieved and additional mid-term potential for RES-E in EU-15 by country (left) and by RES-E category (right). A similar picture is shown for the new Member States (EU-10) in Fig 8. Note that in both figures no future potential is indicated for biomass, as the allocation of the total biomass potential to the different sectors (electricity/ heat/ transport) is part of the optimisation process in the applied modelling approach.

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Figure 7[65] Achieved (2004) and additional mid-term potential 2020 for electricity from RES in EU-15 – by country (left) and by RES-E category (right)

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Figure 8[66] Achieved (2004) and additional mid-term potential 2020 for electricity from RES in EU-10 countries – by country (left) and by RES-E category (right)

Given the overall optimisation across sectors, the availability of biomass and the allocation of biomass resources across sectors are crucial. The total domestic availability of solid biomass was set at 219 Mtoe/yr. Biomass data have been cross-checked with the European Commission, EEA and the GEMIS database[67]. In the Least-cost scenario it has been assumed that biomass can be imported to the European market. Specifically:

- Solid biomass in the form of wood products and wood residues can be imported to a maximum of 15% of the total additional primary input of forestry biomass.

- Liquid biofuel in the form of ethanol and biodiesel products can be imported to a maximum of 30% (average to be achieved over the whole 15-year period), corresponding to a default case based on solely domestic biofuel supply.

Figure 9 Solid biomass potential in primary energy terms (Mtoe/year)

A sensitivity case analyses the effect on the deployment of RES of allowing no biomass imports to the EU (sensitivity case No import of biomass to the EU ).

In this context, the figures indicate the dynamic development of identified biomass primary potentials at the EU-25 level.

RES targets

The Least-cost scenario assumes achieving a target of 20% RES in 2020. Sensitivity cases are conducted to analyse the effect of a lower or higher RES target (16%, 18% or 22% instead of 20%) on additional RES generation costs and the deployment of various RES technologies (sensitivity case Variation in RES target ). Note that the least-cost optimisation approach is defined as achieving an overall RES target; no sector-specific targets (separately for RES-E, RES-H and RES-T) are defined. The analysis assumes a linear path from current RES deployment up to the 20% target.

RES cost

Parameters on long-term cost developments of RES in the Least-cost scenario and the balanced scenario are based on the FORRES 2020 project[68]. Costs are adapted endogenously on the basis of technology-specific learning rates. Exceptions to this rule are the cost developments specified for solar thermal, tidal and wave energy, for which expert cost forecasts are used. In order to obtain a better understanding of the relation between long-term cost developments and the resultant overall costs of meeting future RES targets, the rate of technology learning is varied in sensitivity cases. Model results are obtained for Accelerated and Decelerated technological learning , assuming a 15% higher and lower learning rate respectively for each technology (compared to default learning rates).

Note that the analysis uses a quite detailed level of specifying costs and potentials. The analysis is not based on average costs per technology. For each technology a detailed cost-curve is specified for each year, based on "cost-bands". These cost-bands summarise a range of production sites that can be described by similar cost factors. For each technology a minimum of 6 to 10 cost bands is specified. For biomass at least 50 cost bands are specified for each year in each country. Economic conditions in respect of the various RES technologies are based on both economic and technical specifications, varying across the EU countries[69]. Fig 10 depicts the typical current band with of long-run marginal generation costs [70] per technology for the electricity sector. For the purposes of calculating the capital recovery factor, a default setting is applied with respect to payback time (15 years) and weighted average cost of capital (6.5%). The broad range of costs for several RES technologies reflects variations in resource (e.g. for photovoltaics or wind energy) or demand-specific conditions (e.g. full-load hours in the case of heating systems) within and between countries as well as variations in technological options such as plant size and/or conversion technologies.

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Figure 10 Long-run marginal generation costs (for 2005) for various RES-E options in EU Member States.

For hydropower (large- and small-scale) and wind onshore, non-harmonised cost settings are applied, i.e. country-specific data on investment costs and, where suitable, O&M-costs are also used. For all other RES-E options harmonised cost settings are applied across the EU. The ranges expressed for economic and technical parameters in these instances refer to differences in plant sizes (small- to large-scale) and/or conversion technologies applied. All data on investment costs, O&M-costs and efficiencies refer to the default start year of the simulations, i.e. 2005, and are expressed in €2005.

Prices for imported biomass are set exogenously:

- The price of imported wood is set country-specific, indicating trade constraints and transport premiums. The current European average is 16 €/MWh.

- The price of imported biofuels is put at a European average of 62 €/MWh.

Scenario parameters and sensitivity cases

Key parameters with a likely major effect on the modelling results were selected and corresponding sensitivity cases defined:

- Less energy efficiency policies : Sensitivity analysis serves to quantify the synergies between ambitious energy efficiency policies and the achievement of renewable energy targets set in relative terms – i.e. defined as a percentage of corresponding demands.

- Higher energy prices : World-market energy fuel prices have been very volatile over recent years. Obviously the additional generation costs of renewable energy are largely affected by the variations in world-market fuel prices. Accordingly, the applied sensitivity analysis aims to quantify this sensitivity to varia