RESGen RES Generation - From Research Infrastructure to Sustainable Energy and Reduction of CO2 Emissions

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1 RESGen RES Generation - From Research Infrastructure to Sustainable Energy and Reduction of CO2 Emissions Grant agreement No REPORT WP Number and title WP2 Boosting the Regional Sustainable Energy Concept Task Number and title Task 2.3 Benchmarking Deliverable Number and title D2.3 Author and Partner Tecnalia Date and Version final Dissemination Level PU (Public)

2 Abstract: This document is about benchmarking sustainable energy potential in RESGen regions and European Member States. The main aim of this task is to systematically compare the various dimensions including current energy use, renewable energy potential, governance capacity, demand conditions, and research and development capacities that are considered to drive the sustainable energy potential in regions and countries and to construct a composite indicator that aim to collect these dimensions to unified index. The document is divided into four chapters the first chapter is introducing the benchmarking objectives and is followed by the Chapter 2 that describes the method. The Chapter 3 presents the results of the benchmarking including the comparison in terms of sustainable energy potential index, the five sub-indexes and the underlying indicators. The last chapter aims to summarise the results and draw some conclusions. COPYRIGHT Copyright by the RESGen Consortium. The RESGen Consortium consists of the following institutions: Regional Council of (OSTRO) Coordinator University of Vaasa Vaasa Energy Insitute (UWASA) Contractor Oy Merinova Ab (MERINOVA) Contractor Fundación Labein (TECNALIA-LAB) Contractor Ente Vasco de la Energía (EVE) Contractor Cluster de Energía del País Vasco (CEPV) Contractor Northern-Hungarian Regional Innovation Agency (NORRIA) Contractor Károly Róbert College (KRF) Contractor Development Company (CDC) Contractor This document may not be copied, reproduced, or modified in whole or in part for any purpose without written permission from the RESGen Consortium. In addition to such written permission to copy, reproduce, or modify this document in whole or part, an acknowledgement of the authors of the document and all applicable portions of the copyright notice must be clearly referenced. All rights reserved. 2

3 Table of content: 1 Introduction Objective of benchmarking The role of benchmarking in RESGen project RESGen benchmarking method Overview on bechmarking and composite indicators Description of the sustainable energy potential index Current energy use Renewable energy potential Governance capacity Demand conditions Research and development capacity Results of the benchmarking sustainable energy potential Sustainable energy potential index Sub-index I: Current energy use Sub-index II: Renewable energy potential Sub-index III: Governance capacity Sub-index IV: Demand conditions Sub-index V: R&D capacity Discussion and conclusions Summary of results in RESGen regions Reflection on the chosen benchmarking method Results in the context of RESGen project Lessons learnt...36 References:...37 Annex I: Description of the indicators Annex II: Data Annex III: Example of the index construction 3

4 1 Introduction To measure is to know. If you cannot measure it, you cannot improve it." (Lord Kelvin) The European Council has set ambitious energy and climate change objectives for to reduce greenhouse gas emissions by 20%, to increase the share of renewable energy to 20%, and to make a 20% improvement in energy efficiency. EU energy and climate goals have been incorporated into the Europe 2020 Strategy for smart, sustainable and inclusive growth adopt by the European Council in June 2010, and into its flagship initiative Resource efficient Europe. Within in this line of thought the RESGen project is aiming for supporting the development of regional research driven clusters in sustainable energy field. The overall objective of RESGen is to pave the way for more sustainable energy self-sufficient European regions. This report aims to support the overall objective of RESGen project by comparing the sustainable energy potential of RESGen regions and benchmarking it against to European Member states. This is done by constructing a sustainable energy potential index that aims to take into account different aspects contributing towards more sustainable energy deployment. 1.1 Objective of benchmarking The RESGen benchmarking exercise aims to measure sustainable energy potentiality of RESGen regions and benchmark it against to European Member States. The objective is to distinguish and take into account the different dimensions involved to sustainable energy potential, analyse and identify good practises and opportunities for improvement. The idea of RESGen benchmarking exercise is to achieve comparable data sets on different dimensions that together comprise sustainable energy potential. The combination of five dimensions current energy use, renewable energy potential, governance capacity, future demand conditions for energy, and research and development capacity is expected to give more comprehensive view on sustainable energy potential. The benchmarking of sustainable energy potential of RESGen regions (,, Northern and ) is contrasted against countries in order to understand where our regions stand and where they may look for peers or best practises. 1.2 The role of benchmarking in RESGen project The role of WP2 in general, and Task 2.3 is to provide information on regional sustainable energy situation in order to better plan future actions within regions (WP3 Roadmap) and between regions (WP4 Joint Action Plan) as well as to set grounds for mentoring activities (WP5). The previous tasks of WP2, namely Task 2.1 Regional sustainable energy offer, demand and related policies and Task 2.2 Regional SWOT, were more focused on individual regions. This Task opens up the perspective in two important ways: 1) it aims to achieve comparison of regions and 2) it widens the perspective from four RESGen regions to whole European Union. 4

5 2 RESGen benchmarking method 2.1 Overview on bechmarking and composite indicators Originally benchmarking was developed as business management tool for improving the performance of the firm with comparison to its competitors. Accordingly, benchmarking can be defined as continuous process of measuring products, services and practices against the toughest competitors or those companies recognized as industry leaders (Kearnes, 1986) or to as the search for industry best practises that lead to superior performance (Camp, 1989). Later, benchmarking has gained popularity among public management and policy-making as well, especially in the fields of science and technology policy and health. Recent article (Grupp and Schubert, 2010) states that constructing science and technology indicators on a national level has become a standard practice to compare the performance between countries. Following the lead the objective of this RESGen benchmarking task is to compare systematically the sustainable energy potential of RESGen regions to European countries. The European Commission has led in promoting the use of so-called composite indicators that is, aggregation of different types of indicators into simpler constructs for the purpose of summarising complex multi-dimensional phenomena like sustainable energy potentiality. It has been argued that by aggregating a number of different variables, composite indicators are able to summarise the big picture in relation to a complex issue with many dimensions (EC, 2003; p. 433). The composite indicators have become very popular manner to compare systematically the performance of countries. With the aim of standardize the calculation method of composite indicators, OECD and Joint Research Centre of European Commission together published a set of guidelines for constructing composite indicators (OECD and JRC, 2008). Several indicators have followed these guidelines, such as Global Competitiveness Index (WEF; 2010), Regional Competitiveness Index (JRC, 2010) or Environmental Performance Index (Emerson et al., 2010). Similarly, European Commission (e.g. EC, 2010) has published European Innovation Scoreboards (EIS) 1 that systematically viewed the innovation performance of the Member States. Although composite indicators have gained popularity during recent years through their easily communicative results that are prone to attract public interest, there are some downsides related to them. It has been argued that composite indicators may give misleading or non-robust messages to policy makers leading to very simplistic policy conclusions (OECD and JRC, 2008). In addition, the construction of composite indicators involves subjective choices (like selection of the indicators, weighting methods, and treatment of missing values), thus very transparent manner of reporting is required (ibid). Finally, the quantity and quality of data are important preconditions for composite indicators as for all other types of analysis utilizing quantitative data. 1 The new results of 2010 (Innovation Union Scoreboard, IUS) have been published February 2011 with slightly modified methods in order to better monitor the progress towards Innovation Union. 5

6 2.2 Description of the sustainable energy potential index The benchmarking of RESGen regions to countries in sustainable energy context is done by calculating an index of sustainable energy potential that aims to take into account different dimensions related to sustainable energy potential. The selected dimensions were current use of sustainable energy, renewable energy potential, governance capacities, demand conditions, and research and innovation potential (Figure 1). The dimensions have been selected in order to reveal the diversity of aspects that may have an impact on sustainable energy potential of a region or nation. Each of these dimensions brings together a set of indicators with an aim to provide a balanced assessment of the sustainable energy potential. Figure 1: Dimensions of sustainable energy potential The five dimensions further described below are considered to form the core of sustainable energy potential and accordingly these dimensions are transformed to five sub-indices. Each subindex is calculated based on indicators that are considered to measure this dimension of sustainable energy potential. The sources of these indicators included Eurostat, European Environmental Agency, ESPON, Joint Research centre (JRC), Food and Agriculture Organization (FAO). However the lack of indicators in regional level challenged the data collection efforts. As the regional energy statistics were practically non-existent from international statistics collections, the energy statistics were requested from each RESGen region. As the data collection methods may vary between the regions, the measures are only indicative and they may not be taken as official comparison but rather a tentative analysis of regional sustainable energy potential Current energy use The indicators selected to describe the current state of sustainable energy deployment were chosen from the basis of European energy objectives ( ). Thus the indicators are taking into account energy efficiency, renewable energy use and Greenhouse gas (GHG) emissions. For measuring energy efficiency, the final energy consumption (FEC) per gross domestic product and FEC per capita were used. The former can be considered to measure energy intensity of the nation 6

7 or region and the latter is an indicator of absolute energy used. The absolute energy used depends on the general development of the country that is to say that especially in less developed countries FEC per capita is strongly associated with GDP. Once a country has reached a certain development level, the FEC per capita varies with changes in climate and geographic conditions and with structure of the economic activity. The FEC per GDP is on the other hand considered to be an indicator of energy intensity of the economy and it refers to the amount of energy used to produce a unit of output. The improvements of energy intensity are in general driven by structural changes of economy from industry intensive towards services and less energy intensive industries as well as by efficiency improvements in all sectors (EEA, 2007a). The other pillar of sustainable energy- renewable energy was taken into account with one indicator measuring the proportion of renewable energy of electricity consumption. The last indicator shows the current level of GHG emissions of the region or country Renewable energy potential The indicators for renewable energy potential were selected in order to give insights of renewable resources available for potential deployment of energy production. The indicators for wind power were taken from a recent publication of EEA (2009) that gives estimates for onshore and offshore wind power for EU countries. In addition to calculating raw wind resource potential, the EEA study also introduces and quantitatively analyses the environmental and social constraints of wind sector development and the estimations also evaluates the economic feasibility of wind energy production across Europe in order to weight the potential output at competitive rates. For RESGen regions, the regional estimates based on the same EEA calculations were available from ESPON ReRisk report. However, as these regional estimates had been calculated only for onshore wind energy, the indicators of offshore wind energy potential were left out of the analysis. The solar power consists of thermal, photovoltaic and concentrated solar power. The chosen indicator captures only the photovoltaic potential and it is taken from estimations made by Joint Research Centre (JRC, 2010). Their Photovoltaic Geographical Information System (PVGIS) makes a geographical assessment of solar resource with given performance of photovoltaic technology. Their interactive tool permits for estimates of photovoltaic power in regional levels as well. The database consists of raster maps representing twelve monthly averages and one annual average of daily sums of global irradiation for horizontal surfaces (JRC, 2010). For hydro-electric power, the estimations made by World Energy Council (WEC, 2007) were utilized. They estimate the gross theoretical capability, technically exploitable capability and economically exploitable capability for most of the countries in world. For the purposes of the benchmarking the technically exploitable capability was used as an indicator of hydro-electric power. As there were no indicators available for the regions, some estimates based on national numbers and regional area were made. The biomass intensity was calculated based on the forest intensity and arable land intensity of the countries and regions. Although much more sophisticated manners for estimating the biomass energy potential exist (see e.g. EEA, 2007b for agricultural biomass or Kindermann et al for forest based biomass), the estimations are largely dependent on the forest stock or agricultural yield, thus the intensity was considered to be sufficient estimate for the potentiality. Although the statistics for municipal solid waste would be easily available, they were decided to be left out from 7

8 the analysis because of the questionable sustainability of the indicator. For tidal power, wave energy and geothermal energy, all certainly forming an essential part of renewable energy potential, no existing indicators were found. The utilization of these sources of renewables is technologically at very early stage of development and perhaps this is partly the reason why estimations are still lacking Governance capacity Governance capacity aims to provide some indicators related to energy and environmental policy objectives and achievement towards these objectives. Firstly, the objective for renewable energy and the objective for greenhouse gas emissions were taken into account. As well as the level of objectives set, the progress towards these objectives was considered to describe the governance capacity. Thus the third indicator taken into account was the current achievement in relation to objective of renewable energy. Similarly, the GHG emission change in was thought to describe the efficiency of public governance Demand conditions This dimension is formed by a set of indicators having to do with conditions of future energy demand. Higher energy demand is considered to have also a positive impact on deployment of sustainable energy potential. Firstly, the electricity prices for domestic and industrial consumers are included. It is expected that the higher the electricity prices, the larger is the demand for energy from renewable sources and deployment of technologies that are improving the energy efficiency. It is also thought that future growth of population and GDP are prone to raise the demand for energy, thus the population growth projections and the GDP growth are included as drivers of energy demand as well. The employment in high energy consumption sectors aims to measure the weight of the energy for the economy in general by showing how largely the employment is dependent on industries that are high energy consumers. If the importance is high, so is the demand for energy. The last indicator measuring the energy demand conditions is the heating degree day that is an indicator of the demand for energy needed for heating. It is a climate based indicator and heating demand is considered to be rather inelastic and thus acting as a pushing element towards sustainable energy use Research and development capacity Research and development (R&D) capacity aims to show the potentiality of the region or nation in conducting research and innovation activities, ever so important for sustainable energy deployment and technological advancement. The indicators aim to capture the innovation potentiality firstly in general, and secondly in terms of energy sector potential. As the technologies needed for deployment of renewable energy sources are not restricted to energy technologies only, the general potential in R&D is considered as equally important. Firstly, the R&D expenditures were considered to measure the overall input for innovation. As an estimation of innovation output, the number of patents was used. The number of FP7 projects was taken into account as a measure of internationalization of research activities. For particular potentiality of the energy sector research, the share of patents in energy technologies is considered to measure 8

9 the energy research intensity, similarly as share of ENERGY-FP7 of total FP7 projects. The following table lists all the indicators used and the sources of data. Table 1: List of indicators 9

10 2.3 Method for constructing the index The method for calculating the index of sustainable energy potential follows to some extent the guidelines of constructing composite indicators (OECD, JRC, 2008) but for example the strong request of basing the index construct on theory is not possible to follow with sustainable energy potential. Thus the index construct and indicator selection is more explorative process and consequently the results are only indicative. Because the indicator selection process may leave some room for subjective selection, the aim was to describe the method itself as transparent way as possible. The steps for calculating the index followed the ones used for calculating European Innovation Scoreboard (EC, 2010). (1) Calculating the indicators based on the raw data extracted from various sources. The data sources and indicator calculation method for each corresponding indicator can be found from Annex I and the final data set utilized from Annex II. (2) Replacing the missing values Some (energy) indicators were not available from common sources in regional level and thus the following replacing methods were used (1) The regional statistics were requested from RESGen partners for the following indicators,: Ia, Ib, Ic, Id, IIIa, IIIb, IIIc and IIId. In some cases, the indicators were not available neither in regional level, and thus these missing values were replaced with national level values. (2) For the case of RESGen region (NUTS3), in all the cases when the values were available in NUTS3, they were utilized, in case not NUTS2 Länsi-Suomi was used. (3) In regional level, the indicator IIc Hydro power potential is an estimation based national values. The regional estimates were calculated by dividing the national hydropower potential with total area of the country and the multiplied with the area of the region. The following table summarizes the NUTS levels utilized for RESGen regions: 10

11 In addition for, there were some missing values (indicator (IIIc) missing CY and MT; and indicator (Va) missing GR). These values were left without replacement and thus for calculating the sub-indices and the index, these countries receive the averages without these indicators. (3) Calculating the inverse values (1/x) for indicators for which the smaller the value the better the indicator. These include indicators Ia (Energy intensity), and Ib (FEC per capita). and Id (GHG per capita). In addition, for the indicators IIIb (GHG objective) and IIId (Achievement towards GHG objective) the additive inverse (x*-1) values were taken. (4) Transformations The indicators Ia, Ib, Id, IIa, IIb, IIc, IId, IIe, Iva, IVb, IVf, Vb and Vc are unbound indicators, where values are not limited. These indicators can be highly volatile and have skewed distributions (e.g. the indicator may have exceptionally high or low values that distort the distribution from normal distribution). In order to smooth the distribution, a square root transformation was utilized for these indicators. Square root transformation diminish the largest values relatively more than the smaller values. It is most typically used for relational indicators like the ones utilized in this benchmarking. That is why the square root transformation was chosen over other common transformation methods like natural logarithm. (5) Identification and treatment of outliers Outlier is an exceptionally high or low value. A common rule of a thumb is when 11

12 the observation gets a value that has a larger distance than three times standard deviation from the mean value, it is an outlier. The outliers may distort the rescaled scores that are based on minimum and maximum values of the variable. The outliers (exceptionally high values) are found from the indicators Ic and IIIa. When determining the maximum values (Step 6 of the method) the two outlier values are excluded and when calculating the re-scaled scores (Step 7 of the method) the outliers are automatically replaced with value 1 (maximum). (6) Determining maximum and minimum scores For each indicator, minimum and maximum values are determined. (7) Calculating re-scaled scores Re-scaled scores are calculated by first subtracting the minimum score and then dividing by the difference between maximum and minimum score. Consequently, the maximum rescaled score gets a value of 1 and the minimum 0. (8) Calculating the sub-indices For the five dimensions (Current energy use, Renewable energy potential, Governance capacities, Demand conditions and R&D capacity) a sub-index is calculated as the unweighted average of the re-scaled scores for all the indicators for the respective dimension. (9) Calculating the Sustainable Energy Potential Index The Sustainable Energy Potential Index is calculated as the unweighted average of the five sub-indices. In order to illustrate how the index has been calculated, we give an example of calculating the Current energy use sub-index for in Annex III. 12

13 3 Results of the benchmarking sustainable energy potential The results of the benchmarking are shown firstly in level of the sustainable energy potential index and then in level of each sub-index. In order to advocate transparency, the results are also analysed in indicator level. This also helps to make more detailed comparison on various aspects that are thought to drive the sustainable energy deployment. The comparison in indicator level allows as well each RESGen region to focus on benchmarking results that are most interesting for their region. 3.1 Sustainable energy potential index A summary index of sustainable energy potential is provided by the composite indicator obtained by an appropriate aggregation of the 24 indicators. Figure 2 shows the results for RESGen regions and for 27 EU Member States. The results of benchmarking sustainable energy potential of RESGen regions and European Union Member States indicate that, and have the most potential. This index gathers the five different dimensions that describes the different aspects of sustainable energy potential and thus shows the overall potential of the countries and regions in terms of current sustainable energy use, renewable energy potential, policy objectives and efficiency, demand conditions and research and innovation potential. From RESGen regions, the best performer is that shows more potential than countries in average. receives an index value very close to average, whereas and Northern show more modest potential. 13

14 Northern 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 Figure 2: Sustainable energy potential index. Sub-index I: Current energy use appears to be a European benchmark regarding sustainable energy use, followed by and. From RESGen regions, is clearly the best performing, followed by and Northern 2 which almost yield to average. appears rather low in the ranking, but not so far from European average in terms of the sub-index value. 2 No energy data for Northernern available in regional level. Thus the index value is the same for Northernern and. 14

15 Northern 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 Figure 3: Sub-index I - Current energy use These results can be best explained by looking at the underlying indicators that are forming this sub-index. The following figures show the performance in sustainable energy use in indicator level. 0,30 0,25 0,20 0,15 0,10 0,05 0,00 Figure 4: Energy intensity (FEC per GDP). Northern 15

16 10,00 9,00 8,00 7,00 6,00 5,00 4,00 3,00 2,00 1,00 0,00 Figure 5: Final energy consumption (FEC) per inhabitants (toe per capita). 0,90 0,80 0,70 0,60 0,50 0,40 0,30 0,20 0,10 0,00 Figure 6: Share of renewable energy of electricity consumption (%). Northern North 16

17 30,00 25,00 20,00 15,00 10,00 5,00 0,00 Figure 7: GHG emissions per capita. North The figures show firstly how the two indicators measuring energy efficiency show very different patterns. Basically, when analysing the results of energy intensity the economically most developed countries are the best performers, in other words they are able to generate most value with least energy. RESGen region appears as the benchmark of the whole Europe followed by, and and. Also is more energy efficient than EU countries in average. When the energy efficiency is measured in terms of energy consumption per capita, the order of the countries and regions is changing completely. The best performing countries are, and. From RESGen regions Northern and are showing absolute values of energy use smaller than average and Basque Country yields to about European average. On the other hand, the highest energy consumption values are found from. Current use of renewables was the third indicator included to the index of sustainable energy use. Here is the benchmark having 80% of electricity consumption covered by renewables, followed by (64%), (50%) and (49%). The rest of the RESGen regions, Northern 3, and, the renewable intensity is among the lowest levels in Europe. The last indicator the greenhouse gas emissions per capita measures the current state of the play in emissions. This indicator shows a similar pattern of countries than the one of absolute energy use, i.e. highly developed countries have high emission burdens and vice versa. There are certain exceptions, namely countries with clean renewable energy sources (hydropower, thermal power) or countries that are using nuclear power or combinations of these two (e.g. and ). From RESGen regions, Northern 4 and are superseding the European average, where as and 5 are below the European average. 3 No renewable energy figures available in regional level for or Northern, thus national (NUTS1) values are used. 4 No GHG emission data available for Northernern, thus national (NUTS1) values are used. 5 No GHG emission data available for in regional level, thus national (NUTS1) value is used. 17

18 3.3 Sub-index II: Renewable energy potential The second sub-indicator was intended to measure the renewable energy potential. The final index covers indicators showing onshore wind energy potential, solar energy intensity, hydro energy potential and biomass potential (forest area and arable land area). The results show that the most renewable energy potential is found from, and. The RESGen regions are all below the European average but this is partly misleading, as most of the renewable energy sources are presented in absolute terms. For example wind energy potential and biomass potential (forest area and arable land area) are directly related to the area of the region or country. Best way to analyse the relative potential would be indicators in relation of energy consumption of the region or country. But as these are not available, renewable energy potential is measured mostly in absolute terms. In addition, as there are no data available regarding ocean energy or geothermal power potential, countries like UK, (ocean energy) and Iceland (geothermal energy) are showing less potential than they actually have. Northern 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 Figure 8: Sub-index II - Renewable energy potential Closer look to the indicator level reveals that there are large differences among European countries in wind energy potential, for instance. A group of seven countries (,,, UK,, and ) are having the most wind energy capacity of Europe, the rest having a much smaller potential. shows a relatively high level of wind power 18

19 potential, but the rest of the RESGen regions on the other hand, are having much less potential in relation to European average. Having this said the wind energy potential indicator shows how the wind energy potential is enormous in Europe and how the total electricity consumption in Europe could in hypothetical terms be covered by wind power. The solar power potential (intensity) of Europe is concentrated to South Europe,, and having the most potential. and Northern have more potential for solar power than European countries in average whereas yield a figure slightly less than average. has less solar power potential but however more than in average Figure 9: Onshore wind energy potential 2030 (total potential) TWh North North Figure 10: Yearly average sum of global irradiation per square meter received by the modules of the given system (kwh/m2). 19

20 North Figure 11: Hydropower potential (theoretical possibility for electricity generation) (TWh/year) The hydropower potential shows again large differences between countries -, and having the highest potential. Hydropower potential estimates are rather modest for RESGen regions 6. The most forest area of Europe can be found from, and. Similarly, the most arable land area is located in, and. As mentioned, these indicators are far from exact measures of biomass potential, but however indicating the level of natural resources available for biomass production Figure 12: Forestry area (km2). North 6 Note that hydropower potential is an estimate for all RESGen regions based on national figures, as no data was available on regional level. 20

21 Figure 13: Arable land area (km2) 3.4 Sub-index III: Governance capacity North This indicator aims to measure the ambitiousness of sustainable energy policy objectives on one hand, and the efficiency of these policies i.e. the improvement towards the set objectives on the other. The sustainable energy policy objectives used here are renewable energy share and green house gas (GHG) emissions., and appear to be European benchmark regarding the governance objectives and efficiency. RESGen regions and Northern appears to have more some what more balanced policy objectives and progress towards the objectives than European average whereas and yield to sub-index value slightly below the average, although the differences are very small. 21

22 Northern 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 Figure 14: Sub-index III Governance capacity. When looking at indicator level, Ostobothnia with objective of 67,7% 7 of renewable energy in 2020 is a clear benchmark of Europe followed by, and. The renewable energy objective for is 15% (UK national target), for Northern 13% ( national objective) and for 12%, all lower objectives than European average that is 20%. 7 The new, yet not approved Regional energy strategy has set the objective of achieving 100% of renewable energy by Thus, the 67,7% is an estimation for

23 0,80 0,70 0,60 0,50 0,40 0,30 0,20 0,10 0,00 Figure 15: Renewable energy objective (%). North The efficiency of achieving the objective is measured in terms of achievement in 2008 in relative to the objective of renewable energy. With this respect,, and show the largest potential to achieve the objective. is the best performing RESGen region, followed by, Northern and. 0,90 0,80 0,70 0,60 0,50 0,40 0,30 0,20 0,10 0,00 North Figure 16: Achievement in 2008 towards the objective of renewable energy 2020 (% of objective achieved). The other indicator measuring the governance of sustainable energy is the objective set for Greenhouse gas emissions for period UK has set an objective larger than EU average aiming at reductions of 12,5% of GHG emissions during the period of and this same objective is used for region as no regional objective has been set. Also and Northern slightly outperform the average objective of. has set the national objective of 0%, and when regional objectives are not applicable is given 23

24 the national objective. 0,30 0,20 0,10 0,00-0,10-0,20-0,30-0,40 Figure 17: Objective for GHG reductions for the period of (%). North The last indicator measures the efficiency of achieving the GHG objectives by indicating the change in GHG between years Here and consequently Northern has been able to lower the GHG emissions by 10% are among the benchmarks in Europe. Also UK and thus region have achieved similar reductions in GHG emissions than in average. yields to a reduction of 3%, whereas () has increased the GHG emissions in ,10 0,05 0,00 North -0,05-0,10-0,15 Figure 18: GHG reductions (% of GHG emissions). 24

25 3.5 Sub-index IV: Demand conditions The fourth dimension of sustainable energy potential is formed by demand conditions of energy. It is expected that higher the demand for energy is in general higher the need for usage of sustainable energy as well. This sub-index is summing up various aspects that are expected to have an impact on energy demand like current energy balance, electricity prices, population growth projections, GDP growth, economic dependency on high energy consumption sectors, and heating demand. The results of this sub-index show that, and have the highest scores in respect of demand conditions. At the same time they however are countries or regions with relatively low renewable energy potential (see Figure 8, p. 18). Northern has also higher demand conditions score than EU average, whereas and are yielding to score similar to the European average. Northern 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 Figure 19: Sub-index IV Demand conditions In the indicator level, the current electricity prices are expected to drive the development of sustainable energy as well the higher the prices the higher the demand for renewable energy and energy efficiency. For domestic consumers, the highest electricity prices are found from, and. From RESGen regions the domestic consumers in have the highest electricity prices whereas the electricity is somewhat cheaper in, UK and, 25

26 respectively. 0,300 0,250 0,200 0,150 0,100 0,050 0,000 Figure 20: Electricity prices for domestic consumers (e/kwh) When the electricity prices for industrial consumers are considered,, and are having the highest prices, whereas, and have the lowest energy prices for industrial consumers. 0,160 0,140 0,120 0,100 0,080 0,060 0,040 0,020 0,000 Figure 21: Electricity prices for industrial consumers (e/kwh) The population growth is expected to drive the future energy demand as well. The population growth projections to 2020 show that, and are expected to have the highest growth rate. Population is expected to grow in with over 10% by 2020 and for the expected growth is 3.7%. population is expected to remain constant, whereas the population in Northern is expected to decrease by 7,8%. 26

27 0,25 0,20 0,15 0,10 0,05 0,00-0,05-0,10 Figure 22: Population growth projection 2020 (%) North The GDP growth projections are calculated based on past ten years ( ) average GDP growth and thus these estimations do not properly take into account the effect of the economic crisis. Based on the estimations, however, all the RESGen regions are expected to grow, Northern showing the highest growth rate followed by, and, respectively. 0,18 0,16 0,14 0,12 0,10 0,08 0,06 0,04 0,02 0,00 North Figure 23: Average yearly GDP growth (estimations based on past 10-years period ) It is expected that countries or regions whose economy is more based on high-energy consumption industries are having less elasticity in energy demand. Thus the indicator showing the share of employment on high-energy consumption industries are expected to have higher demand conditions foe renewable energy. The industries considered as high-energy intensive were manufacture of pulp, paper and paper products, manufacture of coke, refined petroleum products and nuclear fuel, manufacture of chemicals and chemical products, manufacture of rubber and plastic products, manufacture of other non-metallic mineral products, manufacture of 27

28 basic metals, and transport, storage and communication. The results show that, Northern and are among the most energy dependent economies, thus indicating that the demand for sustainable energy solutions is also higher. is different in respect of this indicator, having the score among the lowest in Europe. 0,20 0,18 0,16 0,14 0,12 0,10 0,08 0,06 0,04 0,02 0,00 North Figure 24: Employment in high energy consumption sectors (%). The heating demand is considered as a demand condition that is relatively inelastic as well, meaning that more demand there is for heating more demand there is for sustainable energy as well. This indicator shows that there is most heating demand in North Europe (, and ) being among the regions with most demand for heating. Northern, and are having less heating degree days than European countries in average Figure 25: Heating degree days (10-years average) North 28

29 3.6 Sub-index V: R&D capacity The last sub-index aims to show the potentiality of research and innovation. The indicators that comprise this dimension include R&D investments, patents, FP7-projects as an indicator of internationalization of R&D, and two indicators aiming to capture the intensity of energy sector in R&D and innovation (share of FP7 projects and patents in energy field). The indicators measuring the general potential are considered to be important as the research benefiting the usability of renewable energy sources is not limited to energy field R&D, as for example research in emerging horizontal areas like ICT, nanotechnology or biotechnology are important towards development of sustainable energy deployment. Northern 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 Figure 26: Sub-index V Research and development capacity The benchmark of Europe in innovation potential is followed by and. and supersede the European average whereas and Northern show some what less potential for innovation in respect of these selected indicators. The R&D investment intensity is highest in and followed by. Basque Country yields the European average whereas Northern and have the lowest levels in R&D investments. 29

30 0,04 0,04 0,03 0,03 0,02 0,02 0,01 0,01 0,00 Figure 27: R&D intensity (% of GDP) North Patents are commonly used indicator measuring the output of innovation activities, although it may be more an indicator of high-technology R&D output. is a clear benchmark in patenting, followed by and. is again the best performing RESGen region followed by, and Northern Figure 28: EPO patent applications (per million of inhabitant). North The last indicator measuring the general potentiality of R&D, is the number of FP7 projects. Although this indicator may not be the best possible indicator measuring the internationalization of R&D, it is a measure of at least activity of seeking European funding (that may of course depend on national R&D funding availability among other things)., and appear above the European average with similar FP7 project intensity whereas in Northern the FP7 projects are less popular. 30

31 0,25 0,20 0,15 0,10 0,05 0,00 Figure 29: Number of FP7 projects per population. North The two last indicators included to the dimension measuring research and innovation potentiality aimed to show the particular potentiality of research in energy field. The share of patents in energy domain is largest in, followed by and. Although these countries do not appear at the top when analysed with other innovation related fields, it shows that in relative terms, these countries seem to be oriented to energy research. has higher share of energy field patents than the EU countries in average and yields to share very close to the European average. and Northern are less active in energy field patenting. 0,60 0,50 0,40 0,30 0,20 0,10 0,00 Figure 30: EPO patent applications in energy field per total patent applications. North 31

32 1,20 1,00 0,80 0,60 0,40 0,20 0,00 Figure 31: Share of ENERGY FP7 (%). North Lastly, the share of FP7-projects in energy field indicates that and are active in energy domain in Europe. On the other hand, and Northern are having very view energy FP7-projects. 32

33 4 Discussion and conclusions The European energy strategy has ambitious goals to turn our energy systems onto a more secure and sustainable path. The European Council adopted in 2007 ambitious energy and climate change objectives for 2020 to reduce greenhouse gas emissions by 20%, to increase the share of renewable energy to 20% and to make a 20% improvement in energy efficiency. The RESGen project has the objective of supporting regions towards increased utilization of sustainable energy by reinforcing regional research driven clusters and collaboration between the regions. The aim of this benchmarking was to obtain systematic comparison of sustainable energy potential that RESGen regions possess and benchmark it against the European countries. This was done by constructing a composite index of sustainable energy potential that consisted of five dimensions. The selected dimensions aimed to take into account the different aspects that may drive the sustainable energy potential. The following discussion aims to first summarise the results of benchmarking in RESGen regions, then make a reflection on the chosen benchmarking method followed by overview of the results in the context of RESGen project. Lastly, some overall lessons learnt are discussed. 4.1 Summary of results in RESGen regions The overall results show that there is a difference in the potential between the RESGen regions being the best performing RESGen region. But when taking a closer look to the results, the largest differences between the RESGEn regions can be found from the dimension that aimed to measure the potential of Research and innovation capacity (Sub-index V). Regarding the other four dimensions, the RESGen regions are relatively equal, as it can be seen from Figure 32 below. 0,8 0,7 0,6 0,5 0,4 0,3 0,2 0,1 0 INDEX I: Current energy use II: Renewable energy potential III: Governance capacity IV Demand conditions V R&D capacity Northern Figure 32: Summary of sub-indexes in RESGen regions and average. Although constructing a composite index may be a useful tool for national or regional benchmarking, in sustainable energy potential context, the results may be misleading. As for example, the indicators measuring renewable energy potential were mostly available only in 33

34 absolute terms, although the indicators in relative terms would have been much better indicators of the potential. In any case the results of the renewable energy potential clearly demonstrate that all the RESGen regions have natural resources for renewable energy. It seems that have most potential for wind energy, whereas and Northern show relatively higher levels of solar energy potential. Northern has a large potential for biomass as it possess the most arable and forest land areas of the RESGen regions. has higher potential for onshore wind energy than for example, and although proper indicators were not available at regional level, it is very likely that the offshore wind energy potential is the highest in as UK has the highest offshore wind energy potential in Europe. Similarly, especially but also is showing high potential for ocean energy, although exact measures were not found for benchmarking purposes. When viewing the policy objectives and their efficiency, the results are very two-fold. When objectives of Renewable energy are considered (with objective of 100% by 2040) is a clear benchmark in context of European countries but at the same time the national (/regional) GHG-objectives of are very modest (0%). (or UK) on the other hand, shows rather ambitious objectives in GHG emission cutting (12,5%) but is more moderate in regards of objectives set for renewable energy (15%) in the European context. and have set more modest objectives on both GHG emissions and renewables, but are on the other hand showing good progress in achieving these objectives. In fact is among the benchmarks in cutting GHG emissions with 10% decrease during In regards of energy demand context, is the most dependent on energy imports, where as other RESGen regions have much higher shares of primary energy production. The energy prices, both for domestic and industrial customers, are higher than European average in and whereas UK and have energy prices lower than European average. The price of electricity is influenced by the market price of primary fuels used to produce it but also by the cost of carbon dioxide (CO 2 ) emission certificates. Besides from these reasons also the infrastructure e.g. large scale use of cogeneration (electricity and heat) possibilities may influence the electricity pricing., and Northern are all industrialized regions and thus they show similar patterns of being highly dependent on energy intensive industries. Heating demand is obviously much higher in due its location, whereas the rest of RESGen regions have much lower levels of heating demand. As mentioned, the sub-index showing the largest differences within the RESGen regions was by far the one measuring the Research and innovation capacities. With the chosen indicators (R&D intensity, patents and FP7 projects) the difference between best () and worst (Northern ) performing RESGen region manifest a relatively large gap. This may be due national science strategies where has centralized most of its R&D in the capital area and the relative newness of regional innovation strategies and policies in Northern (EC, 2010c). But this does not mean that less research intensive regions would not be able to turn themselves as leaders in sustainable energy in many cases the second mover advantage i.e. efficient knowledge transfer and absorption from elsewhere with lower costs has been very successful. 34

35 4.2 Reflection on the chosen benchmarking method The RESGen benchmarking on sustainable energy potential utilized publicly available quantitative data with an objective to construct a summary index of sustainable energy potential. The quantitative approach was chosen because it was thought that systematic analysis of quantitative indicators may have complemented our understanding of regional sustainable energy potential. Originally, the task was focusing only on RESGen regions but after some nice discussions and reflections during RESGen project steering committee meetings, it was decided that a European wide comparison would give more value added to this task. In addition, after the initial data screening it was quite clear that regional data from common sources was very scarce, especially regarding energy indicators. Thus it was thought that besides requesting the regional data from RESGen partners sources, the benchmarking in national level would back-up the analysis. Due these changes in the scope and the problems of data availability this task turned out to be more challenging than originally thought. Further complication was found when seeking statistics on renewable energy potential. Although renewable energy sources are highlighted in many European and national level policies, the reliable and comparable data on the potential of each renewable source is quite challenging to obtain. Thus the results of the benchmarking, especially regarding this dimension, are merely indicative. However the utilized sources for solar and wind energy for example may give some further ideas for more detailed regional comparison. By definition the composite indicators are thought to simplify the comparison of regions/countries in the case of multi-dimensional policy issue. In the case of sustainable energy potential, we may say that the composite indicator is not perhaps the most suitable manner as the groups of indicators included under one dimension are not necessarily correlated (e.g. FEC/GDP and FEC/capita; or any of the indicators measuring renewable energy potential) but on the contrary may show very different patterns and thus the sub-index values may be misleading. This is why the final benchmarking is also including the comparison in indicator level in order to advocate transparency and to ensure full utilization of the results. As a reflection, it may be argued that in the case of sustainable energy potential, the construction of composite indicators may not be the best possible method for comparison. 4.3 Results in the context of RESGen project This task had the objective of comparing the RESGen regions in sustainable energy context. Although we may discuss about the chosen method, the objective of comparing RESGen regions is achieved. The composite indicator may give the regions some hints of the overall situation but the report allows also more detailed comparison as well by providing the indicator level comparison. In addition, the data set collected (Annex II) allows regions to do more detailed analysis on themes of their interest. The results of this benchmarking task complement the analysis of WP2 Boosting the regional sustainable energy capacities and capabilities by two important manners: firstly by receiving a systematic comparison of the RESGen regions and secondly, by having a comparison of RESGen regions towards European countries. These results may be used as general inputs for constructing the regional roadmaps (WP3) and for seeking for the possible joint actions of future (WP4). More concretely, the results of the benchmarking can be applied for drawing some lessons for the 35

36 manual of managing regional sustainable energy development (Task 2.5). Similarly, the results may serve as background information towards RTD policy recommendations (Task 3.5). 4.4 Lessons learnt All in all, this benchmarking exercise has shown some concrete indicators of sustainable energy potential of our RESGen regions and European Member States. Although we often may think that quantitative indicators may only be able to describe the reality only to certain extent, we may also argue that they may give us some objective facts to support our (sometimes) subjective believes. And like mentioned at the very beginning of this report, without being able to measure, it is at least hard to achieve any improvements. Thus although this benchmarking may have been unable to achieve very rigorous results, it has for sure shown some comparison of RESGen regions and EU countries regarding indicators that are to some importance for achieving the common European objectives of Based on the results, the following four points can be raised for further discussion: European countries and regions are very diverse in terms of sustainable energy potential. But this diversity is not a weakness, it is a strength. Each European country or region should be able to find their areas of strength and focus on those. These areas of strength may be found from various dimensions of sustainable energy potential, not only from the renewable energy sources. For example, a strategy of building strong research base in energy field in or focusing on low-carbon economy in are good examples of regional specialization. These strategic choices may be turned to sustainable development opportunities that do not only benefit the region in question but the whole European efforts towards more sustainable future. Europe as whole could easily achieve the objectives of renewable energy in terms of resources. The resources exist but their economically and environmentally viable use is still requiring research and development efforts a long side with sufficient political initiatives of European Member States. A single European energy market would make the achievement of the objectives a reality in shorter time perspective. In areas like sustainable energy that aim for social and environmental benefits along with economic benefits, a deeper collaboration in research would benefit the competitiveness of the European economy as whole. For example, the initiative of European Research Area that aims at e.g. free movement of researchers and avoiding duplication in research efforts could be easier to make reality when unifying the forces for a common societal challenge such as sustainable energy. In general, as European regions are often considered as locus points of activities towards common European goals, better availability of standardized data in regional level would significantly improve the possibilities to monitor the progress towards set goals. 36

37 5 References: Camp C, (1989) Benchmarking, The Search for Industry Best Practices that Lead to Superior Performance, Milwaukee WI, ASQ Quality Press. Emerson, J., Esty, D. C., Levy, M.A., Kim, C.H., Mara, V., de Sherbinin, A., and Srebotnjak, T. (2010) Environmental Performance Index. New Haven: Yale Center for Environmental Law and Policy. Grupp, H., and Schubert, T. (2010) Review and new evidence on composite innovation indicators for evaluating national performance, Research Policy, Vol. 39, pp Kearns, D. (1986) Quality improvement begins at the top, in Bowles, J. (Ed.), World 20, No. 5. Kindermann, G.E., McCallum, I., Fritz, S. & Obersteiner, M. (2008) A global forest growing stock, biomass and carbon map based on FAO statistics. Silva Fennica 42(3): OECD, European Commission, Joint Research Centre (2008). Handbook on Constructing Composite Indicators: Methodology and User Guide, by Nardo, M. M. Saisana, A. Saltelli and S. Tarantola (EC/JRC), A. Hoffman and E. Giovannini (OECD), OECD publication Code: E1. ESPON (2010) ReRISK - Regions at Risk of Energy Poverty project. Final report. Available: European Commission (2003) Third European Report on Science & Technology Indicators. European Commission (EC) (2010a) Energy A strategy for competitive, sustainable and secure energy. COM(2010) 639 final. European Commission (EC) (2010b) European Innovation Scoreboard (EIS) Available: European Commission (2010c) CORDIS - Regional Research & Innovation Service, Northern. Available: Accessed: European Commission Joint Research Centre (JCR) (2010) Regional Competitiveness Index. Authors: Paola Annoni and Kornelia Kozovskal. European Environment Agency (EEA) (2007a) Total energy intensity - outlook from EEA. European Environment Agency (EEA) (2007b) Estimating the environmentally compatible bioenergy potential from agriculture. EEA Technical report No 12/2007. European Environment Agency (EEA) (2009) Europe's onshore and offshore wind energy potential. Technical report No 6/2009, Published: 08 Jun Available: European Environment Agency (EEA) (2010) Annual European Union greenhouse gas inventory and inventory report Technical report No 6/2010. World Economic Forum (WEF) (2010) The Global Competitiveness Report Available: International Energy Agency (IEA) (2004) World Energy Outlook. World Energy Council (WEC) (2007) Survey of energy resources. Available: 37

38 Annex I: Description of the indicators

39 (Ia) Energy intensity (FEC per GDP) Description: Energy intensity was calculated as a ratio between final energy consumption (FEC) and gross domestic product (GDP). Source : FEC- Eurostat Energy Statistics (nrg_100a), Indicator: Final energy consumption, Unit: Thousand tonnes of oil equivalent (TOE) Geo: GDP Eurostat Economy and finance (nama_gdp_c) Indicator: Gross domestic product at market prices Unit: Millions of euro Geo: Source regions: FEC RESGen regional sources Indicator: Final energy consumption, Unit: Thousand tonnes of oil equivalent (TOE) Geo: NUTS level 2 (ES21, UKK3, HU31), NUTS level 3 (FI195), GDP Eurostat General and regional statistics (nama_r_e2gdp) Indicator: Gross domestic product (GDP) at market prices Unit: Millions of euro Geo: NUTS level 2 (ES21, UKK3, HU31), NUTS level 3 (FI195) Notes: Regional statistics not available for HU31-> Replaced with HU (Ib) Description: Energy use (FEC per capita) As an indicator of absolute energy used, the final energy consumption was divided by population of the country or region. Source : FEC- Eurostat Energy Statistics (nrg_100a), Indicator: Final energy consumption, Unit: Thousand tonnes of oil equivalent (TOE) Geo: Population Eurostat population statistics (demo_pjan) Indicator: Population on 1 January by age and sex Unit: Total population Geo: Source regions: FEC RESGen regional sources Indicator: Final energy consumption, Unit: Thousand tonnes of oil equivalent (TOE) Geo: NUTS level 2 (ES21, UKK3, HU31), NUTS level 3 (FI195), Population Eurostat population statistics (demo_pjan) Indicator: Population on 1 January by age and sex Unit: Total population Geo:, ES21, UKK3, FI19, HU31 Notes: Regional electricity consumption statistics not available for HU31 -> The indicator replaced with HU

40 (Ic) Share of renewable energy generation of electricity consumption (%) Description: The share of renewables in electricity production was calculated by dividing the gross electricity generation (GWh) from renewable sources (sum of gross electricity generation from hydro power plants, geothermal power plants, biomass-fired power stations, wind turbines, photovoltaic systems, and solar thermal systems) by electricity consumption (GWh). Source : Gross electricity generation from renewable sources - Eurostat Energy Statistics (nrg_1071a) and (nrg_1072a), Indicator: sum of gross electricity generation from hydro power plants, geothermal power plants, biomass-fired power stations, wind turbines, photovoltaic systems, and solar thermal systems Unit: Gigawatt hours (GWh) Geo: Electricity consumption - Eurostat Energy Statistics (nrg_105a), Indicator: Electrical energy - Final energy consumption Unit: Gigawatt hours (GWh) Geo: Source regions: Gross electricity generation from renewables RESGen regional sources Indicator: Electricity generation from renewable sources Unit: Gigawatt hours (GWh) Geo: NUTS level 2 (ES21, UKK3, HU31), NUTS level 3 (FI195) Electricity consumption - RESGen regional sources Indicator: Electricity consumption Unit: Gigawatt hours (GWh) Geo: NUTS level 2 (ES21, UKK3, HU31), NUTS level 3 (FI195) Notes: Regional statistics of electricity generation from renewable sources not available for UKK3 and HU31-> Replaced with UK and HU (Id) Description: Greenhouse gas emissions (t per capita) Greenhouse gas emissions was calculated as a ratio between GHG emissions (in tonnes of CO2-equivalents) and population. Source : Greenhouse gas emissions EEA (2010) Annual European Union greenhouse gas inventory and inventory report 2010; Technical report No 6/2010 Indicator: Greenhouse gas emissions in CO2-equivalents (excluding carbon sinks) Unit: tonnes of CO2-equivalents Geo: Population Eurostat population statistics (demo_pjan) Indicator: Population on 1 January by age and sex Unit: Total population Geo: Source regions: Greenhouse gas emissions - RESGen regional sources Indicator: Greenhouse gas emissions in CO2-equivalents Unit: tonnes of CO2-equivalents Geo: NUTS level 2 (ES21, UKK3, HU31), NUTS level 3 (FI195) Population Eurostat population statistics (demo_pjan) Indicator: Population on 1 January by age and sex

41 Notes: Unit: Total population Geo: NUTS level 2 (ES21, UKK3, HU31), NUTS level 3 (FI195) Regional statistics of GHG emissions not available for FI195 and HU31-> Replaced with FI and HU (IIa) Description: Source and regions: Solar power potential (kwh/km2) The final indicator is the yearly average sum of irradiation per square meter, based on measurement for 566 ground meteorological stations distributed over Europe and combined with land cover data. JRC (2010) Photovoltaic Geographical Information System (PVGIS)- Geographical assessment of solar resource and performance of photovoltaic technology. Online tool available at: Indicator: Yearly average sum of global irradiation per square meter received by the modules of the given system Unit: kwh/m2 Time: Y2006 Geo: and regions NUTS level 2 (ES21, UKK3, HU31), NUTS level 3 (FI195) (IIb) Description: Source : Onshore wind energy potential estimation for year 2030 (TWh/year) Estimations based on wind speed data that is then used along with projections of wind turbine technology development to calculate the maximum amount of wind energy that could be generated (the technical potential) in 2020 and 2030 taking into account environmental and economical constraints. EEA (2009) Europe's onshore and offshore wind energy potential. Technical report No 6/2009, Published: 08 Jun Available: Indicator: Total generation potential of wind energy on land Unit: TWh/year Time: Y2030 Geo: Source regions: ESPON (2010) ReRisk Regions at Risk of Energy Poverty Final report (Annex 2). This report utilized the EEA (2009) wind energy potential calculations as basis for their NUTS2 estimations. Indicator: Total generation potential of wind energy on land Unit: TWh/year Time: Y2030 Geo: NUTS level 2 (ES21, UKK3, FI19, HU31) (IIc) Description: Technically exploitable hydro power capacity (TWh/year) Estimations based on technically exploitable hydro power resources in national level. Source : WEC (2007) Survey of energy resources. Available: /publications/survey_of_energy_ resources_2007/default.asp Indicator: Technically exploitable hydro power capacity Unit: TWh /year Time: Y2007 Geo: Source regions: Estimations based on WEC national hydro power capacity by using areas of countries and corresponding NUTS2 regions as estimation scales. Indicator: Technically exploitable hydro power capacity Unit: TWh/year

42 (IId) Time: Y2007 Geo: NUTS level 2 (ES21, UKK3, FI19, HU31) Forest area (km2) Description: Forest area is used as an estimator of forest based biomass potential. Although more sophisticated estimations exist (see e.g. Kindermann et al. 2008), they seem to be largely based on forest stock. Of course the forest stock or yearly growth is dependent on forest type and climate conditions, for example, and economical and environmental constraints exist for forest use. Independently to these constraints, forest area is considered to be sufficient proxy for forest based biomass potential. Source : Forest area - FAO Statistics Indicator: Forest area Unit: km2 Geo: Source regions: Forest area ESPON database Indicator: Forest area Unit: km2 Geo: NUTS level 2 (ES21, UKK3, HU31), NUTS level 3 (FI195) (IIe) Arable land area (km2) Description: Arable land area is by definition meaning land that can be used for growing crops, but it is not necessarily currently being used by agriculture. Although very sophisticated models of agriculture based biomass exist (see e.g. EEA, 2007), the availability of arable land is the largest contributor to the estimations. Naturally, other issues like land use for producing crops for alimentation, climate, crop yield, and economical constraints etc. influence the agricultural biomass potential. Source : Arable land area - FAO Statistics Indicator: Arable land area Unit: km2 Geo: Source regions: Arable land area ESPON database Indicator: Arable land area Unit: km2 Geo: NUTS level 2 (ES21, UKK3, HU31), NUTS level 3 (FI195) (IIIa) Description: Source : Objective for share of energy from renewable sources in 2020 (% of FEC) The objective for share of energy from renewable sources in 2020 is considered as an indicator of sustainable energy governance capacity. DIRECTIVE 2009/28/EC On the promotion of the use of energy from renewable (23 April 2009) Available: Unit: % Geo: Source regions: RESGen regional sources Unit: % Geo: NUTS level 2 (ES21, UKK3, HU31), NUTS level 3 (FI195)

43 Notes: Regional objectives for energy generation from renewable sources are not available for HU31 and UKK3 -> Replaced with HU and UK. (IIIb) Achievement towards to the objective of renewable energy in 2020 (%) Description: Source : The achievement of the objective for share of energy from renewable sources is considered as an indicator of sustainable energy governance capacity. It is calculated by dividing the share of energy from renewable sources in gross final consumption of energy in 2005 by the objective for share of energy from renewable sources in DIRECTIVE 2009/28/EC On the promotion of the use of energy from renewable (23 April 2009) Available: Unit: % Time: Y2005 Geo: Source regions: RESGen regional sources Unit: % Geo: NUTS level 2 (ES21, UKK3, HU31), NUTS level 3 (FI195) Notes: Regional objectives for energy generation from renewable sources are not available for HU31 and UKK3 -> Replaced with HU and UK. The regional achievement towards objective is calculated with share of energy from renewable sources in year 2008 in the place of 2005 like for the countries. (IIIc) Objective for GHG (%) Description: Source : The Kyoto objectives for Greenhouse gas emission reductions are considered as indicators of sustainable energy governance capacity. EEA (2010) Annual European Union greenhouse gas inventory and inventory report 2010; Technical report No 6/2010 Indicator: Greenhouse gas emissions targets Unit: % Time: Y2005 for base and Y2012 for objective Geo: Source regions: RESGen regional sources Unit: % Geo: NUTS level 2 (ES21, UKK3, HU31), NUTS level 3 (FI195) Notes: Regional objectives for GHG emissions are not available for FI195, UKK3 and HU31-> Replaced with FI, UK and HU. (IIId) GHG emission change (%) Description: The achievement of the objective for GHG emissions is considered as an indicator of sustainable energy governance capacity. It is calculated by deducting the GHG emissions in 2005 from GHG emissions in 2008 and then dividing the result by the emissions in Source : EEA (2010) Annual European Union greenhouse gas inventory and inventory report 2010; Technical report No 6/2010 Indicator: Greenhouse gas emissions targets 2012, Greenhouse gas emissions 2005, 2008 Unit: t Time: Y2005 for base and Y2008 for achievement Geo: Source regions: RESGen regional sources

44 Notes: Unit: t Time: Y2005 for base and Y2008 for achievement Geo: NUTS level 2 (ES21, UKK3, HU31), NUTS level 3 (FI195) Regional objectives for GHG emissions are not available for FI195, UKK3 and HU31-> Replaced with FI, UK and HU. (IVa) Description: Source : Notes: Electricity prices for domestic consumers (e per kwh) Electricity price for domestic consumers is expected to be an indicator of demand in the electricity markets. When electricity prices are high, there is larger demand for renewable electricity sources and efficient use of electricity. (In the case of electricity prices the relation is not so straight forward as the electricity prices are very seldom based solely on market prices.) Eurostat Energy Statistics (nrg_pc_204), Indicator: Electricity - domestic consumers - half-yearly prices - New methodology from 2007 onwards Unit: Euros per Kilowatt hour (VAT excluded) Time: Y2010 S1 Geo: No regional values existing. For index calculation national electricity prices were used for ES, UK, FI and HU. (IVb) Description: Source : Notes: Electricity prices for industrial consumers (e per kwh) Electricity price for industrial consumers is expected to be an indicator of demand in the electricity markets. When electricity prices are high, there is larger demand for renewable electricity sources and efficient use of electricity. (In the case of electricity prices the relation is not so straight forward as the electricity prices are very seldom based solely on market prices.) Eurostat Energy Statistics (nrg_pc_205), Indicator: Electricity - industrial consumers - half-yearly prices - New methodology from 2007 onwards Unit: Euros per Kilowatt hour (VAT excluded) Time: Y2010 S1 Geo: No regional values existing. For index calculation national electricity prices were used for ES, UK, FI and HU. (IVc) Population growth projection 2020 (%) Description: Source and regions: Source and regions: Although the relationship between population growth and energy demand is not necessarily linear, it is considered that population growth expectation is one of the factors driving the demand for sustainable energy. The growth percent was calculated by utilizing the population projection in 2020 and population in 2008 as a base year. Population projection Eurostat population statistics (proj_08c2150p), Indicator: Population projections Unit: Total population Time: Y2020 Geo:, ES21, UKK3, FI19, HU31 Population Eurostat population statistics (demo_pjan) Indicator: Population on 1 January by age and sex Unit: Total population Geo:, ES21, UKK3, FI19, HU31

45 Notes: Population growth projection statistics are not available in NUTS3 level -> FI195 was replaced with FI19 (IVd) GDP growth (%) Description: Source and regions: Notes: Although the relationship between population growth and energy demand is not necessarily linear, it is considered that population growth expectation is one of the factors driving the demand for sustainable energy. Eurostat Population Statistics (proj_08c2150p), Indicator: Population projections Unit: Total population Time: Y2020 Geo:, ES21, UKK3, FI19, HU31 Population growth projection statistics are not available in NUTS3 level -> FI195 was replaced with FI19 (IVe) Employment in high energy consumption (%) Description: Source and regions: Notes: The more dependent the economy is on industries that have high energy consumption levels, the more demand there is for energy. The high energy consumption sectors were defined to be the following: Manufacture of pulp, paper and paper products; Manufacture of coke, refined petroleum products and nuclear fuel; Manufacture of chemicals and chemical products; Manufacture of rubber and plastic products; Manufacture of other non-metallic mineral products; Manufacture of basic metals; Transport, storage and communication. The indicator was calculated as percentage of employment in these sectors of total employment. Employment on high energy consumption sectors - Eurostat Employment by economic activity (lfst_r_lfe2en1) Indicator: employment Unit: Total persons Geo:, ES21, UKK3, FI19, HU31 Total employment - Eurostat Employment - LFS adjusted series (lfsi_emp_a) Indicator: employment Unit: Total persons Geo:, ES21, UKK3, FI19, HU31 Employment statistics in sectors was not available in NUTS3 level -> FI195 was replaced with FI19 (IVf) Description: Source and regions: Notes: Heating degree days Heating degree day is a measurement of the demand for energy needed for heating. It is derived from measurements of outside air temperature. Eurostat Heating degree-days (nrg_esdgr_a) Indicator: heating degree day (annual data) Unit: Actual heating degree-days Geo:, ES21, UKK3, FI19, HU31 Heating degree day statistics were not available in NUTS3 level -> FI195 was replaced with FI19. (Va) R&D intensity (% of R&D expenditures of GDP)

46 Description: Source and regions: Notes: R&D intensity is the most typical measure of innovation inputs. It was calculated by dividing the R&D expenditures by GDP. R&D expenditures - Eurostat R&D expenditure at national and regional level (rd_e_gerdtot) Indicator: Total intramural R&D expenditure (GERD) Unit: Millions of euro Geo:, ES21, UKK3, FI19, HU31 GDP Eurostat General and regional statistics (nama_r_e2gdp) Indicator: Gross domestic product (GDP) at market prices Unit: Millions of euro Geo:, ES21, UKK3, FI19, HU31 R&D expenditure statistics were not available in NUTS3 level -> FI195 was replaced with FI19. (Vb) Description: Source and regions: Notes: Patents per capita Number of patents is a typical indicator of innovation output (although it may be biased to high-tech sectors). The indicator used was EPO patent applications per million of inhabitants. Eurostat - Patent applications (pat_ep_ntot) and (pat_ep_rtot) Indicator: Patent applications to the EPO per million of inhabitants Unit: Patents per per million of inhabitants Geo:, ES21, UKK3, FI19, HU31 Patent statistics were not available in NUTS3 level -> FI195 was replaced with FI19. (Vc) Description: Source and regions: Notes: FP7 projects per capita This indicator was included to give some measure of internationalization of research and innovation activities. Number of FP7-projects European Commission Cordis database Indicator: Number of FP7-projects Unit: Number of projects Time: All projects approved in FP7 until end of Geo:, ES21, UKK3, FI19, HU31 Population Eurostat population statistics (demo_pjan) Indicator: Population on 1 January by age and sex Unit: Total population Geo:, ES21, UKK3, FI19, HU31 For searching regional projects from Cordis-database the search function was applied. The function was first defined to take into account all the national projects and then in the search terms it was added: the name of the region in English OR the name of the region in national language OR NUTSxx. Then the list of projects was manually checked. For testing the validity of the method, some sample of national projects was manually checked with the help of Google maps in order to confirm that the selected key words were returning all the FP7 project of the region. (Vd) Share of patents in energy technologies (%) Description: The share of patents in energy technologies was considered as an indicator of energy intensity of the research. The indicator was calculated by dividing the energy field

47 patents by total patents. Source and regions: Notes: Patent applications in energy technologies - Eurostat Patent applications- (pat_ep_nrgpct) Indicator: Patent applications to the EPO per million of inhabitants in energy technologies Unit: Patents per per million of inhabitants Geo:, ES21, UKK3, FI19, HU31 Eurostat - Patent applications (pat_ep_ntot) and (pat_ep_rtot) Indicator: Patent applications to the EPO per million of inhabitants Unit: Patents per per million of inhabitants Geo:, ES21, UKK3, FI19, HU31 Patent statistics were not available in NUTS3 level -> FI195 was replaced with FI19. (Ve) Share of FP7-projects in energy (%) Description: Source and regions: Notes: The share of FP7 projects in FP7-ENERGY was included to give some measure of internationalization of research and innovation activities in energy field. It was calculated as a share of total FP7-projects. Of course by taking into account just FP7- ENERGY, several energy field research projects are not taken into account (e.g. RESGen). Number of FP7-ENERGY projects European Commission Cordis database Indicator: Number of FP7-projects in FP7-ENERGY Unit: Number of projects Time: All projects approved in FP7 until end of Geo:, ES21, UKK3, FI19, HU31 Number of FP7-projects European Commission Cordis database Indicator: Number of FP7-projects in FP7-ENERGY Unit: Number of projects Time: All projects approved in FP7 until end of Geo:, ES21, UKK3, FI19, HU31 For searching regional projects from Cordis-database the search function was applied. The function was first defined to take into account all the national projects and then in the search terms it was added: the name of the region in English OR the name of the region in national language OR NUTSxx. Then the list of projects was manually checked. For testing the validity of the method, some sample of national projects was manually checked with the help of Google maps in order to confirm that the selected key words were returning all the FP7 project of the region.

48 Annex II: Data

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