Algae production in 2019
This map shows location of algae production by production method in the Nordic Arctic and Baltic Sea Region in 2019 Algae and seaweeds are gaining attention as useful inputs for industries as diverse as energy and human food production. Aquatic vegetation – both in the seas and in freshwater – can grow at several times the pace of terrestrial plants, and the high natural oil content of some algae makes them ideal for producing a variety of products, from cosmetic oils to biofuels. At the same time, algae farming has added value in potential synergies with farming on land, as algae farms utilise nutrient run-off and reduce eutrophication. In addition, aquatic vegetation is a highly versatile feedstock. Algae and seaweed thrive in challenging and varied conditions and can be transformed into products ranging from fuel, feeds, fertiliser, and chemicals, to third-generation sugar and biomass. These benefits are the basis for seaweed and algae emerging as one of the most important bioeconomy trends in the Nordic Arctic and Baltic Sea region. The production of algae for food and industrial uses has hence significant potential, particularly in terms of environmental impact, but it is still at an early stage. The production of algae (both micro- and macroalgae) can take numerous forms, as shown by this map. At least nine different production methods were identified in the region covered in this analysis. A total of 41 production sites were operating in Denmark, Estonia, the Faroe Islands, Iceland, Norway, Germany, and Sweden. Germany has by far the most sites for microalgae production, whereas Denmark and Norway have the most macroalgae sites.
Change in share of biofuels in transport from 2010 to 2018
This map shows change in share of biofuels in final energy consumption in transport in the Nordic Arctic and Baltic Sea Region from 2010 to 2018. Even though a target for greater use of biofuels has been EU policy since the Renewable Energy and Fuel Quality Directives of 2009, development has been slow. The darker shades of blue on the map represent higher increase, and the lighter shades of blue reflect lower increase. The lilac color represent decrease. The Baltic Sea represents a divide in the region, with countries to the north and west experiencing growth in the use of biofuels for transport in recent years. Sweden stands out (16 per cent growth), while the other Nordic countries has experienced more modest increase. In the southern and eastern parts of the region, the use of biofuels for transport has largely stagnated. Total biofuel consumption for transport has risen more than the figure indicates due to an increase in transport use over the period.
Share of biofuels in transport in 2018
This map shows the share of biofuels in final energy consumption in transport in the Nordic Arctic and Baltic Sea Region in 2018. There has been considerable political support for biofuels and in the EU, this debate has been driven by the aim of reducing dependency on imported fuels. For instance, 10 per cent of transport fuel should be produced from renewable sources. The darker shades on the map represent higher proportions, and the lighter shades reflect lower proportions. As presented by the map, only Sweden (20.7%) had reached the 10 per cent target in the Nordic Arctic and Baltic Region in 2018. Both Finland (8.3%) and Norway (8.3%) were close by the target, while the other countries in the region were still lagging behind, particularly the Baltic countries.
Tertiary education attainment level of 30- to 34-year-olds 2019
The map shows the proportion of the population aged 30-34 years old, who had a tertiary education at the European level in 2019. Purple shades indicate higher proportions, and pinkish shades reflect lower proportions. It is common to show the education attainment for the age group 30-34 since it is an age group where most people have finalised their studies. The focus on this age group makes it easier to see recent trends and outcomes of policies. Overall, over 40% of Europeans aged 30-34 years old had a tertiary education in 2019. Young people in the Nordic countries are among the most educated, with approximately half of 30 to 34-year-olds achieving a tertiary education across all Nordic countries. The highest proportions can be found in the capital regions. Stockholm is particularly noteworthy, with over 60% of 30 to 34-year-olds having had a tertiary education. Regions with prominent universities also stand out – for example, Skåne, Uppsala, Västerbotten and Västra Götaland (Sweden), Trøndelag (Norway) and Østjylland (Denmark).
Smart specialisation domains in Swedish regions
This map gives an overview of the S3 focus areas in the Swedish regions in 2019. The major S3 domains in Sweden shown in the map provide a good overview of the key specialisation areas in Sweden. It is possible, for example, to check which Swedish regions have “green”, “sustainable”, “environment” at their smart specialisation domains (marked in green in their respective infoboxes for the domains in the figure). The information illustrated in the map can assist Swedish regions when they are considering opportunities for S3 synergy and co-operation with each other. In Sweden, the Swedish Agency for Economic and Regional Growth (Tillväxtverket) is a central actor in assisting regions in their work with smart specialisation. Tillväxtverket promotes opportunities for cooperation between the Swedish regional S3 processes and provides relevant information and learning seminars related to S3. Read the digital publication here.
EU JRC S3 Platform 2019 in Nordic regions
This map shows the Nordic Regions that have registered on the EU JRC S3 platform, status as of October 2019. The regions that have registered on the S3 platform receive practical advice and broadened opportunities for international networking. In October 2019, there were 182 EU regions registered on the S3 platform, as well as 18 non-EU Member State regions. Of these regions, 38 are Nordic. It is worth noting that, as a non-EU member state, Norway has seven registered regions on the platform. Registration on the S3 platform is by no means a guarantee of success of a regional smart specialisation process, but it indicates the willingness of the region to learn more about S3 and to participate in international and interregional S3 cooperation through the possibilities provided by the S3 platform. The smart specialisation concept has been diffusing rapidly across Europe in the 2010s, as an increasing number of regions adopt it and design strategies departing from their own preconditions. The S3 platform in Seville, Spain, hosted by the Institute for Prospective Technological Studies (IPTS), was established in 2011 to assist EU countries and regions to develop, implement and review their smart specialisation strategies. The S3 platform provides information, methodologies, expertise and advice to national and regional policymakers, promotes mutual learning and transnational co-operation, and contributes to academic debates around the concept of smart specialisation. Read the digital publication here.
Regional innovation scoreboard 2019
This map shows the regional innovation scoreboard (RIS) in the European regions in 2019. The small map shows the innovation scoreboard at national level. The index shows the performance of innovation systems, classified into four main performance groups (leader, strong, moderate and modest). The European innovation scoreboard provides a comparative assessment of the research and innovation performance in European countries. It assesses the relative strengths and weaknesses of national innovation systems and helps countries identify areas they need to address. The Regional innovation scoreboard (RIS), a regional extension of the European innovation scoreboard, assesses the innovation performance of European regions on a limited number of indicators. The RIS 2019 covers 238 regions across 23 EU countries, as well as Norway, Serbia and Switzerland. Cyprus, Estonia, Latvia, Luxembourg and Malta are also included at country level. The RIS 2019 is a comparative assessment of regional innovation based on the European innovation scoreboard methodology, using 18 of the latter’s 27 indicators. It provides a more detailed breakdown of the performance groups with contextual data that can be used to analyse and compare structural economic, business and socio-demographic differences between regions. The Nordic regions are doing well in an overall RIS comparison regarding innovation performance. There are, however, considerable differences in innovation performance between the Nordic regions. For example, the capital regions have higher levels of innovation performance than more rural and peripheral regions, according to RIS 2019. This is often due to the critical mass of companies and the spatial significance of the proximity of firms and entrepreneurs, enabling knowledge-sharing and spill-over effects. Read the digital publication here.
Higher educational institutions in the Arctic
The map shows universities and other educational institutions on post-secondary and tertiary level located in the Arctic. The red circles indicate a location of a university, college, or campus areas within the Arctic. The size of the circle corresponds to the number of educational institutions in a specific location. There is a high density of educational education institutions around Anchorage (Alaska), in Iceland, the Faroe Islands and the Arctic Fennoscandia (see zoom-in maps). In the Yukon (Canada), the Yukon College is the main educational institution, which has several campus areas across the region. In the Russian Arctic the largest centres with higher educational institutions are in Murmansk, Naryan-Mar (Nenets), Nizhnevartovsk (Khanty-Mansi), Salekhard (Yamalo-Nenets), and Yakutsk (Sakha).
Early leavers from education and training 2016
This map shows the percent of early school leavers in the Nordic Region (NUTS 2 level) and Baltic states in 2016, calculated as the total number of individuals aged 18-24 having a lower secondary education as the highest level attained and not being involved in further education or training. The numbers in each region indicate the proportion of females per 100 males. The yellow/red shading indicates the percent of early school leavers in 2016. The lighter the colour the lower the percentage of early school leavers in 2016. The grey colour indicates no data. Early school leaving is of concern in the Nordic Region to varying degrees. From a pan-European perspective, the Danish (7.2%), Swedish (7.4%) and Finnish (7.9%) averages all fall below the EU average (10.7%) and are in line with the Europe 2020 target of below 10%. The Norwegian average (10.9%) remains slightly above the target but is comparable to the EU average. The average rate of early school leaving in Iceland (19.8%) is substantially higher than the other Nordic countries and the EU average. There is both a spatial and a gender dimension to this problem. The spatial dimension of early school leaving is highlighted in this map, which shows rates of early school leaving in the Nordic Region at the NUTS 2 level. The map highlights the comparatively high rates in Norway, particularly in the north. It is worth noting that, although still high in a Nordic comparative perspective, early school leaving rates have decreased in all Norwegian regions since 2012. Rates are also high in Greenland, with a staggering 57.5% of young people aged 18–24 years who are not currently studying and who have lower secondary as their highest level of educational attainment. The map also shows the gender dimension of early school leaving, with…
Household access to high capacity fixed broadband 2016
This map shows the household access to high capacity fixed broadband for all Nordic municipalities in 2016. The blue shading indicates the percentage of household with access to high capacity fixed broadband speed of at least 30 mbit/s in 2016. The darker the blue the larger the percentage of household with access to high capacity fixed broadband speed in the municipality, while the brightest colours represent municipalities with a low share. The grey colour indicate municipality with no data. High capacity fixed broadband coverage enhances access to digital solutions in both rural and urban contexts across the Nordic Region, thus making these areas good places to live, work and run a business domestically and across national borders. At a municipal level the household coverage by high capacity fixed broadband shows a more varied picture than that at the regional level. The average figure for Nordic municipalities was 63% in 2016, with more homogeneous figures in Denmark and Sweden than in Norway and Finland. The variation between neighbouring municipalities reflects the decision at the municipal level to prioritise investments in broadband infrastructure development as well as the nurturing of a favourable climate for the establishment of data centres requiring fast broadband networks, among other things. Fifteen Nordic municipalities, located in Sweden and Norway, had already reached the 100% mark for household coverage by high capacity fixed broadband in 2016. In Sweden, these municipalities are located in both the capital city region and in Skåne. In Norway, they are found in the more remote and rural parts of Møre og Romsdal (e.g. Giske), Troms (i.e. Lavangen) and Finnmark regions (Båtsfjord). Municipalities having values above 90% are mostly located in capital city regions as well as in more rural contexts in Jylland (Denmark), southern Sweden and northern Finland and Norway. One explanation for…
Next Generation Access coverage 2016
This map shows the Next Generation Access (NGA) network coverage in European regions in 2016. The blue shading indicates the percentage of household covered by NGA broadband in European NUTS 3 regions. The darker the blue the larger the percentage of household covered by NGA broadband in the region, while the brightest colours represent regions with a low share. Regions with relatively small territories and important population densities stand out in terms of high NGA network coverage, e.g. urban regions in the Netherlands and Switzerland. Capital city regions also have high NGA network coverage scores, while the more rural regions continue to lag, e.g. in parts of France and Poland. The Nordic countries are characterised by having almost no differences within their territories, i.e. no large variation in terms of NGA network coverage, unlike the clear regional differences in countries such as France or Italy. All regions in the Nordic countries score in the range of 65% to 95% of households having NGA network coverage, except for Etelä-Pohjanmaa in Finland which has a coverage range of 35% to 65% and the Danish statistical region of Østjylland and the capital regions of Denmark and Iceland with scores between 95% and 100% respectively. The relatively high figures for the Nordic Region can in part be explained by the existence of national and regional digitalisation strategies over the last decade or so. In Denmark, as well as in the other Nordic countries, digitalisation has long been on the national agenda. One of the main goals of these strategies has been to increase the growth and productivity of the business community – and to make it easier and cheaper to establish digital infrastructure. The regional level has an important role to play in the development of digital infrastructure, hence the relevance of the elaboration…
Gross Regional Product per capita in million PPP 2015
This map shows the gross regional product per capita in million purchasing power parity (PPP) in all Nordic and Baltic Regions in 2015. The green tones indicate regions with a gross regional product per capita above the EU28 average. The darker the tone the higher the gross regional product per capita. The brown/yellow shading indicates regions with a gross regional product per capita below the EU28 average. The darker the tone the lower the gross regional product per capita. In economic development terms, the Nordic Region continues to perform well in relation to the EU average. Urban and capital city regions still show high levels of GDP per capita reflecting the established pattern throughout Europe. Stockholm, Oslo, Helsinki, Copenhagen and the western Norwegian regions are among the wealthiest in Europe, again confirming that the capital regions and larger cities are the strongest economic centres in the Nordic Region. In addition to these urban regions, some others also display high levels of GRP per capita. What is interesting is that in the aftermath of the economic crisis some second-tier city regions, such as Västra Götaland with Gothenburg in Sweden, are now also displaying fast growth rates as indeed are some less metropolitan regions in the western part of Denmark. These regions display GRP per capita levels which correspond to, or even exceed, those of most metropolitan regions in Europe. most of the central and eastern parts of Finland remain below the EU average.
Change of total R&D expenditure 2007-2015
This map shows the change in the expenditure in research and development (R&D) between 2007 and 2015 in the Nordic Region. The blue tones indicate regions with a positive change in the R&D expenditure between 2007 and 2015. The red tones indicate regions with a negative change in the R&D expenditure between 2007 and 2015. The darker the colour, the stronger the change. The grey colour indicates no data. The bars indicate the value of expenditure in R&D in million euros, the light brown for 2007 and the dark brown for 2015. The values of 2007 expenditures were adjusted to 2015 price level. High levels of Research and Development (R&D) expenditure are viewed as a vital enabling factor for innovation which is one of the key policy components of the Europe 2020 Strategy. At the regional level in Finland, most regions have shown a downward trend in R&D expenditures both in percentage and in real terms. This reflects the challenging fiscal policy and economic conditions experienced in Finland after the global financial crisis in 2008 and the difficulties faced by the ICT sector, where R&D is highly concentrated, as well as the inability of other industries to compensate for the decline of the ICT sector. Åland on the other hand has shown a positive trend in R&D expenditures, although having very low values in absolute numbers for both years. Sweden has experienced a dramatic decrease (< -6.1%) in R&D expenditures in Värmland, Blekinge and Gotland which was in large part, if not entirely, caused by the relocation of important R&D facilities/resources. Similarly, some major closures and redundancy notifications led to substantial cuts in R&D spending in Skåne in 2015, despite the increasing number of start-ups. For Iceland, available data for the 2013–2016 period indicates a steady and stable increase in…
European eco-innovation parks in 2014
This map shows the location of the European eco-innovation parks in the states of the European Union (EU) and Iceland in 2014. The eco-innovation parks are eco-industrial parks and eco-innovative areas combining residential and industrial activities. The establishment of eco-industrial parks is one way to promote eco-innovation, resource efficiency and the circular economy. They create new and innovative business opportunities and improve ecosystems. The blue colour indicates the EU-member states having at least one identified eco-innovation park in 2014. The circles indicate the location and the type of the eco-innovation parks (green, in use; yellow, under construction; brown, planned; red, stopped). The grey colour indicates no data. A clear spatial concentration of eco-innovation parks can be observed in the Ruhr area and around Leipzig (Germany), in southern Belgium, south-western and northern Netherlands, western Switzerland, along the “arch” stretching from Turin via Pisa to Udine in North-East Italy, around Barcelona as well as around some national borders in the industrialised parts of north-West Europe namely Germany-Netherlands-Belgium-France and Switzerland. Eco-innovation parks differ in many respects, for instance regarding energy and material flows (e.g. waste heat, steam, power; wood chips, bark, ash, pulping chemicals), number and size of companies involved and jobs created, public sector involvement and finally, in terms of (the drivers behind) their evolution. The latter means that some parks evolved and expanded around a few economic activities and companies (e.g. saw mills) to include further activities (e.g. pulp mills, power plants etc.), while others were intentionally planned and put into operation.
NEET rate for young people 18-25 years in 2016
Share of young people aged 18-25 years neither in employment nor in education and training in 2016 There are a range of reasons why a young person may become part of the “NEETs” group, including (but not limited to): complex personal or family related issues; young people’s greater vulnerability in the labour market during times of economic crisis; and the growing trend towards precarious forms of employment for young people. Successful reengagement of these young people with learning and/or the labour market is a key challenge for policy makers and is vital to reducing the risk of long-term unemployment and social exclusion later in life. The map highlights two Polish regions, Podkarpackie and Warminsko-Mazurskie, as having the highest NEET rates in the BSR. High rates can also be found in several other Polish regions as well as the Northern and Eastern Finland Region. The lowest NEET rates in the Baltic Sea Region can be found in the Norwegian Capital Region, followed by several regions in Sweden, Norway and Denmark.
Youth unemployment rate in 2016
The map highlights two Polish regions, Podkarpackie and Lubuskie, as having the highest youth unemployment rates in the BSR. Several other regions in Poland, along with regions in Northern Finland, Central Sweden and the southernmost Swedish region of Skåne, have also been rather severely hit by youth unemployment. The lowest youth unemployment rates can be found in several Russian regions, among them St. Petersburg, and in regions in Northern Germany and Northern Norway.
Tertiary education among working-age population, change 2010-2015
The Nordic countries, as well as Estonia and Lithuania, have had among the highest levels of tertiary education in Europe in recent years. This map demonstrates that many regions, particularly those in Poland, are catching up. Several Lithuanian and Latvian regions have also had high rates of positive change between 2010 and 2015. The most modest growth rates, between 0 and 1.5 percent change, were experienced in two Estonian regions, one in Denmark and one in Northern Germany. Mecklenburg-Vorpommern and Brandenburg, in North-Eastern Germany, were the only regions within the Baltic Sea Region to experience a decrease in the share of working-age persons with tertiary level education from 2010-2015.