Maps

The map shows the geographical delimitation of cross-border regions and committees financed by the Nordic Council of Ministers as of December 2021.

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.

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.

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.

The map shows the share of cross border commuters in the total employees with residence in a NUTS2 Nordic Region in 2015. The darker the blue, the higher the share. For the most NUTS2 regions in the Nordic, the percent is lower than 0,5%, indicating the commuting workers are the absolute minority in the total employed people. Åland (2,6%) and the South Sweden region (2,7%) stand out with more than 2% of employees in the region commuting cross-border for work. The destination country for Åland workers is Sweden, while for Swedish workers living in the south is Denmark. The commuting pattern is also apparent for the Swedish NUTS2 regions along the border line with Norway, with relatively higher percent of cross border workers commuting to Norway compared with other Nordic NUTS2 regions. At a finer scale (e.g., NUTS3) would show higher percentages in a number of regions, e.g., by taking only the NUTS 3 region – Skåne instead of the NUTS 2 region South Sweden (Skåne+Blekinge) or the border regions between NO and SE.

The map builds on statistics of cross-border commuters with residence in a NUTS2 Nordic region commute for work in 2015. For each NUTS2 region, the map shows the total number of commuters who commute to other Nordic countries for work. The number of commuters is categorised into three groups visualised in different shades: the darker, the higher the number of commuters. In addition, the most common country these commuters commute to from each region is identified by specific colours. For example, the darkest red indicates a region with at least 2,000 commuters working in another Nordic country, of which the largest group number of commuters works in Denmark. The most commuters were from the region of South Sweden (16 543) in 2015, and the majority of them commuted to Denmark for work. Norway is the most popular destination for work commuters in the Nordic Region, e.g., all Swedish regions except for the South Sweden region, all the regions in Denmark except for the Copenhagen region, and Iceland. Sweden is more attractive for work commuters living in Finland, Copenhagen region, and bordering regions in Norway.

The map shows the geographical delimitation of cross-border regions and committees financed by the Nordic Council of Ministers.

To secure better access to general practitioners for the rural population, the region of Västerbotten has developed the concept of virtual healthrooms (VHRs). These VHRs are unstaffed, which means that they have no regular health personnel in situ. They are equipped with distance-spanningtechnology, which means that patients can go there to take consultations from a practitioner online, conducting health checks such as measuringblood pressure or heart rate. The coloured patches on the map show those populated areas in Västerbotten where inhabitants can expect a reduction of travel distance to primary health care through the implementation of VHRs. The coloured patches are populated areas in Västerbotten (by 1000*1000m grid) with improved accessibility of health care resulting from the implementation of virtual health rooms. The colour indicates the total distance reduced.Distance is measured as being via the road network. The average distance to the closest primary health care facility (health centre or virtual health room) is 6 km for the overall population in Västerbotten. The implementation of VHRs means that around 3.5% of the 270,000 inhabitants of Västerbotten experience increased accessibility toa primary health care service. The travel distance for this portion of the population has been cut by almost 50%, from 42 km per person to 23 km per person. Patients may also use virtual health rooms to conduct teleconsultations with health professionals at specialised hospitals, which creates even greater potential from an accessibility standpoint.

The map illustrates the accessibility of highly specialised care in Västerbotten in Sweden. The colours represent car ride times in minutes from the place of residency to the nearest health care facility within a certain service type, with a travel range of 10 minutes to two hours. The health care facilities are also located on the map. The only hospital offering highly specialised in-patient care is located in Umeå, and half of the regional population (51.0%) can reach the hospital within a 40-minute car ride. For the inhabitants of Storuman, however it takes more than a two-hours car ride to access specialised care in the hospital. The accessibility of health care services for rural dwellers are improved by means of distance-spanning digital solutions. Instead of making the effort of physically visiting a health care facility, they can access video consultations with general practitioners via virtual health care rooms, and then decide whether a follow-up physical visit to the specialised hospital is needed.

The map illustrates the accessibility of in-patient care in Västerbotten in Sweden. The colours represent car ride times in minutes from the place of residency to the nearest health care facility within a certain service type, with a travel range of 10 minutes to two hours. The health care facilities are also located on the map. The accessibility of in-patient care shares similar characteristics to that of access to dropin out-patient care. The ten facilities mentioned above provide in-patient care, too. The only difference is that two facilities outside the region were included in the analysis. But they did not alter the overall picture, suggesting that inhabitants living along the border line tend not to cross the border to seek in-patient care. These hospitals are accessible to 56.3% of the population within a 10-minute car ride, and 97.1% of the population within a one-hour car drive.

The map illustrates the accessibility of out-patient drop-in care in Västerbotten in Sweden. The colours represent car ride times in minutes from the place of residency to the nearest health care facility within a certain service type, with a travel range of 10 minutes to two hours. The health care facilities are also located on the map. Health care facilities offering out-patient drop-in care are characterised by even geographical distribution across Västerbotten. Each inland municipality has one such heath care facility, apart from Norsjö. All ten facilities offer drop-in care during both office hours and evenings and weekends (24/7). According to the results of our accessibility analysis, 56.% of the regional population can access drop-in out-patient care within a ten minute car ride. Within a half-hour car ride, the drop-in care service is available to 83.3% of inhabitants.

The map illustrates the accessibility of primary care in Västerbotten in Sweden. The colours represent car ride times in minutes from the place of residency to the nearest health care facility within a certain service type, with a travel range of 10 minutes to two hours. The health care facilities are also located on the map. The 37 health care facilities in the region contribute to widespread coverage of primary outpatient care for the population in Västerbotten. Over 80% of the inhabitants can access such health care services within a 10-minute car ride, and a half-hour car ride can cover 95.8% of the regional population. In general, coastal municipalities have easier access to health care services than inland municipalities. As one of the inland municipalities, Storuman accommodates one health care facility which provides most of the health care services expected in relation to highlyspecialised care.

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).

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.

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…

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.

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.

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.

As of 1 January 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.