Maps

Members of NATO (North Atlantic Treaty Organization) and non-NATO members as of March 2024. Countries that joined after 2015: Montenegro (2017), North Macedonia (2020), Finland (2023) and Sweden (2024). 

Members of NATO (North Atlantic Treaty Organization) and non-NATO members as of April 2023. Countries that joined after 2015: Montenegro (2017), North Macedonia (2020), and Finland (2023). 

The map shows all routes in our sample with significant travel time benefit for electric aviation. They are 203 in total. A route has a significant travel time benefit if the travel time for both car and public transportation exceeded 1,5 times the travel time for electric aviation. I.e., if one of the existing transport modes is faster or up to 1,5 times the travel time for electric aviation, electric aviation does not have the potential to improve accessibility between the two destinations, according to our analysis.

The map shows all routes between urban and rural areas where electric aviation has significant time benefits compared to other traffic modes. Yellow lines are already served by aviation, while blue color indicates non-existent routes where electric flight would reduce the travel time between destinations. Our motivation for focusing on urban-rural routes was based on the assumption that electric aviation can increase the access for rural areas to public facilities and job opportunities, as well as the possibility of connecting remote areas with national and international transport systems. The result, though, can only be understood in terms of travel time benefits between the areas, and thus reveals little about accessibility to mentioned opportunities. The following are examples of themes to be investigated further within the main project. Identify regional hubs Among others, the project FAIR (2022) has addressed the need to update the flight system to a more flexible aviation network, that meet travelers’ needs with smart mobility. This can be done by identifying demands and establishing regional hubs for electric aviation, which can serve remote and regional areas. The potential of Hamar and Bodö in Norway as regional hubs should be studied more closely.

The map shows all routes between urban areas separated by water, and where electric aviation has significant time benefits compared to the fastest traffic mode. Yellow lines are already served by aviation, while red color indicates non-existent routes where electric flight would reduce the travel time between destinations. The result is in line with our assumptions, that there is a lack of fast connections between potential labor markets in urban areas, which are geographically close but separated by open water.

The map visualizes all routes with significant travel time benefit, which are already served with commercial flights. Information on existing routes has been obtained from the report Nordic Sustainable Aviation (Ydersbond et al, 2020) and applies to the year 2019. Since then, routes may have been added or removed, which is important to bear in mind in future investigations. However, choosing a later year risk giving equally misleading results, as flights decreased drastically during the pandemic. Statistics for 2019 provide a picture of the demand that existed before the pandemic, which is the latest stable levels that can be obtained. Whether air traffic will ever return to the same levels as before the pandemic is too early to say.   The majority of routes are found in Norway, along the coastline, which confirms earlier knowledge that Norway has a more extensive and coherent aviation network than the rest of the Nordic region.

This map shows the travel time calculations for electric aviation versus travelling by public transportation. Routes represented by any nuance of green, are routes with significant travel time benefits for electric aviation in comparison with public transportation. The darker the nuance of green, the larger time benefit for electric aviation. The beige color represents routes where the travel time for public transportation is the same or up to 1,5 times the travel time for electric aviation. The red color represents routes where public transportation is faster than electric aviation. Purple lines represent routes where no public transportation is available.  These were also routes where we could see significant time benefits for electric aviation. The number of changes when commuting with public transport may have a negative impact on perceived accessibility. In this accessibility analysis, however, we stay with the same criteria for public transport as for travel by car. For future research, the number of changes when commuting by public transport could be considered in the comparison.

This map shows the travel time calculations for electric aviation versus traveling by car. Routes represented by any nuance of green, are routes with significant travel time benefits for electric aviation in comparison with car. The darker the nuance of green, the larger time benefit for electric aviation. The beige color represents routes where the travel time for car is the same or up to 1,5 times the travel time for electric aviation. The red color represents routes where car is faster than electric aviation.

The map shows all routes with a maximum distance of 200 km divided into three categories, based on the airports’ degree of urbanization: Routes between two rural airports, routes between one rural and one urban airport and routes between two urban airports. The classification is based on the new urban-rural typology. We restricted the analysis to routes between rural and urban areas as well as routes between urban areas that are separated by water. Those are 426 in total. We based our criteria on the assumption that accessibility gains to public services and job clusters can be made for rural areas, if better connected to areas with a high degree of urbanization. Because of possible potential to link labor markets between urban areas on opposite sides of water urban to urban areas that cross water are also included. This is based on previous research which has shown the potential for electric aviation to connect important labor markets which are separated by water, particularly in the Kvarken area (Fair, 2022). Our choice of selection criteria means that we intentionally ignore routes where electric aviation may have a potential to reduce travel times significantly. There might also be other important reasons for the implementation of electric aviation between the excluded routes. Between rural areas, for example, tourism or establishing a comprehensive transport system in the Nordic region, constitute reasons for implementing electric aviation. Regarding routes between urban areas over mainland, the inclusion of more routes with the same rationale as above – that significant time travel benefits could be gained between labor markets with electric aviation (for example between two urban areas in mountainous regions where travel times can be long) – can be motivated. Some of those routes can be important to investigate at a later stage but are outside the…

This map classifies all airports by a degree of urbanisation. The classification is based on the new urban-rural typology. We classified all airports localized within any of the top five urbanization classes (Inner urban area, Local center in rural area, Outer urban area, peri-urban area, or Rural area close to close to urban) as Urban. All other airports, localized within the bottom two classes (Rural heartland or Sparsely populated rural area) were classified as Rural. No adjustments were made based on the proximity of the airports to urban areas. During the process we considered adjustments in the categorization based on the airports’ potential catchment area from a close urban area. For example, one can assume that Gällivare Lappland airport in the north of Sweden, has its main catchment area from Gällivare which is classified as a local center in rural area (i.e. Urban). The airport, though, is localized within the category Rural heartland. Yet, we decided to let the typology determine to which category each airport belong.

This map shows all possible electric aviation routes of a maximum distance of 200 kilometres within the Nordic region. First generation electric aviation will have a limited range due to battery capacity. According to the report Nordic Sustainable Aviation, routes up to 400 kilometers constitute an initial market for electric airplanes in the Nordic region. However, also shorter distance routes under 200 km, where cruise speed is less important and in sparsely populated regions where passenger volumes are very small, will be the focus (Ydersbond et al, 2020). The first generation of aircrafts that rely solely on electric power have a defined maximum range of 200 km (Heart Areospace, 2022). For this accessibility study, we only included routes of a maximum distance of 200 kilometers. This selection gave us 1001 possible routes in total.

This map shows all airports within the geographical scope which may be operated with commercial flight. To limit our selection of airports, we used a combination of two official airport code systems: IATA (International Air Transport Association) and ICAO (International Civil Aviation Organization). IATA-codes specify the airport as a part of a commercial flight route. However, the IATA system, is not solely limited to airports. Other locations, such as bus or ferry stations can also apply for an IATA location code, as long it is included in an airline travel chain. The ICAO-code, on the other hand, indicates that the location is an airport, but not necessarily for commercial flights In order to obtain a selection of airports that met our criteria, an airport was included only if it had both an IATA-code and an ICAO-code.  Three different sources are used: 1) Swedavia (lists all airports in the Nordics that Swedavia traffics today). This is our main source, but it does not include all existing airports in the Nordic countries. Therefore, we also use two other sources: 2) Avcodes: Airport code database, from which other airports, that are not served by Swedavia, are obtained. 3) Wikipedia. Finally, the listed airports are checked against Wikipedia, to verify if any airports have been missed through the other sources. This selection gave us 186 airports in total.

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 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 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.