Maps

This map shows the result of Nordregio’s Regional Potential Index in 2024 (data from 2022). Nordregio’s Regional Potential Index (RPI) enables cross-regional comparison of development potential and illustrates the regional balance between the Nordic countries and has been part of the State of the Nordic Region report since 2018. The purpose of this multidimensional index is to summarise the current and past performance of the Nordic regions across major policy domains. The index helps to identify regions that have high potential and those in need of further support to boost their potential and meet existing challenges. It provides policy-makers with a comparative learning tool that informs the design of effective regional development strategies at Nordic level. Nordregio’s RPI is a multi-item measurement scale that incorporates information about the demographics, labour market and economic output of the Nordic countries’ 66 administrative regions. It consists of eight indicators classified into four main groups and eight subgroups. These components and indicators were originally selected on the basis of their relevance for regional development. The 2024 RPI is based on a new refined method that maintains a similar set of indicators but applies a more robust statistical process to the construction of the RPI. In brief, the new methodology consists of a pre-processing stage, in which the input data is prepared for analysis, and a processing stage, in which the indicators are weighted and aggregated. More information about the method can be found in the State of the Nordic Region 2024 report. The RPI was calculated retroactively for the 2015–2023 period. However, the focus in this section is on 2022 – the most recent year in our time series with full data coverage. The map shows the redesigned RPI for that period. In line with the principles of accumulation and agglomeration that drive the…

This maps shows the gender distribution in employment at both municipal (big map) and regional levels (small map) in the Nordic Region in 2021 (measured in November). Blue shades indicate more men employed and red shades more women. Most regions had higher employment rates for men than women, with an average of 3.7% more men across the countries and sectors. In Finland, the rates were more balanced, with only 1.3% more men in employment. In three regions in Finland, employment rates were slightly higher for women: Etelä-Karjala – Södra Karelen, with 0.8% more women than men; Kainuu – Kajanaland, with 0.4%; and Uusimaa – Nyland with 0.1%. These are the only regions in the Nordic countries with a prevalence of employed women at the regional level. For the rest, employment rates were higher for men, with Icelandic regions having the largest share: Suðurnes had 11.3% more men than women, Vesturland 8.8%, Austurland 8.7%, and Suðurland 8.2%. The average for the Icelandic regions was 7.5%. For Denmark, this was 4.9%, for Sweden, 3.9%, and for Norway, 3.4%. At the municipal level, however, the situation was much more varied. Åland is one of the most extreme examples. Although the average was 2.5% more men employed than women, Åland has several municipalities with extremes at both ends. On the one hand, there are municipalities like Lumparland, with 16% more women than men, and Brändö, with 11.3%. On the other hand, Kökar had 64% more men than women. The variations between Ålandic municipalities can largely be attributed to the municipalities’ population size. The larger cities, like Trøndelag in Norway, may be more balanced at the regional level, but within the region, there are municipalities with a 20% prevalence of women in employment (Namsskogan, Meråker, Holtålen), as well as some with 18% (or higher) more…

This map shows the spatial distribution of Nordic electricity production per capita, by volume and source type in the Nordic Region in 2021. The data is presented at a regional level, except for Iceland (national level) and Denmark (bidding zones). The circles represent electricity production in GWh, while the green shades indicate electricity production per capita (kWh). Finally, the colour of the circles denotes the source of electricity. The Nordic Region overall has a high electricity production per capita; in fact, Iceland and Norway have the world’s highest electricity production per capita. The electricity mix in 2021 was 96% fossil-free – 73% from renewables (mainly hydropower) and 17% from nuclear power. In 2000 85% of the electricity production was fossile-free. Still there are clear spatial differences in the electricity production. Firstly, we see the high amount of electricity being produced for the five nuclear facilities in Sweden and Finland. Secondly, a substantial volume of hydro-electricity is produced in southern Norway, throughout Iceland, Northern Sweden and Northern Finland. As a result, over half of Nordic electricity is produced from hydropower. Wind power is the source of electricity that has been growing the most during the last two decades, from 1.2% in 2000 to 14% in 2021. The regions with the highest electricity production per capita are in Iceland, Northern Sweden, and Northern and Western Norway. Both Finland and Denmark are net importers of electricity, but both countries have rapidly transitioned away from fossil fuels. Cheap and fossil-free electricity is a prerequisite for the green transition and with growing industries within e.g. battery production, green steel and mining, the need for fossil-free electricity is expected to increase in the coming decades.

This map shows the tonnes of greenhouse gas emissions per person employed in Nordic municipalities (big map) and regions (small map) in 2022. The data for Iceland is presented at the national level, while no data was available for the Faroe Islands and Greenland. The map is based on data on emissions per sector and country from Eurostat and detailed employment by sector data from the Nordic statistical offices. By calculating the average emissions per person employed and per sector we could use municipal employment by sector data to assess the average emissions per person employed in each municipality. The results are an estimation based on the assumption that all jobs in the same sector have the same GHG emissions. In 2022, greenhouse gas (GHG) emissions per person employed in the Nordic Region were 15.7 tonnes. This is higher than the EU average of 13.5 tonnes. There are also fairly big differences between the Nordic countries, with higher emissions per person employed in Iceland (28.6), Denmark (23.1) and Norway (20.5) and lower emissions in Finland (15.7) and Sweden (8). On the other hand, the emissions per person employed have decreased faster in the Nordic Region than for the EU as a whole. In the last decade, emissions per person employed fell by 24% in the Nordic Region compared to the EU average of 22%. The biggest decrease (32%) was in Finland. The sectors with the highest emissions per worker vary slightly between the countries. In Sweden and Norway, the sector with by far the highest emissions per worker was the manufacture of petroleum coke and refined petroleum products. However, it should be noted that the number of workers in this sector is small. In Denmark, the highest emissions by person employed could be found in water transport; in Finland, in…

This map shows the share of people aged 25-64 years with a tertiary education in Nordic municipalities (big map) and regions (small map) in 2022. All of the Nordic countries have a higher share of people who completed tertiary education than the EU average. The highest share is in Sweden (48.6%), followed by Norway (47.8%), Iceland (42.9%), Finland (42.7%) and Denmark (42.1%). There are, however, big differences within the countries with a considerable urban-rural divide in terms of tertiary education. The capital regions of Oslo (66%), Stockholm (58%), Copenhagen (Hovestaden) (51%), Reykjavik (51%) and Helsinki (Uusimaa) (49%) stand out with particular high share of tertiary educated population. Conversely, rural regions such as Iceland outside of Reykjavik (30%), Kymenlaakso (34%), Kainuu (34%), Etelä-Savo (35%), Satakunta (35%) in Finland and Syddanmark (35%), Midtjylland (36%) and Nordjylland (36%) in Denmark are among the regions with the lowest share of tertiary educated population. The successful implementation of the Bologna Process and the derived increase in educational levels of young people in the Nordic Region coincides with large numbers of the ‘baby boomer’ generation leaving the labour market. This generation has a significantly lower level of education than the current 24–35 age group – i.e. those who are now entering the labour market. This trend can be seen across the Nordic Region.

This map displays the share of high-skilled workers as a share of the total number of workers in Nordic municipalities (big map) and regions (small map). “High-skilled workers” is here defined as group 1-3 (Managers, Professionals and Techinicians/associated professionals) of the International Standard Classification of Occupations (ISCO). For Iceland national data is used. The EU average of high-skilled workers is 43%, and the Nordic countries are at the top of the rankings – 49.5% in Finland, 51.1% in Denmark, 54.2% in Norway, 54.5% in Iceland and 58.9% in Sweden. On a regional level, the highest share is in the capitals and bigger cities, such as Stockholm (72%), Oslo (71%), Hovedstaden (Copenhagen) (60%), Uppsala (60%) and Uusima (Helsinki) (59%). The lowest shares are in the Finnish regions of EteläPohjanmaa, Keski-Pohjanmaa, Satakunta and Etelä-Savo (less than 40%). However, this does not necessarily mean that employers will have a greater chance of successfully recruiting high-skilled workers in the future, partly because those in this group already have jobs and partly due to generally lower investments in education.

This map shows the ratio between the age groups 20–29 and 55–64 at the municipal level (big map) and regional level (small map). A ratio below 1 indicates that fewer individuals are entering the labor market than leaving it, while a ratio above 1 means more people are entering than exiting. For the Nordic Region as a whole, the ratio is 0.95, meaning that there are slightly fewer people in the age group 20–29 than 55–64. Iceland is the only country with a ratio above 1 (Iceland: 1.3; Greenland: 0.99; Denmark: 0.97; Norway: 0.95; Finland: 0.94; Sweden: 0.93; Faroe Islands: 0.88; Åland: 0.63). All of the Icelandic regions, as well as the capital regions of Norway, Denmark and Finland, have a ratio above 1. In Sweden, the highest ratios are in Uppsala (1.25), Västerbotten (1.14) and Östergötland (1.03), while the ratio in Stockholm is below 1 (0.95). The lowest ratios are found in Etelä-Savo (0.62) and Åland (0.63) in Finland, Sjælland (0.63) in Denmark, Västernorrland (0.74) in Sweden, and in Vestfold og Telemark (0.78) and Viken (0.77) in Norway. However alarming these trends and developments are, they are neither new nor undescribed. An analysis of the factors and policy strategies that are influencing these developments enables adjustments to be made to future trends.

This map illustrates the projected change in the working-age population across Nordic municipalities (large map) and regions (small map) from 2023 to 2040. The working-age population is defined here as individuals aged 20 to 64. The blue areas on the maps represent municipalities and regions where the working-age population is expected to increase during this period. In contrast, the red areas indicate a projected decline in the working-age population. These projections are based on data from Nordic statistical institutes, though it’s important to note that the underlying assumptions may vary between Nordic countries. In most of the Western world, the working-age population is decreasing. In the EU, this age group is expected to decrease by 6.5% between 2023 and 2040. Only five EU countries – Malta, Luxembourg, Ireland, Sweden and Belgium – are expected to enjoy growth in the working-age population during this period. However, in the Nordic Region as a whole, the working-age population is expected to grow slightly, with an average increase of 1.9%. As the map shows, the distribution is quite varied, with considerable differences both between and within the countries. The biggest increase is expected in Iceland (28%), followed by Sweden (5.8%), Åland (3.9%) and Norway (0.6%). Decreases are expected in Finland (-0.5%), the Faroe Islands (-2.6%), Denmark (-3.2%) and Greenland (-11.4%). This development is in addition to the decreases already experienced by Finland since 2013.  In general, the trend of growing populations in cities and decreasing populations in rural areas is expected to continue. The regions that are expected to have the highest working-age population growth include Höfuðborgarsvæðið in Iceland, Uppsala (+13%), Stockholm (+12%), Skåne (+9%), Halland (+7%) and Västra Götaland (+6%) in Sweden, Uusimaa (+8%) and Pirkanmaa (+5%) in Finland, and Oslo and Viken in Norway (+5%). In addition, Copenhagen municipality (+5%), Rødovre (+9%)…

This map shows the employment rate for the age group 55-74 years in the Nordic municipalities (big map) and regions (small map) in November 2022. Employment rate refers to the share of population in a particular age group that is in employment (LFS definition), in this map it is measured for the age group 55-74 years. Older adults (55+) were less affected by the pandemic. In general, this age group has a stronger attachment to the labour market, and a higher proportion work in occupations where remote work was possible. For quite some time, the Nordic Region has boasted a higher employment rate among older adult workers compared to the rest of Europe, although this gap has narrowed somewhat in recent years. In 2022, 75.5% of the Nordic population aged 55–64 were employed, whereas the EU average stood at 62.3%, representing a difference of 13 percentage points. In 2012, however, this difference was 21 percentage points, and in 2002, it was 29. The map shows variation between the Nordic countries, with Finland exhibiting a lower employment rate for this age group. The highest rates were observed in the Faroe Islands (62%), Greenland (61%), Åland (56%), and the Swedish regions of Jämtland-Härjedalen (55%) and Jönköping (54%). The Labour Force Survey (LFS) is the official source for labour market statistics and the only source that is comparable across countries. The data comes from a standardised survey that is conducted in the same way in all EU countries, as well as in many non-EU countries, including in Norway, Iceland and the Faroe Islands. The LFS is the main source for the map, however since it is not possible to break down at a municipal level, register data has been made to create estimates at a municipal level.

These maps shows the employment rate in 2022 for those born in a EU country (top left) and those born outside of the EU (bottom left), as well as the change in employment rate between 2020 and 2022 for those born in the EU (upper right) and outside the EU (lower right). The data is displayed at NUTS 2 level and comes from the labour force survey (LFS). The category ‘foreign-born’ is quite heterogeneous and consists of everything from labour migrants to refugees – two groups who face quite different conditions and have different connections to the labour market. The employment rate for people born in another EU country – a group that includes a large proportion of labour migrants – has been on par with the employment rate for native-born people for a long time. As can be seen in the top-left figure in the map, in 2022 all NUTS2 regions except Southern Denmark had an employment rate of 75% or more for this group. The highest employment rate was observed in the Swedish NUTS2 regions of Middle Norrland, Stockholm and Western Sweden, followed by Oslo in Norway and Iceland. The employment rate for people born outside of the EU (a group that largely consists of refugees) has been lower for a long time than that of native-born people and those born in the EU. While the employment rate for people born in non-EU countries is still lower than for natives (a 15 percentage point difference (pp) in Sweden, 11 pp in Norway, 7 pp in Denmark and Finland, and 2 pp in Iceland), this gap has been closing in the last couple of years since the pandemic. Between 2020 and 2022, the employment rate for those born outside of the EU rose almost eight percentage points in Denmark…

This map shows the employment rate for Nordic municipalities (big map) and regions (small map) in 2022 (measured in November). Employment rate refers to the share of population in a particular age group that is in employment (LFS definition), in this map it is measured for the age group 20-64 years. At a national level, all of the Nordic countries have employment rates higher than the EU average (74.6%). The highest rates were in the Faroe Islands (93%), Åland (86%) and Iceland (83%), followed by Sweden (82.2%), Denmark (80.1%), Norway (80.9%), Finland (78.4%) and Greenland (75%). Of the EU countries, only the Netherlands (82.9%) had a higher employment rate than Sweden in 2022. As the map shows, most of the regions and municipalities had employment rates above 75% (the green areas on the map) in 2022, meaning that three-quarters of people aged 20–64 were working. In addition to the Faroe Islands, Iceland and Åland, the Swedish regions of Jämtland-Härjedalen, Halland and Jönköping also had employment rates of more than 85%. The only regions with employment rates under 75% were the Finnish regions of North and Southern Karelia and KeskiSuomi. On a municipal level, the highest employment rates were found in rural municipalities, mainly in Iceland and the Faroe Islands, as well as some municipalities in Österbotten in Finland (such as Pedersöe and Korsholm). Of the intermediate municipalities (between urban and rural), the highest employment rates were in the Swedish municipalities Habo (Jönköping), Hammarö (Värmland), Nykvarn (Stockholm), Gällivare (Norrbotten), Kungsbacka (Halland), Svedala (Skåne) and Mosfellsbær (Höfuðborgarsvæðið) in Iceland. Of the urban municipalities, the employment rate was highest in Garðabær and Kópavogur in the Capital Region of Iceland, Tyresö and Täby in the Stockholm region, Partille in Västra Götaland in Sweden, and Bærum and Rælingen in Viken in Norway. The lowest employment…

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.