Maps

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

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.

The travel times indicate the fastest morning connection outbound from Copenhagen Central Station or Malmö Central Station, departing after 6:30AMand arriving before 9:00AM. The station catchments are calculated by bicycle travel time for any time remaining beyond train travel. For instance, a 35-minute train ride and a 10-minute cycle ride results in a 45-minute total travel time. The shades of green indicate the travel time to other train stations and their surrounding areas in four main classes: up to 15 minutes, 16 to 30 minutes, 31 to 45 minutes and 46 to 60 minutes. The areas not highlighted in green on the map are further than one hour by train from either Copenhagen or Malmö main train stations. The map clearly shows that the vast majority of areas within the Capital Region of Denmark, a number of stations and areas which are part of the region of Zealand, for instance Slagelse and Næstved, as well as areas located along four main train corridors in Skåne (Malmö-Helsingborg, Malmö-Hässleholm, Malmö-Trelleborg and Malmö-Ystad) are within the one-hour travel time by train from/to Copenhagen and/or Malmö, thanks to the different train types (Öresund trains, regional trains and intercity trains). Areas of the GCR which are beyond the one-hour travel condition are the most northern part of the Capital Region of Denmark, the southern and western parts of Zealand (e.g. Kalundborg and Vordingborg) as well as most of the eastern half part of Skåne. In terms of population, the current situation provides this possibility to almost 3 million out of 4.3 million inhabitants, corresponding to 69% of the total population living in the Greater Copenhagen Region in 2020. The proportion of the total population increases to 75% when the region of Halland is excluded (as this was not initially part of the GCR when the…

This map shows the Greater Copenhagen Region, which constists of 85 municipalities and four regional authorities: the Capital Region of Denmark and Region Zealand on the Danish side, and the regions of Skåne and Halland on the Swedish side.

These maps show the number of jobs in the bioeconomy sectors as a share of the total employment in 2017 (left) and 2009-2017 change (right), excluding the NACE A sectors. The non-NACE-A sectors have seen a more moderate decrease in Finland but a noticeable increase in most parts of Sweden, Denmark and the Faroe Islands and a remarkable increase in Iceland with more than 15% (Figure 9.3). The average increase in the Nordic Region is 4.3% in the non-NACE-A sectors. There are also notable differences within the countries, showing that regional differences matter. In Denmark, Greenland and the Faroe Islands, there is a positive trend in most regions, but in Finland, almost all regions are losing jobs in the primary industries. In Norway and Sweden, some regions show positive development while others are in decline. Read the digital publication here.

These map show the number of jobs in the agriculture, forestry and fisheries sectors (NACE A sectors) as a share of the total employment in 2017 (left) and 2009-2017 change (right). The map (left) indicates ranges of people employed in agriculture, forestry and fisheries sectors between below 2% to above 8%, with the highest proportion being in Greenland with 23.5% in 2017. In the EU-28, the overall proportion of the total workforce with jobs in the NACE-A sectors is an average of 4.6%. Overall, the NACE-A sectors show a sharp decline in employment (right map), particularly in Finland, Iceland and Norway (except northern Norway), where the decline was up to 20% between 2009 and 2017. Read the digital publication here.  

These maps show the number of jobs in the bioeconomy sectors as a share of the total employment in 2017 (left) and 2009-2017 change (right). The share of jobs in the bioeconomy varies from below 15% to above 22.5%. For instance, in the western regions of Denmark, the central and southern regions in Finland, Greenland and some mid and northern regions of Norway, over 22.5% of the working population is employed in the bioeconomy. At the same time, most regions in mid-Norway, in all Finnish and some Danish regions witnessed negative development in jobs in the bioeconomy. In Greenland, Iceland, Faroe Islands, Sweden, Northern Norway and other parts of Denmark the situation is very different and the number of jobs in the bioeconomy has increased. These proportions should also be seen in relation to the increase in total number of jobs17 in most regions in the Nordic countries except for the Finnish regions and Åland where there is a decline in most regions. Read the digital publication here.

The map provides an overview of the population change in Arctic settlements with 500 inhabitants or more during the period 2000 to 2017. The purple underlaying layer shows the extent of permafrost across the Arctic. The circles indicate settlements with 500 inhabitants or more and are proportional to the total population in 2017. Blue tones indicate population growth between 2000 and 2017, while red tones indicate population decline. Four zoomed-in maps show areas with high settlement density – Arctic Fennoscandia, Iceland, the Faroe Islands, and Alaska. In Alaska, population increased in the largest settlements between 2000 and 2017. The population decline in small settlements located far from the two large cities – Anchorage and Fairbanks – has been caused by outmigration, which has cancelled out the positive natural population growth. In the Canadian Arctic, most people live in a few settlements. Similar to Alaska, the population increased in the largest settlements and decreased in small settlements. Most of the smaller settlements in Arctic Fennoscandia have witnessed a population decline between 2000 and 2017, except in Norway. The dominant pattern in Fennoscandia is a population growth in larger settlements and a population decline in surrounding smaller settlements. This is similar to the pattern observed in the other Nordic Arctic countries – Iceland, Greenland, and the Faroe Islands. New inhabitants settled in the capitals (Reykjavik, Nuuk, and Torshavn) and regional centres, from both domestic and international locations, while settlements in sparsely populated areas are becoming less attractive to incomers. In the Russian Arctic, the regions can be divided into the oil and gas areas of the Khanty-Mansi and Yamal-Nenets, and other areas. The population is growing in the oil and gas areas and declining slowly in the others. Over 75% of the settlements have been shrinking throughout the 21st century, mainly because…

The map shows the indigenous population as a share of the total population in the Arctic. Approximately one million people, or 9% of the total population in the Arctic is indigenous. Indigenous population reassembles more than 40 different ethnic groups. In terms of statistics there are no circumpolar definition of an indigenous person and therefore the number of indigenous people is based on different national definitions. Official statistics do not necessarily recognize indigenous populations separately. The indigenous population is the highest in the Canadian Arctic and in Greenland, weighing more than 75% of the total population. The Yukon, the southern part of Northern Quebec, and Labrador and Newfoundland (Canada) have the lowest shares of indigenous population. In the remaining part of the Arctic, indigenous peoples represent less than half of the total population, except in Sakha (Russian Federation), Southwest Region and Northern Region (Alaska, USA) where 50-75% of the population is indigenous. There are no indigenous people in Iceland, the Faroe Islands and Svalbard.

This map shows the country of origin of Foreign Direct Investment (FDI) in the Nordic regions between 2003 and 2016. The colours indicate different countries of origin of FDI inflow by number of projects between 2003 and 2016. FDI inflows examined by country of origin, reveal an interesting pattern in terms of intra-Nordic investment inflows, confirming the assumption that proximity, both in terms of geographical distance, and in terms of rules, regulation and business culture is an important driver of FDI. A breakdown at the regional level reveals that 55 out of the 74 Nordic regions received the largest number of FDI projects from a region located in another Nordic country. The largest share of these intra-Nordic flows originates from Sweden (35 regions in total), particularly in the manufacturing sector as well as the ICT sector in Norway. The largest share of FDI projects from Finland are attracted to Sweden’s highly competitive international manufacturing industry. Denmark is the main source country of FDI inflows in both Greenland (mostly in the transportation and storage and business services sectors) and the Faroe Islands (mostly manufacturing and finance and insurance activities sectors). Eighteen Nordic regions have their largest source country in terms of project located outside the Nordic Region, i.e. other European and extra-European countries. FDI inflows from other European countries are the highest in terms of projects in six Nordic regions, most of these regions can be characterised by their relative remoteness and strong industrial profile. Finally, two extra-European countries, namely the United States and Canada, are the largest source country in twelve Nordic regions, that are either capital city regions with a strong and diversified service sector or peripheral industrial regions.

Co-operation Committees funded by the Nordic Council of Ministers in 2018 The Nordic countries have a long history of cross-border co-operation. In parts, this co-operation has been carried out through regional cross-border committees which cooperate across the state borders. The activities of some of these committees have been going on since the 1960’s. The aim of the committees are increasing growth and development of the actual border region, building on the concept that bigger, dynamic and well-integrated regions will be able to retain and attract companies and people for further growth and development. The cross-border regions are geographically delimited by their member institutions, which consist of government organisations on either regional or municipal level in neighbouring countries. These can be municipalities, counties, local authority associations or other organisations that deal with regional development. In some cases, the same regional body might be involved in several cross-border committees. Such areas are hatched in the map.

The map shows the change in jobs (growth or decline) at municipal level over a period that can be attributed to local factors (e.g. local policies or local natural or institutional conditions) and is thus not due to national or industry trends. The map is based on shift-share analyses which can help answer why employment is growing or declining in a local or regional industry. This type of analysis can answer questions like: How much of local job growth can be attributed to expected national and sectoral trends and how much is due to local factors Is the local economy growing or declining? How does the community compare with other communities? What are the local strengths and weaknesses in providing jobs and in which sectors? What industries are doing well? What industries should be targeted? There are large differences between municipalities in terms of the change in employment due to local factors. For example some of the municipalities in Mid Jutland in Denmark show a high local employment effect while others in the North of Jutland do not. These differences can be due to access to markets, to industrial culture, to municipal or regional facilitation etc. In-depth research is needed in order to understand the reasons. The map does not show the total change in jobs.

This map shows the employment recovery from the financial crisis, with the employment rate (20-64 years) in European regions in 2016 related to the EU2020 goal of 75%, as well as the change in the employment rate between 2009 and 2016. The green colour indicates regions with employment rates above 75% and an increase in the employment rate between 2009 and 2016. The red colour indicates regions with employment rates below 75% and a decrease in the employment rate between 2009 and 2016. The yellow colour indicates regions with employment rates above 75% and a decrease in the employment rate between 2009 and 2016. The blue colour indicates regions with employment rates below 75% and a decrease in the employment rate between 2009 and 2016. The grey colour indicates regions with no data available. On a European scale, the effect of the financial crisis on employment became noticeable from 2009 onwards when average employment rates started to decline. This continued until 2013 when the average European employment rate reached its lowest level of 68.3% for the age group 20–64 years. After 2013 the employment rate started to rise again but it took until 2016 for the average European employment rate to reach and then surpass pre-crisis levels. In 2016 the average employment rate in the European union was 71%, edging closer to the EU2020 goal of 75%. In some regions, primarily in southern Europe, employment rates have still to recover to pre-crisis levels. This is particularly so for Greece, Spain, Italy and Portugal which were particularly hard hit by the debt crisis and thus had to undertake massive cuts across the public sector. On the other hand, some countries such as Germany, Austria and Switzerland saw rising employment rates even during the financial crisis. The differential nature of outcomes in…

This map shows the total value of Foreign Direct Investment (FDI) in million euros, and the intensity of the FDI in the Nordic regions between 2003 and 2016. The FDI intensity is an index of the value of FDI inflows divided by the gross regional product. The FDI intensity index allows comparing the performance of the Nordic regions in attracting FDI inflows by taking the size of the regional economy into account. A high intensity result reflects a more resilient regional economy, due to a diversity of investors. The brown shading indicates different FDI intensities in the regions between 2003 and 2016. The lighter the shade, the lower the FDI intensity. The grey colour indicates regions with no data. The circles indicate the total value of FDI in million euros (2016 value) in the regions between 2003 and 2016. The larger the circle, the greater the value. The colour in the circles indicate changes in the FDI total value between 2003 and 2016. The pink colour indicates regions with a decrease in the FDI value, the yellow colour indicates stable FDI values, and the blue colour indicates regions with an increase in the FDI value. Large capital city regions tend to be the main beneficiaries of FDI inflows and this pattern is also observed in the Nordic Region. Stockholm region emerges as the clear leader with a total deal value of EUR 110,000m. The Danish capital region attracted about half of Stockholm Region’s deal value – EUR 53,804m followed by Helsinki (EUR 45,000m), Skåne (EUR 30,000m), and Oslo (EUR 29,100m). The total deal value for Reykjavík is rather modest – at around EUR 2,000m, and is comparable to regions such as Dalarna and Jönköping (Sweden), Sør-Trøndelag (Norway) and Lappi (Finland). All Nordic regions have witnessed an increase in FDI inflows…

This map shows the life expectancy at birth in the regions of Europe in 2015 and the change between 2005 and 2015. The green shading indicates the life expectancy at birth in years in the regions of Europe in 2015. The darker the green the longer the life expectancy at birth. The grey colour indicates regions with no data. The blue circles represent the changes in the life expectancy at birth between 2005 and 2015. The larger the circle, the greater the increase in the life expectancy at birth between 2005 and 2015. As one of the UN Sustainable Development indicators for good health and well-being, life expectancy at birth measures the general health status of a population. Life expectancy at the national level is highly correlated to national income indicators such as GDP per capita. Most of Europe has however reached a level where further increases in wealth no longer increase average life expectancy. The longest life expectancies are found in parts of Spain, France, Italy, Switzerland and Norway. In 2015 there is still a visible East-West divide, where countries in Eastern Europe, in the south-east of the Baltic Sea Region and Northwest Russia have a significantly lower life expectancy. The size of the blue bubbles indicate however that these regions have witnessed the largest increases over the last ten years. If their economies continue to grow it is likely that their life expectancy will continue to increase for some years to come. Regional differences are partly due to individual characteristics, but also to the local environment as well as the politics and institutions that influence the local economy, access to local services and care, general educational levels and the local norms that influence lifestyles.

These maps provide a comparative overview of the eco-innovation performance on the Eco-Innovation Index of the states of the European Union (EU) in 2010 (upper map) and in 2016 (lower map). Eco-innovation is currently at the core of EU policies as it is believed to be a key driver in the transition to a green economy, growth generation and new job creation in the years to come. The eco-innovation scoreboard is an index of eco-innovation performance in the EU-member states, based on 16 indicators in 5 thematic areas. It promotes a holistic view of economic, environmental and social performance and complement other measurement approaches in terms of assessing the innovativeness of the EU countries. The green tones indicate differences in eco-innovation performance in the EU-member states in 2010 and 2016. The darker the green, the more performant the state in eco-innovation. The grey colour indicates no data. The high scores of the three Nordic countries, along with Germany and Austria, can partly be explained by the fact that the Eco-Innovation Scoreboard is measured in relation to EU averages. The eco-innovation landscape has remained stable across the Nordic countries during the period 2010–2016, with no significant change in performance. Finland, Denmark and Sweden remain among the top-performing countries. In 2016, Finland scored highest in terms of the overall eco-innovation performance of all the Nordic countries, followed by Denmark and Sweden. In 2010, Denmark was the best Nordic performer on the eco-innovation index, closely followed by Finland and Sweden. Placing the Nordic countries in a European context, we see that other European countries are catching up but with different intensities. Lithuania and Latvia have significantly improved their positioning on the eco-innovation index in 2016 as have Slovakia, Greece and Portugal compared to their scores in 2010. In contrast, Austria, Belgium and the…

This map shows the value of greenfield investment as Foreign Direct Investment (FDI) inflow in million euros, and the greenfield investment as a share of the total FDI inflow in the Nordic regions between 2003 and 2016. Greenfield investments and mergers and acquisitions are fundamentally different forms of FDI, implying that their effect on host economies differs substantially. Consensus does however exist around the notion that the short-term effect should be greater when a foreign company’s penetration occurs through greenfield investment. The brown shading indicates different shares of greenfield investment related to the total FDI inflows in the regions between 2003 and 2016. The lighter the shade, the lower the share of the greenfield. The grey colour indicates regions with no FDI inflow data. The circles indicate the greenfield investment in million euros (2015 value) in the regions between 2003 and 2016. The larger the circle, the greater the value. The colour in the circles indicate changes in the greenfield investment between 2003 and 2016. The blue colour indicates an increase in deal value in the second period (>60% of the inflows of 2003–2016 took place in 2010–2016), yellow indicates a stable value in inflows between the two periods (40–60% of the inflows of 2003– 2016 took place in 2010–2016), while red indicates a decrease over time between the two periods (<40% of the inflows of 2003–2016 took place in 2010–2016). Examined by regions with the largest share of greenfield investment during the period 2003–2016, the administrative regions of Helsinki (EUR 9,402m), Stockholm (EUR 7,896m) and Copenhagen (EUR 6,601m) emerge as Nordic leaders in attracting this form of foreign investment. Due to their innovation-based economies and booming start-up scenes, both Västra Götaland and Skåne (Sweden) have also performed exceptionally well in attracting greenfield investment securing deal value totals of EUR…

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…