Maps

This map shows the percentage change in household disposable income between 2018 and 2022 in Nordic municipalities (big map) and regions (small map). Household disposable income per capita is a common indicator of the affluence of households and, therefore, of the material quality of life. It reflects the income generated by production, measured as GDP that remains in the regions and is financially available to households, excluding those parts of GDP retained by corporations and government. In sum, household disposable income is what households have available for spending and saving after taxes and transfers. It is ‘equivalised’ – adjusted for household size and composition – to enable comparison across all households. Purchasing Power Standards (PPS) is used to compare the countries’ economies and the cost of living for households. As shown in the map, between 2018 and 2022, household disposable income increased for all Danish, Icelandic, and Norwegian municipalities and decreased for Finnish and Swedish municipalities. On average, the city municipalities have higher incomes and increased most in Finland and Sweden in 2018–2022. In Sweden, a tendency towards larger falls in income was observed in several southern municipalities. In summary, absolute household income increased in all Nordic countries but not when measured in purchasing power. Based on this metric, on average, Norwegian households are the most well-off and Iceland the worst off, while Danish households benefited from a stronger currency in 2022. Single-parent households have had lower increases in household income than other families in Norway and parts of Sweden. Municipalities show a similar trend in Norway and Denmark, although Norwegian coastal municipalities fared slightly better in 2022. Disposable income is falling in all Swedish and Finnish municipalities.

The data excludes emissions from land use, land use change or forestry (LULUCF). The regional data has been adjusted to UNFCCC national data. The data for Denmark, Iceland and Greenland is on national level. It should be noted that displaying emissions on a territorial basis may be skewed due to the inter-regional dynamics of energy processes, natural resource distributions and concentrations of industrial activities. From 2017 to 2021, the Nordic regions cut their per-capita GHG emissions by on average 11.3%, with an overall Nordic average fall of 8.7% over the same period. In regions historically reliant on fossil fuels for heat and power generation, emissions have continued to decline. This trend is evident in Denmark, as well as in Southern Sweden and Southern Finland – densely populated areas that have taken steps toward expanding district heating coverage and reducing carbon intensity. The largest decrease in GHG emissions per capita was found in Troms and Finnmark, with a 42.3% decrease, Satakunta with a 30.2% decrease and Päijät-Häme – Päijänne-Tavastland with a 29.2% decrease. Only three regions (Greenland, Trøndelag and Blekinge) saw an increase in GHG emissions per capita. At an aggregated level, industrial-related emissions decreased throughout the Nordic Region, but this trend does not hold true for regions in Norway with intensive offshore oil and gas operations. For instance, Nordland, Vestland, Møre og Romsdal, Vestfold and Telemark exhibited the highest per capita emissions in 2021. Between 2017 and 2021, emissions were increasing in many Norwegian regions with intensive offshore oil and gas activity, but also in Norrbotten in Sweden (21.2 tonnes of CO2 equivalent per capita) and Gotland (33.6 tonnes of CO2 equivalent per capita) due to intensive activity in the metal and cement industries, respectively, as well as in several Finnish regions. At the other end of the scale, the…

This map shows the percentage change in the Gini coefficient between 2018 and 2022. The big map shows the change on municipal level and the big map at regional level. Blue shades indicate a decrease in income inequality, while red areas indicate an increase in income inequality The Gini coefficient index is one of the most widely used inequality measures. The index ranges from 0–1, where 0 indicates a society where everyone receives the same income, and 1 is the highest level of inequality, where one individual or group possesses all the resources in the society, and the rest of the population has nothing. The map illustrates significant variations in the change in income inequality across Nordic municipalities and regions. Between 2018 and 2022, income inequality increased in predominantly rural municipalities, notably in Jämtland, Gävleborg, Dalarna and Västerbotten in Sweden, as well as Telemark in Norway. For Denmark, the rise in inequality is mainly for the municipalities in Western Jutland. At the same time, approximately one third of municipalities in the Nordic Region experienced a decrease in income inequality during the same period, primarily in Finland and Åland. For example, in Finland, the distribution of inequality was more varied. This trend aligns with the ongoing narrowing of the household income gap observed in many Finnish municipalities since 2011, which is mainly attributed to the economic downturn of the early 2010s, as well as demographic shifts such as outmigration and ageing.

The map shows the type (positive or negative) and size of population change from 2010 to 2019 in all nordic municipalities. In Finland, Denmark, and Greenland there is a clear pattern of population growth in and around the larger cities and population decline in rural areas. Geographical and administrative differences mean that a much larger number of rural municipalities in Finland are dealing with population decline. Sweden experienced substantial population growth between 2010 and 2019, primarily due to high levels of international immigration. As a result, many rural areas also experienced population growth, particularly in the south of Sweden. However, in the more sparsely populated municipalities in the north of Sweden, the pattern is somewhat similar to that observed in Denmark and Finland, albeit with population decline in lower absolute numbers. Both Iceland and the Faroe Islands experienced substantial growth of their tourism industries within the period. This enabled some rural areas to maintain or even grow their populations. Norway exhibits more balanced population development in general, with a mix of population growth and decline in rural areas throughout the country.

The map shows the relative change in the number of overnight stays at the regional level between 2019 and 2020 for domestic visitors. This map is related to the same map showing change in overnight stays for foreign visitors 2019–2020. The sharpest fall in visitors from abroad was in destinations where foreign tourists usually make up a high proportion of the total visitors. This is particularly relevant to islands like Åland (89% decrease on foreign visitors, from early 2019 to mid-2020) and to Iceland (66-77% drop depending on region). Lofoten and Nordland County in Norway, as well as Western Norway with Møre and Romsdal, which also have a high proportion of international tourists during the summer season due to their scenic landscape, also recorded sharp falls of 77-79% on foreign visitors during the same period. In Finland, the lake district (South Savo) and Southern Karelia, as well as the coastal Central Ostrobothnia (major cities Vasa and Karleby), recorded a 75-77% drop in the number of visitors from abroad. The fall here was mainly due to the lack of tourists from Russia. Even Finnish Lapland suffered a major fall in international visits during the winter peak period. For many local businesses that rely heavily on winter holidaymakers, the 2021/22 winter was a make-or-break season. In Sweden, the regions of Kalmar, Västra Götaland, Värmland and Örebro lost 77–79% of visitors from abroad, probably due to much fewer visitors from neighbouring Norway and from Denmark. In Denmark, the number of overnight stays by visitors from abroad to the Capital Region was down by 73%, whereas the number of domestic visitors declined by 27%. No region lost as many overnight visitors, both from abroad and domestic, as the capital cities and larger urban areas in the Nordic countries. Copenhagen, Oslo, Stockholm, Helsinki and Reykjavik…

The map shows the relative change in the number of overnight stays at the regional level between 2019 and 2020 for foreign visitors. This map is related to the same map showing change in overnight stays for domestic visitors 2019–2020. The sharpest fall in visitors from abroad was in destinations where foreign tourists usually make up a high proportion of the total visitors. This is particularly relevant to islands like Åland (89% decrease on foreign visitors, from early 2019 to mid-2020) and to Iceland (66-77% drop depending on region). Lofoten and Nordland County in Norway, as well as Western Norway with Møre and Romsdal, which also have a high proportion of international tourists during the summer season due to their scenic landscape, also recorded sharp falls of 77-79% on foreign visitors during the same period. In Finland, the lake district (South Savo) and Southern Karelia, as well as the coastal Central Ostrobothnia (major cities Vasa and Karleby), recorded a 75-77% drop in the number of visitors from abroad. The fall here was mainly due to the lack of tourists from Russia. Even Finnish Lapland suffered a major fall in international visits during the winter peak period. For many local businesses that rely heavily on winter holidaymakers, the 2021/22 winter was a make-or-break season. In Sweden, the regions of Kalmar, Västra Götaland, Värmland and Örebro lost 77–79% of visitors from abroad, probably due to much fewer visitors from neighbouring Norway and from Denmark. In Denmark, the number of overnight stays by visitors from abroad to the Capital Region was down by 73%, whereas the number of domestic visitors declined by 27%. No region lost as many overnight visitors, both from abroad and domestic, as the capital cities and larger urban areas in the Nordic countries. Copenhagen, Oslo, Stockholm, Helsinki and Reykjavik…

The map shows the change in new registered passenger cars from 2019 to 2020. In most countries, the number of car registrations fell in 2020 compared to 2019. On a global scale, it is estimated that sales of motor vehicles fell by 14%. In the EU, passenger car registrations during the first three quarters of 2020 dropped by 28.8%. The recovery of consumption during Q4 2020 brought the total contraction for the year down to 23.7%, or 3 million fewer cars sold than in 2019. In the Nordic countries, consumer behaviour was consistent overall with the EU and the rest of the world. However, Iceland, Sweden, Finland, Åland, and Denmark recorded falls of 22%–11% – a far more severe decline than Norway, where the market only fell by 2.0%. The Faroe Islands was the only Nordic country to record more car registrations, up 15.8% in 2020 compared to 2019.  In Finland, Iceland, Norway, and Sweden, there were differences in car registrations in different parts of the country. In Sweden and Finland, the position was more or less the same in the whole of the country, with only a few municipalities sticking out. In Finland and Sweden, net increases in car registrations were concentrated in rural areas, while in major urban areas, such as Uusimaa-Nyland in Finland and Västra Götaland and Stockholm in Sweden, car sales fell between 10%–22%. Net increases in Norway were recorded in many municipalities throughout the whole country in 2020 compared to 2019.

The map shows the relative change in the number of concluded business bankruptcies by region, 2015–2019 average compared to 2020. At sub-national levels, the distribution of business bankruptcies does not show a clear territorial pattern. In Iceland and Denmark, businesses in the most urbanised areas, including the capital regions, seem to have been those that benefited most from the economic mitigation measures (-23.9% in Höfuðborgarsvæðið and -24.4% in Region Hovedstaden). By contrast, Oslo is the only Norwegian region where there were more business bankruptcies in 2020 compared to the 2015–2019 baseline (1.9% increase). Most Norwegian regions did, in fact, have fewer bankruptcies in 2020, particularly in the western regions. One plausible explanation for this could be that the number of business failures during the baseline period was especially high in western Norway due to the fall in oil prices in 2014–2015. In Sweden the situation is even more mixed. Here, businesses in urban areas seem to have been more exposed to the distress caused by the Covid-19 pandemic. The most urbanised regions in the Stockholm-Gothenburg-Malmö corridor registered a greater increase in liquidations (Jönköping, Kronoberg and Södermanland regions saw surges of around 20%). However, predominantly rural regions in Sweden, such as Västerbotten and Jämtland, also recorded higher numbers of bankruptcies than average (9.8% and 8.8% increase, respectively). In Finland, the impact was greater in Lapland (26.9%) and around Helsinki (Uusimaa, 25.9%) than in the central parts of the country. Åland also experienced a moderate rise in business bankruptcies in 2020 (4.0%), mostly related to the tourism sector.

The map shows a typology of European regions by combining information on pre-pandemic unemployment rates with unemployment rates in 2020, based on the annual Labour Force Survey (LFS) that is measured in November. On one axis, the typology considers the extent of the change in the unemployment rate between 2019 and 2020. On the other axis, it considers whether the unemployment rate in 2020 was above or below the EU average of 7.3%. Regions are divided into four types based on whether the unemployment rate decreased or increased and how it relates to the EU average. Regions falling into the first type, shown in red on the map, had an increase in the unemployment rate in 2020 as well as an above-average unemployment rate in general in 2020. These regions were most affected by the pandemic. They are mainly found in northern and central parts of Finland, southern and eastern Sweden, the capital area of Iceland, Latvia, Lithuania, Spain and central parts of France. Regions falling into the second type, shown in orange on the map, had an increase in the unemployment rate in 2020 but a below-average unemployment rate in general in 2020. These regions had low pre-pandemic unemployment rates and so were not as badly affected as the red regions, despite the rising unemployment rates. They are located in Denmark, Iceland, Norway, Åland, southern and western Finland, Sweden (Gotland, Jönköping, and Norrbotten), Estonia, Ireland, northern Portugal and central and eastern parts of Europe.

The map shows the population change by component 2010-2019. The map is related to the same map showing regional and municipal patterns in population change by component in 2020. Regions are divided into six classes of population change. Those in shades of blue or green are where the population has increased, and those in shades of red or yellow are where the population has declined. At the regional level (see small inset map), all in Denmark, all in the Faroes, most in southern Norway, southern Sweden, all but one in Iceland, all of Greenland, and a few around the capital in Helsinki had population increases in 2010-2019. Most regions in the north of Norway, Sweden, and Finland had population declines in 2010-2019. Many other regions in southern and eastern Finland also had population declines in 2010-2019, mainly because the country had more deaths than births, a trend that pre-dated the pandemic. In 2020, there were many more regions in red where populations were declining due to both natural decrease and net out-migration. At the municipal level, a more varied pattern emerges, with municipalities having quite different trends than the regions of which they form part. Many regions in western Denmark are declining because of negative natural change and outmigration. Many smaller municipalities in Norway and Sweden saw population decline from both negative natural increase and out-migration despite their regions increasing their populations. Many smaller municipalities in Finland outside the three big cities of Helsinki, Turku, and Tampere also saw population decline from both components. A similar pattern took place at the municipal level in 2020 of there being many more regions in red than in the previous decade.

The map shows the annual average internal net migration in 2010-2019. The map is related to the same map showing net internal migration in 2020. The maps show several interesting patterns, suggesting that there may be an increasing trend towards urban-to-rural countermigration in all the five Nordic countries because of the pandemic. In other words, there are several rural municipalities – both in sparsely populated areas and areas close to major cities – that have experienced considerable increases in internal net migration. In Finland, for instance, there are several municipalities in Lapland that attracted return migrants to a considerable degree in 2020 (e.g., Kolari, Salla, and Savukoski). Swedish municipalities with increasing internal net migration include municipalities in both remote rural regions (e.g., Åre) and municipalities in the vicinity of major cities (e.g., Trosa, Upplands-Bro, Lekeberg, and Österåker). In Iceland, there are several remote municipalities that have experienced a rapid transformation from a strong outflow to an inflow of internal migration (e.g., Ásahreppur, Tálknafjarðarhreppurand, and Fljótsdalshreppur). In Denmark and Norway, there are also several rural municipalities with increasing internal net migration (e.g., Christiansø in Denmark), even if the patterns are somewhat more restrained compared to the other Nordic countries. Interestingly, several municipalities in capital regions are experiencing a steep decrease in internal migration (e.g., Helsinki, Espoo, Copenhagen and Stockholm). At regional level, such decreases are noted in the capital regions of Copenhagen, Reykjavík and Stockholm. At the same time, the rural regions of Jämtland, Kalmar, Sjælland, Nordjylland, Norðurland vestra, Norðurland eystra and Kainuu recorded increases in internal net migration. While some of the evolving patterns of counterurbanisation were noted before 2020 for the 30–40 age group, these trends seem to have been strengthened by the pandemic. In addition to return migration, there may be a larger share of young adults who…

The map shows the annual average international net migration from 2010 to 2019. The map is related to the same map showing net migration in 2020. At regional level, there are only minor changes between the net migration in 2010-2019 and 2020. All regions of Norway, all regions of Sweden except Gotland and Uppsala, and the regions of Österbotten in Finland, Midtjylland in Denmark and Norðurland eystra in Iceland experienced a slight decrease in international net migration I 2020 compared to 2010-2019. There is a more marked increase in net migration in the Faroe Islands, Greenland and the region of Norðurland vestra in Iceland, and a slight increase in the region of Austurland in Iceland. At municipal level, the maps show more changing patterns. In Denmark, Norway and Sweden, several municipalities – both in the capital, intermediate, and rural regions – had lower levels of international net migration in 2020 compared to 2010-2019. In Iceland and Finland, the picture is more balanced, with some municipalities showing a decrease, others an increase. In the Faroe Islands and Greenland, several municipalities/regions had an increase in international net migration.

The excess mortality has affected overall life expectancy at birth across Europe. In 2019, prior to the start of the pandemic, Spain, Switzerland, and Italy had the highest life expectancy in Europe, followed closely by Sweden, Iceland, France, and Norway. Finland and Denmark had slightly lower levels but were still at or above the EU average (Eurostat, 2021). Life expectancy across the EU as a whole and in nearly all other countries has been steadily increasing for decades. Declines in life expectancy are rare, but that is indeed what happened in many countries in Europe during the pandemic in 2020. One study of upper-middle and high-income countries showed that life expectancy declined in 31 of 37 countries in 2020. The only countries where life expectancy did not decline were New Zealand, Taiwan, Iceland, South Korea, Denmark and Norway. The largest falls were in Russia and the United States. The high excess mortality in Sweden in 2020 has had an impact on life expectancy. In Iceland, Norway, Finland, Denmark and the Faroe Islands, life expectancy went up for both sexes in 2020 (data not yet available for Greenland and Åland). In Sweden, life expectancy fell by 0.7 years for males from 81.3 years to 80.6 and for females by 0.4 years from 84.7 to 84.3 years. The steeper decline in life expectancy for males is consistent with the larger number of excess deaths among males.  Thus, compared to other Nordic countries, the adverse mortality impact of the pandemic has been greater in Sweden. However, when comparing Sweden to the rest of Europe, it is the Nordic countries, other than Sweden, which are exceptional. The trend among countries in Europe is for a fall in life expectancy in 2020. The largest declines were in countries in southern and eastern Europe. Italy and…

The map shows the relative change in single-family house prices from fourth quarter 2019 and first quarter 2020 to second and third quarter of 2021 for Danish municipalities. The map shows that most municipalities experienced high price increases during this period, but the extent to which this change was more pronounced differs between municipalities. The increases are highest in the Copenhagen Region, but also in Aarhus and surrounding municipalities, and the peri-urban areas around Vejle. High relative increases are also found in coastal and island municipalities (e.g.  Bornholm, Lolland, Svendborg and around the Western part of the Limfjord), though it is worth noting that these municipalities had lower relative house prices to begin with. These patterns may reflect changing preferences for houses, demand for more space and access to recreational areas.

The map shows the change in regional Gross Value Added (GVA) from 2019 to 2020 (in fixed prices). As shown in the map, aggregated production levels, in terms of Gross Value Added (GVA), contracted in nearly all of the Nordic regions between 2019 and 2020. In general, the variability was comparatively smaller within each country than it was between countries, even when comparing regions with similar economic profiles from different countries. On average, the impact was greater on regions in Sweden and Finland than those in Denmark. Still, some relevant territorial patterns emerge from the changes to regional GVA shown in the map.   The contraction was larger in regions with higher dependence on tourism services and hospitality (Åland and some municipalities in South Karelia, Finland, and Bornholm, Denmark), as well as on mass-market retail and logistics, particularly in the areas surrounding the capital regions (Södermanland and Västmanland in Sweden and Greater Copenhagen in Denmark). In Sweden and Finland, a remarkable regional divide can also be traced between territories specialised in transformation sectors with limited vulnerability to the impact of Covid-19, including forestry and specific types of processing (e.g. pulp, cement), like Nord Ostrobothnia, Kainuu and Pirkanmaa in Finland, and Gotland, Västerbotten and Örebro in Sweden. Aggregated output in these regions fell less than in regions with greater exposure to industrial manufacturing, like Kymenlaakso in Finland and Kronobergs in Sweden.    Similarly, the impact on the financial centres in Denmark (Greater Copenhagen) and Sweden (Stockholm) was less than regions with mid-sized cities and diversified urban economies, like Vestjylland (Århus) in Denmark and Upsala in Sweden. Interestingly, the shock to the Finnish economy was greater in the Helsinki metropolitan area (-3.6% Uusimaa) than it was for the Tampere region (-0.5% in Pirkanmaa). This may be due to the relatively higher concentration…

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.

The map shows annual average immigration flows above 3,000 people, and the growing diversity in their countries of origin Sweden and Denmark, in particular, experienced large inflows from non-Nordic countries during the period 2010-2019, with Sweden standing out as the Nordic country with by far the largest immigrant in-flows. A large portion of these arrivals were from war-torn Syria (an annual average of almost 15,000), followed by Poland (approximately 4,500), United Kingdom, Iraq, India and Iran (around 4,000 each). Denmark experienced a smaller number of inflows above 3,000 people, compared to Sweden. The largest non-Nordic inflows to Denmark were around 5,000 people (per sending country) and included migrants from the U.S., Germany, Romania and Poland. For Norway, large non-Nordic in-flows were limited to Lithuania and Poland. Similarly, Finland had only one major inflow, from Estonia.

The map shows mortality rate per 100,000 population in 2019 adjusted by 2013 European Standard Population. To compare the relative health status of different population groups, the population’s age distribution needs to be taken into account, because death rates for most diseases generally increase with age (Curtin & Klein, 1995). “Age-standardized mortality rates adjust for differences in the age distribution of the population by applying the observed age-specific mortality rates for each population to a standard population” (World Health Organization, 2020b). In other words, this technique allows comparison between populations with differing age profiles. The populations are thus given the same age distribution structure, so that those differences in mortality which are not due to the ageing of the population can be highlighted. At a national level, there are age-standardised mortality rates higher than the Nordic average (883.0) in Greenland (2,449.2), Denmark (988.6), and Finland (921.7). These statistics show the number of deaths per 100,000 of the population in each country, after removing variations brought about by differing age structures between the three countries. In the calculation of age-standardised mortality rates, we used the European Standard Population in 2013. It is observable that age-standardised mortality rates are lower in capital regions and large cities in all Nordic countries, suggesting a lower level of ill health in these regions. There is a high variation of mortality rates between municipalities in Finland, Iceland, Norway, and Sweden. The municipalities having the highest mortality rates tend to be economically disadvantaged, with lower levels of disposable household income and a smaller proportion of people having attained tertiary education. Since the map was created using death data for only one year, it is important to recognise that the number of deaths may vary a good deal from year-to-year in municipalities with a small population.

This map indicates the geographical areas involved in case studies and more in-depth research but does not cover municipalities and other areas involved in e.g. questionnaires or more general studies.

This map indicates the geographical areas involved in case studies and more in-depth research but does not cover municipalities and other areas involved in e.g. questionnaires or more general studies.