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All possible electric aviation routes by a degree of urbanisation

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…

Access to fixed broadband at minimum download speed 100 Mpbs

The map shows the proportion of households that had access to fixed-line broadband with download speeds >100 Mbps (superfast broadband) at the municipal level, with darker colours indicating higher coverage. Overall, Denmark has the highest levels of connectivity, with 92% of municipalities providing superfast broadband to at least 85% of households. In over half (59%) of all Danish municipalities, almost all (>95%) of households have access to this connection speed. The lowest levels of connectivity are found in Finland. This is particularly evident in rural municipalities where, on average, less than half of households (48%) have access to superfast broadband. Connectivity levels are also rather low in some parts of Iceland, for example, the Westfjords and several municipalities in the east.  Households in urban municipalities are still more likely to have access to superfast broadband than households in rural or intermediate municipalities, but the gap appears to be closing in most. This is most evident in Norway, where the average household coverage for rural municipalities increased by 31% between 2018 and 2020. By comparison, average household coverage for urban municipalities in Norway increased by only 0.7%. In the archipelago (Åland Islands, Stockholm and Helsinki), general broadband connectivity is good; however, some islands with many second homes still have poor coverage. 

Remote work potential

The map shows the share of jobs that can potentially be done from home. At the municipal level it shows that the highest proportion is in, or in the proximity of, the largest urban conurbations.   The purple areas show the municipalities that has a remote-work potential above the Nordic average (37%) and the blue areas the municipalities with remote-work potential below the Nordic average.  The indicator is based on the methodology of Dingel & Neiman (2020). This method estimates the proportion of jobs that can theoretically be performed from home based on the tasks included in different occupations. Dingel & Neiman’s US classification was translated to the European International Standard Classification of Operations (ISCO-08) codes. The data is based on the 4-digit ISCO-08 and includes 437 occupations. The result was that every 4-digit ISCO occupation was coded as either 1: possible to work from home or not possible to work from home. For more information about the method please look at the State of the Nordic Region 2022 publication.  The ten municipalities with the highest proportions are all in capital regions, with seven out of 10 in either Copenhagen (Hovedstaden) or Stockholm Region. In general, people in urban municipalities are more likely to be able to work from home (46.2%) than those in intermediate municipalities (32.3%) and rural municipalities (27.8%).    It seems to be the case that the higher proportion of jobs that can be done from home in urban areas relates to the differences in industrial and occupational profiles between urban and rural areas, in particular, a higher concentration of knowledge-intensive occupations in urban areas. These differences are also evident when comparing countries. For example, Denmark has a rather large number of municipalities with high proportions of jobs that can be done from home. This may be due to…

Unemployment typology

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.

Cross-border commuting as share of employment

The map illustrates the average share of employees who commuted to another Nordic country between between 2015 and 2018 in Nordic regions (NUTS 2). Between 2015 and 2018, an average of approximately 49,000 people held a job in a Nordic country in which they were not residents. This indicates that, on average, 0.5% of the Nordic working-age population commuted to a job in another Nordic country. This is below the EU27 average of 1%, with the highest numbers found in Slovakia (5.1%), Luxembourg (2.8%) and Estonia (2.6%). Some of these people cross borders daily. Others work in another country by means of remote working combined with occasional commuting across borders.  Within the Nordic Region, the largest cross-border commuter flows are in the southernmost parts of Sweden, regions in the middle of Sweden and in Åland, where more than 1% of the working population commutes to another Nordic country. However, there may be individual municipalities where cross-border commuting is substantially higher. For example, the employment rate in Årjäng Municipality, Sweden, increases by 15 percentage points when cross-border commuting is taken into account. These municipalities are not reflected on NUTS 2 level when averages are calculated. In terms of absolute numbers in 2015, the highest numbers of commuters were from Sweden: Sydsverige (16,543), Västsverige (7,899) and Norra Mellansverige (6,890). The highest number of commuters from a non-Swedish region were from Denmark’s Hovedstaden (2,583).   Due to legislative barriers regarding the exchange of statistical data on cross-border commuting between the Nordic countries, more recent data is not available.