Regional GDP data for 2020 is not yet available, which limits the extent to which the impact of the COVID pandemic on the economies of the EU regions can be assessed. A modelling exercise using national data and the RHOMOLO regional model, however, gives an indication of the potential regional impact. It shows a particularly severe impact on southern European regions and France, and less effect on Nordic and eastern regions ( Map 1.7 ). The model suggests that in Spain, Italy, France, and Greece, some regions are likely to experience a particularly sharp reduction in GDP. This is especially so for those with a large share of value-added in wholesale and retail trade, transport and accommodation, i.e. in the sectors where tourism is important, which is line with the actual changes in hours worked in 2020 indicated above.

In 2019, 5.5% of the employed population in the EU usually worked from home. Because of the pandemic, and the requirement to work from home where possible, the proportion more than doubled to 12.4% in 2020. The capacity to work from home depends on the type of activity concerned. Some jobs can only be performed in person, as noted above, such as many jobs in healthcare, manufacturing and agriculture. Many of the regions with large cities saw big increases in the proportion of people working from home, reflecting the large share of economic activities which can be performed remotely (usually by high-skilled workers). In particular, the increases were over 15 pp in the Brussels, Helsinki, Dublin, Paris, Cologne and Vienna regions ( Map 1. 5 ). The distribution of critical and ‘teleworkable’ jobs strongly depends on the degree of urbanisation. Rural areas tend to have a larger share of ‘non-teleworkable’ jobs than cities, towns and suburbs.

The biggest reduction in hours worked over the period occurred in the accommodation and food services sector (by 52%) and the arts, entertainment and recreation sector (by 36%). The two most affected broad occupational groups were service and sales workers (which showed a fall of 27%) and elementary occupations (one of 23%).

Because of the pandemic, the number of hours worked declined significantly in the EU between 2019 and 2020, though the scale of the reduction depends on the source of the data used and the working time for which hours are measured. The Labour Force Survey (LFS), which measures weekly hours, indicates a reduction in the EU of 12%, whereas the national accounts data, which measures annual hours, shows a reduction of 6% ( Figure 1. 11 ). Both sources agree, however, that the biggest reductions occurred in Greece, Spain, Portugal and Italy. The LFS data also show that regions with large tourist economies were especially affected ( Map 1.5 ). More developed regions were slightly less affected (with a reduction of 10% based on LFS data) than transition and less developed regions (a fall of 13% in each).

Across the EU, the employment rate declined by most in towns and suburbs (1.1 pp) between 2019 and 2020, followed by cities (0.7 pp) and it barely fell at all in rural areas (by 0.3 pp). The quarterly figures show that the reduction was largest in cities in the second quarter, but it was then overtaken by the fall in towns and suburbs ( Figure 1.10 ).

As reflected by the unemployment figures, the employment rate (of those aged 20-64) in the EU also fell by relatively little, by 0.7 pp between 2019 and 2020. The reduction was largest (1.4 pp) in the southern EU, followed by the north-western EU (0.6 pp) and t the eastern EU (0.2 pp).

At the EU level, employment fell by 3 million, or 1.5%, between 2019 and 2020. Southern EU lost the most employment (2.7%). The reduction in eastern EU was smaller (1.2%), while in north-western EU, it fell by least (0.9%). Employment started to recover in the second quarter of 2021 but has not yet reached its 2019 level.

The pandemic’s impact on the labour market was much more limited due to the many job retention schemes put in place shortly after the outbreak of the crisis. As a result, the economic slowdown did not lead to large increases in unemployment. The EU unemployment rate only went up by 0.5 pp between December 2019 and June 2021, from 6.6% to 7.1% with a peak at 7.7% in September 2020. By contrast, in the United States, which did not rely as much on job retention schemes, the unemployment rate doubled from 3% to 6% between December 2019 and June 2021, with a peak of 14% in April 2020 ( Figure 1. 9 ).

The tourism is not the only sector to have suffered from the economic downturn triggered by the pandemic. Contact-intensive services were also severely affected. In the second quarter of 2020, activity in these sectors was 25% below pre-COVID-19 levels. Other sectors were less affected but still experienced a sharp drop in activity, notably manufacturing (down by 19%) and construction (down by 15%). Services with significant scope for working remotely and with high-skilled workers, like ICT, banking and finance, contracted much less (by less than 10%) and these activities tended to rebound more quickly.

Map 1. 4 ). Regions scoring highly on all three indicators are likely to have been more affected by the reduction in travel and nights spent. For example, the Mediterranean coastal and island regions are likely to have been particularly heavily affected.

Some regions are particularly dependent on tourism, including many of the Mediterranean islands and some coastal regions, the Alpine regions, the Black Sea Coast, Algarve and the Canary Islands. Some capitals and large cities also attract many tourists, but they are less dependent on tourism than coastal or mountain destinations because of much stronger and more diversified economies. To identify the regions most dependent on tourism, three indicators can be combined: nights spent per resident, seasonality of nights spent and the share of foreign tourists (

The countries with the biggest reductions in the number of nights spent per resident were Cyprus, Malta, Croatia, Greece and Spain, with reductions of more than double the EU average ( Figure 1. 8 ). The reductions were much smaller in countries with generally relatively few tourist nights per resident.

The restrictions on international travel also disproportionally affected border areas. People who usually crossed a national border for work, education, healthcare or other services were suddenly no longer able to do so. After the initial restrictions were put in place, while some borders made allowance for cross border commuting, many did not, which underlines the need for a better governance system for functional border areas.

These reductions were primarily caused by the restrictions on international travel that were introduced after the start of the pandemic. By the summer of 2020, all Member States had instituted some restrictions and these mostly stayed in place until summer 2021 ( Figure 1. 7 ). The restrictions on internal movement were part of the response to the first wave of the pandemic, but were loosened in summer 2020. During the second and third waves, internal restrictions remained much laxer. This allowed domestic tourism to recover somewhat during the summer of 2020, but the number of nights spent by tourists in the winter and spring of 2021 remained much less than in 2019.

Restrictions on movement within countries and limits on non-essential travel brought tourism to a standstill. The number of nights spent by tourists plummeted with the outbreak of the pandemic and the strict travel restrictions ( Figure 1. 6 ), falling by more than 90% compared to the same month in the previous year. The nights spent by domestic tourists recovered in the summer of 2020 but then fell again. The nights spent by international tourists remained extremely low throughout 2020 and the first half of 2021. Overall in 2020, nights spent dropped by 54% in relation to 2019, but those spent by international tourists fell by far more (70%) than those spent by domestic ones (39%).

The economic impact of the COVID crisis varies widely across Member States. Between 2019 and 2020, there was a reduction in real GDP growth of 13 percentage points (pp) in Malta and Spain (GDP increasing by 5.5% in 2019 and falling by 7.8% in 2020 in the first and increasing by 2.0% in the second and falling by 10.8% in the second) while the reduction was less than 5 pp in Finland, Denmark and Luxemburg, and in Ireland, there was even a small increase ( Figure 1. 5 ). Economic activity rebounded in 2021, in particular in the Member States where it fell the most.

The COVID-19 pandemic triggered the deepest post-war recession in Europe. Real GDP growth averaged 2.1% a year between 2014 and 2019. In 2020, real GDP fell by 6.0%. All economic sectors were affected by the consequences of containment measures, the disruption of global supply chains, the sharp reduction in demand for goods and services, and the fall in tourism, business travel and recreation. Across Europe and the rest of the world, the crisis led to unprecedented policy responses to mitigate the effects of the shock and strengthen the recovery.

The stay-at-home requirements and the internal movement restrictions meant that people had to rely more on local facilities and amenities. The requirement to work from home and the closure of schools meant that many people in cities were crowded into small living spaces during the day. This highlighted the benefit of nearby green areas that were open to the public. In most cities the majority of the residents can reach at least one hectare of green urban area by walking a short distance. In a number of cities, however, less than half of the people have easy access to green urban spaces. This is the case in all the cities in Cyprus, Malta and Romania , and some big cities in Italy, France and Portugal, where less than half the residents have a green urban area within 400 metres of their home ( Map 1. 3 ). , The working from home requirements and remote lessons also posed challenges for households without fast internet connections, which is more often the case in rural areas.

The difference between Member States, however, was greater as regards specific kinds of restriction. For example, some Member States had long periods during which people were required not to leave their homes except for a short period of daily exercise, grocery shopping or essential trips ( Figure 1. 3 ). On the other hand, some Member States imposed no stay-at-home requirements for almost the whole period, while others imposed only modest restrictions. Eastern Member States tended to have the least restrictions and the southern ones the most. During the first wave, north-western Member States imposed similar restrictions to the eastern ones, while during the second and third waves, they had a stricter approach more similar to southern Member States.

Restrictions imposed in response to the pandemic did not differ greatly between EU Member States ( Figure 1. 3 ). Restrictions peaked in April 2020, were relaxed in summer 2020, and were increased again during autumn and winter 2020-2021. Restrictions started to recede slowly in May 2021 and continued to do so up to September. On average, restrictions in eastern Member States were slightly less strict, while southern Member States had the tightest ones and north-western Member States were in between the two.

The depth of the economic recession during the pandemic was affected by three main factors. First, the length and the strictness of lockdown measures implemented by national, regional and local authorities to limit the spread of the virus. The places with stricter lockdown measures tended to experience a deeper recession. Second, some types of economic activities were much more affected than others. Services, notably accommodation and those relating to culture, leisure, tourism and activities requiring proximity generally have particularly suffered from the containment measures. Member States and regions that are more dependent on these sectors, have seen a bigger drop in their economic activity. Third, the policy response of Member States, regions and local authorities varied in scope and intensity, in part reflecting the differential impact of the pandemic.

The report argues that local and regional authorities should be closely involved in the preparation and implementation of the national Recovery and Resilience Plans. A first assessment indicates that local and regional authorities were not consistently consulted during the preparation of these plans and that some of these consultation only had a limited impact on the final plans.

The report highlights the concern that the pandemic may reduce subnational finance through a combination of falling revenues and rising expenditures . A first rough estimate indicates that this could lead to a funding gap of €180 billion for EU local and regional authorities, if left unaddressed. Fortunately, significant EU and national support to local and regional authorities is likely to have mitigated this effect, but it may still leave some regions and cities more exposed than others. The report also discusses the multiple causes of the asymmetric health impact of the pandemic, ranging from different age structure, mobility, restrictions, underlying health issues, the difference in healthcare capacity and the varied uptake of the vaccines. The report concludes only a place-sensitive policy response can factor in these big spatial differences.

The Annual EU Regional and Local Barometer report by the European Committee of the Regions highlights the current and future challenges for cities and regions in the European Union. The latest edition of this report covers a wide range of issues including the potential asymmetric financial and health impacts of the pandemic and the Recovery and Resilience Plans.

Vaccines offer the best way out of the pandemic. In November 2021, approximately 70% of the total population was fully vaccinated. Uptake of vaccinations, however, differed between and within Member States. Data reported in November indicated that in multiple regions in Romania and Bulgaria less than 20% of the population was fully vaccinated, while in many regions in Belgium, France and Spain more than 80% of the population was fully vaccinated ( Map 1. 2 .

Because of COVID-19, life expectancy in 2020 fell in almost all Member States. The biggest reductions were in Spain (-1.6 years) and Bulgaria (-1.5 years). In only two Member States, Denmark and Finland did life expectancy increase, though only marginally.

The excess mortality rate during the first wave was highest in urban regions and peaked at 80% in April 2020, while it was lower than 40% in intermediate regions and only 20% in rural regions. During the second wave, rural regions had the highest excess rate, which peaked at 55%, while it was somewhat lower in towns and suburbs (48%) and cities (43%) ( Figure 1.2 ).

Regional excess mortality since the start of the pandemic shows the cumulative impact of the different waves ( Map 1. 1 ). It reveals hotspots in northern Italy and Madrid, which were heavily affected in the first wave, as well as in Poland, Czechia, Slovakia and Bulgaria, which were more affected in later waves. Overall, less developed regions had the highest excess mortality rate (17% higher) as compared with transition (11%) and more developed regions (12%).

The excess mortality during the first wave mainly affected regions in Italy, Spain, France, Belgium and the Netherlands. During the second wave, excess mortality was highest predominantly in regions in Eastern Europe, in Poland, Bulgaria, Slovenia, Czechia, Romania and Hungary.

Between March 2020 and July 2021, the COVID-19 pandemic has led to excess mortality in the EU of at least 872,000 deaths. In other words, compared to the average of the five previous years, the number of deaths since the start of the pandemic was 13% higher. This includes deaths directly resulting from COVID-19 and those caused indirectly because of the saturation of hospital capacity and lack of usual care. For example, half the NUTS-3 regions, for which data are available, experienced at least one week with over double the usual mortality ( Map 1. 1 ).

·The number of people usually working from home doubled. This increase was highest in many of the capital regions. These regions typically have a more developed service economy, host jobs that can more easily be done remotely, have a highly educated labour force and high quality IT infrastructure. All these factors facilitated the increase in working from home.

·Thanks to job retention schemes the impact of employment and unemployment was much smaller compared to the reduction in hours worked and GDP. This allowed the EU to avoid a big spike in unemployment.

·The travel restrictions not only affected the tourism sector, but also border areas where people could no longer cross a national border to go to work or to access services.

·The restrictions put in place to contain the pandemic led to the deepest post-1945 recession. The impact was largest on southern regions, especially those dependent on tourism, where the reduction in hours worked and GDP were the most severe.

·The outbreak of the COVID-19 pandemic has led to at least 872,000 more deaths in the EU compared to previous years. Excess mortality was higher in less developed regions than in transition and more developed ones. While the first wave affected primarily north-western regions and southern regions, the following waves led to the highest mortality in eastern regions.

COMMUNICATION FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT, THE COUNCIL, THE EUROPEAN ECONOMIC AND SOCIAL COMMITTEE AND THE COMMITTEE OF THE REGIONS

Since development traps can occur at different levels of development, and appear to be a particular risk for transition regions, they may require policy responses that go beyond the poorest regions. Assisting all regions that are development-trapped to become more dynamic will help to reduce regional inequalities and counter the threat of rising discontent in EU societies.

Regional development traps are a serious risk for the future of the EU. Springing these traps and so liberating the untapped economic potential of the many struggling and stagnating regions in the EU would not only increase their GDP, productivity and employment, but would also boost the growth potential of the EU as a whole. This is not just an economic matter; the sub-par economic performance and lack of employment opportunities are causing social costs and political resentment towards what is increasingly regarded as a system that does not benefit areas that are left behind, leading to a growing geography of discontent.

The differing characteristics of the regions suggest different approaches to avoiding being development-trapped, depending on a region’s level of development. The chances of a region with below average GDP per head in 2000 avoiding being trapped are improved by having a better quality of government and larger industrial output. The latter would also improve the chances of transition regions in this respect. For more developed regions, the chances of staying out of a development trap are better if they have higher R&D investment and a more highly educated work force. In all regions, the chances could be improved by increasing the share of working-age population with tertiary education.

EU regions that were development-trapped in 2000-2019 tend to have a smaller share of industrial output in total production, smaller endowments of human capital, (fewer workers with tertiary education,) and lower levels of support for science and technology ( Table 2 ‑ 6 ). Regions with a better quality of local government, and so a more favourable institutional environment, tend to fare better than those with low government efficiency, limited transparency and accountability, and more corruption. Development-trapped regions also tend to have higher old-age dependency rates and less demographic dynamism, though this is likely to be as much a consequence as a cause of being trapped.

The reasons for falling into a development trap differ between regions. However, there are a number of common traits, including, for example the levels of value-added in industry, human capital, innovation endowment and institutional quality.

-Development-trapped regions with above-average GDP per head, which despite still being relatively prosperous have experienced frequent or long periods of below average growth in GDP, productivity and employment, often because of the demise of industries that used to be their main source of wealth. This group includes a number of regions in northern and central Italy, various regions in France, and a few in Spain, Portugal, Germany, Denmark, Austria and the Netherlands.

-Development-trapped regions with below average GDP per head between 75% and 100% of the EU average in 2000, but where economic dynamism has since stagnated. Accordingly, they have struggled to improve their standing, often in both relative and absolute terms. This group includes a number of regions in the Italian Mezzogiorno and regions in Portugal, Greece and Cyprus, as well as several regions in France and Wallonia in Belgium.

-Development-trapped regions with very low GDP per head, which receive substantial Cohesion policy support, but which, unlike most of the other less developed EU regions, have struggled to sustain long term growth, so consistently lag behind other regions in the EU. Regions in this group include Calabria in Italy, and Anatoliki Makedonia, Thraki and Ipeiros, and Dytiki Ellada in Greece.

Analysis based on this approach shows that the number of years that regions were in a development trap over the 2001-2019 period varies greatly between them ( Map 2 ‑6 ). In general, regions that were in a development trap in 15 years or more during this period (henceforth called ’development trapped‘ regions) are concentrated in southern EU Member States (especially in Greece and Italy) or are rural or old industrial regions in France. Some of the regions, however, are also located in many of the north-western Member States, and so include regions at different levels of initial development. Accordingly, three types of development-trapped region can be identified in terms of their GDP per head in 2000.

For the analysis here, a region is considered to be ’in a development trap’ in a specific year, if the risk of being trapped is greater than 0.5. A region is considered ‘development-trapped’ over the period 2000-2019 if in 15 or more years the risk is greater than 0.5.

Based on these comparisons, various risk indicators are calculated. The indicator used in this report is calculated as follows. For each of the nine comparisons, if the recent growth rate in the region is lower than the benchmark, the region receives a score of one; if not, a score of zero. The risk of the region falling into a development trap in the year in question is given by the average score over the 9 (or 6) comparisons.

For each of these three variables, the growth rate of the region over the 5-year period preceding the year in question is compared to three benchmarks:

The methodology developed by Iammarino et al. (2020) to assess whether a NUTS 2 region is in a development trap in a specific year is based on the development over time of three variables: (i) GDP per head at constant prices, (ii) GVA per person employed (productivity) at constant prices and (iii) the ratio of employment to population.

In Iammarino et al. (2020) the risk of a region being in a development trap in a specific year is assessed in terms of the pattern of growth of GDP per head, productivity and employment, as well as their growth relative to that of the Member State the region is located in and the EU average.

The low growth of regions in the middle category suggests that they may have fallen into a development trap. Many of them are less cost-competitive than less developed regions, characterised by low-cost of capital and labour, and less innovative or productive than more developed regions. Accordingly, their costs tend to be too high to compete with less developed regions and their innovation systems not strong enough to compete with more developed regions. This makes it very difficult for them to escape the development trap and achieve higher GDP per head. While some of these regions had low GDP per head earlier and were catching-up until some years ago, others were formerly relatively prosperous but have moved into a prolonged period of relative economic decline. Indeed, in a quarter of the regions with above average GDP per head in 2000, mainly in north-western but also in southern Member States, GDP per head had fallen below the EU average by 2019 ( Map 2 ‑5 ).

Since 2000, an increasing number of regions have experienced stagnating economic development after reaching a level of GDP per head of 75-100% of the EU average ( Map 2 ‑5 ). As this group has grown larger over time, transition out of it has become rarer. Indeed only one region (Zahodna Slovenija) out of a total of 53 regions in the middle category in 2000 managed to achieve above-average GDP per head by 2019. On the other hand, in 18 of these regions, mainly in the southern EU, GDP per head fell below 75% of the EU average, implying divergence and increasing disparities.

Very low growth is defined here as annual average growth over the period in the bottom quartile of regions.

Iammarino et al. (2020) develop a concept of ‘development traps’, which is based on more dimensions than just a slowdown in GDP growth. It covers three dimensions of the economic dynamism of a region: GDP per head, productivity and employment. Some 45% of the population of the above mentioned middle category regions in 2000 were in regions where growth was very low over the 2001-2019 period ( Figure 2 ‑ 9 ). Moreover, a third of the population were in regions where productivity growth was very low and 40% in regions with very low employment creation relative to the change in population. All of these population shares are higher, in some cases considerably, than in other regions.

However, growth in the group of regions with GDP per head between 75% and 100% of the EU average (i.e. the middle category), does not show any indication of catching-up. Indeed, average growth in these regions was below that of those with above-average GDP per head. Many of them, primarily those in southern EU Member States, experienced lengthy periods of low or negative growth, weak productivity increases and low employment creation or even job losses.

Some of the patterns are in line with convergence theory. In particular, many of the regions with GDP per head below 75% of the EU average in 2000 displayed strong growth over the subsequent 19 years, demonstrating rapid catching up. These regions are mainly those in eastern EU Member States. Conversely, many of the southern EU regions failed to achieve comparably high growth rates. A non-negligible number of southern regions experienced a reduction in GDP per head over the period, even if their initial GDP per head was below 75% of the EU average. Consistent with convergence theory, regions with above-average GDP per head in 2000, tended to have lower rates of growth.

Relating the annual growth of real GDP per head over the 2001-2019 period to the initial level of development of regions in 2000, as measured by GDP per head, reveals some striking patterns ( Figure 2 ‑8 ).

It has become increasingly clear over recent years that not all regions in the EU with a GDP per head below the average are catching up. Regions can be categorised into different groups, defined in terms of their level of GDP, but also by their rates of GDP growth.

Employment in both metro and non-metro regions increased between 2000 and 2008, although at a faster rate in capital metro regions than in other metro regions and by more in the latter than in non-metro regions ( Figure 2 ‑ 7 ). In the following two years, it declined markedly in all regions. In the capital city regions, it began to recover in 2010, with the growth rate accelerating in 2013 and continuing at the same pace up to 2019, when total employment was significantly higher than before the 2007-2008 crisis. In other metro regions, recovery was more hesitant. Employment remained below pre-crisis levels up until 2015, and from then to 2019, its growth rate was more modest than in the capital city regions. In non-metro regions, the effect of the financial crisis was more sustained; employment only began to increase in 2013 and it grew by much less than in metro regions up to 2019, only reaching pre-crisis levels in 2018.

Capital metro, other metro and non-metro regions are defined as follows. Metro regions are NUTS-3 regions, or groupings of NUTS-3 regions, representing functional urban areas of more than 250 000 inhabitants. Capital metro regions are those that include the national capital. Non-metros regions are all others.

In regions in the eastern and north-western Member States, the growth of GDP per head was mainly associated with productivity growth. The pattern is different in southern Member States. Productivity growth was very low during this period and most of the (modest) growth in GDP per head was associated with growth in employment. In capital metro regions in the eastern and southern Member States, the contribution of employment growth to GDP growth was double the average, reflecting a continuing concentration of employment there.

Between 2001 and 2019, real GDP per head in metro regions grew faster than in others in all parts of the EU. ( Table 2 ‑ 5 ). This was a result mainly of above average growth rates in capital city regions, though other metropolitan regions also outperformed non-metropolitan regions, except in the north-western Member States.

In 2019, metropolitan (metro) regions accounted for 59% of population in the EU, 63% of employment and 68% of GDP. Accordingly, they are major centres of employment and business activity with higher productivity than elsewhere.

Between 2014 and 2019, growth of GDP per head resumed in every regional group. Unlike in the period before the financial crisis, however, growth was strongly associated with an increase in the employment rate, which more than offset a reduction in the share of working-age population, while labour productivity grew more slowly than in the pre-crisis period. Again, this pattern of change was more pronounced in the less developed regions. On the other hand, recovery was more subdued in the transition regions, with GDP per head growth being only slightly more than half that in less developed regions, much the same as in the pre-crisis period.

The same identity can be expressed in terms of changes: The change in GDP per head is the sum of the changes in productivity, in the employment rate and in the share of working age population.

Growth in GDP per head can be broken down into three main components: changes in productivity (GDP per person employed), changes in the employment rate (employment relative to population of working age) and changes in the share of working age population in the total. Accordingly, the following identity holds:

Between 2009 and 2013, GDP per head in the EU declined by 0.4% a year. Employment also declined (by 0.5% a year) as both the employment rate and the share of the working-age population fell, while productivity continued to increase, though at a slower rate. This pattern of change is mirrored in each group of regions, but it is more pronounced in the less developed regions and less pronounced in the more developed ones. Accordingly, the less developed regions, as a group, experienced the sharpest decline in GDP per head, but also in the employment rate.

From 2001 to 2008, GDP per head in the EU grew by 1.8% a year in real terms, with productivity growing by 1.2% a year and an increase in the employment rate adding another 0.4% a year ( Table 2 ‑ 4 ). In many less developed regions, where GDP growth was substantially higher than the EU average, productivity growth was also the main component of growth in GDP per head, and even more so than in the EU as a whole, while the employment rate remained unchanged.

Over the 2001-2019 period, GDP per head increased in the vast majority of EU regions ( Map 2 ‑4 and Table 2 ‑2 ). The increase was largely associated with productivity growth, and to a lesser extent with employment growth. Working-age population as a share of the total decreased slightly in the EU and in most regions over this period. Many less developed regions, especially those located in the eastern Member States, had above average productivity and employment growth, offset only slightly by a decline in the share of working-age population, so that growth of GDP per head growth was above the EU average. This, however, masks the fact that in a number of these regions, mainly in Greece and Italy, GDP per head fell over this period, with productivity falling and the employment rate declining or increasing relatively little, combined with a shrinking share of working age population. In most of the EU outermost regions GDP per head remained at the same level or decreased.

The construction industry showed little growth over the 2001-2018 period and even contracted slightly in transition regions. By contrast, employment and GVA in services increased in all regional groups over the period, particularly in financial activities, especially in less developed regions.

Employment in industry also declined in each of the three types of region, though much less so than in agriculture. Despite the loss of labour, GVA increased substantially, as did productivity, especially in the less developed regions. The EU single market has created more potential for specialisation in higher value-added sectors, enabling less developed and some transition regions to maintain a larger share of employment in industry, because they have an attractive balance between labour costs, productivity and accessibility.

Employment in agriculture fell between 2001 and 2018, especially in the less developed regions (by over 3% a year), reflecting their economic restructuring and agricultural modernisation. The latter led to a substantial increase in productivity in the sector and an increase in GVA. Given the large share of employment in agriculture in these regions, this process is likely to continue. The same pattern is observed in the transition and more developed regions, but the reduction in employment and growth in GVA were less than half that in less developed regions.

The sectoral composition of gross value-added (GVA) follows the same general pattern as employment, but the differences between regions at different levels of development tend to be less pronounced. Notably, despite the large work force in agriculture in less developed regions, GVA from agriculture is modest, implying low productivity.

Regions at different levels of development tend to have different economic structures. Less developed regions tend to have relatively large shares of employment in agriculture and industry ( Table 2 ‑ 3 ). In 2018, over 12% employment in these regions was in agriculture, three times more than in transition regions and 8 times more than in more developed ones. Around 21% of employment was in industry, 6 percentage points (pp) more than in transition and more developed regions. Transition and more developed regions are more comparable in terms of their employment shares, with more employed in finance and insurance and public administration.

In the southern and eastern regime, the absorption of technology is important for growth and has positive spill-over effects on neighbouring regions as well. Indeed, spill-over effects are more important generally in southern and eastern regions than in north-western regions, where such effects were possibly significant in earlier periods.

2.Are the determinants of economic growth and resilience the same across regions at different levels of economic development (in terms of GDP per head)?

In the north-western regime, the quality of institutions is an essential determinant of growth, which accords with recent findings in the literature that highlight good institutions as a key growth factor, especially at more advanced stages of development (Annoni and Catalina-Rubianes, 2016; Pike et al., 2017). In the north-western regime of the EU (plus the UK), regions were more resilient if they had higher public and private investment. Results also indicate that high investment levels induce significant positive spill-over effects, suggesting that larger shares of investment in a region have positive effects on the growth rate of neighbouring regions.

Spatial effects are found to be important in all regions. Regions benefit from being surrounded by high-growth ones in both the north-western and south-eastern regimes. Human capital is an important factor of development in both, with basic education being particularly relevant: having large shares of low-educated people appears to be a more important impediment to growth than having smaller shares of high-educated people.

The main part of the analysis is based on an economic growth model where regional growth depends on growth in neighbouring regions and a set of initial endowments, from classical ones - initial level of GDP per head, population growth, human capital and investment - to more complex components of regional competitiveness - quality of government, business sophistication, technological readiness and innovation. The model also takes account of the geographical proximity of regions when assessing their economic development and detects spatial spill-over effects when present, including cross-border (LeSage and Fischer, 2008). Based on this model, the analysis identifies which of these factors has contributed to economic growth in the regions and the size of the effect. A more in-depth discussion of the theoretical framework and assumptions underlying the analysis is provided in Annoni et al. (2019). The main findings, summarised in Table 2 ‑2 , are as follows.

Regions in the EU27 plus the UK are classified into two regimes, based on their initial GDP per head in 2008: a north-western group of relatively high-income regions and a group of southern and eastern lower income regions. The main questions analysed are:

A recent study (Annoni et al., 2019) focuses on the crisis and post-crisis years, 2008–2015) and examines the factors helping regions to recover from the Great Recession, the main aim being to identify the characteristics of regions that showed economic resilience and any potential spill-over effects.

GDP per head in the regions is below the EU average ( Table 2 ‑1 ). In Mayotte, with a population of around 270 000 in 2019, it is only around a third of the EU average, meaning that the region lowest GDP per head in the EU. GDP per head is also low in Guyane (45% of the EU average) and Reunion (68%). The low GDP per head in these three regions is primarily linked to low employment rates and, in the case of Guyane and Mayotte, also to low productivity per worker. Productivity is also low in Madeira and Açores. The share of working-age in total population in the outermost regions is in most cases closer to the EU average, though in Mayotte, reflecting the large number of young people, it is well below and in Canarias, Madeira and Açores well above.

Around five million people live in the outermost regions, some of which have significant population growth due to inward migration. The natural growth rate in population is also relatively high as in most of these regions the population is much younger than in the mainland EU.

The EU includes nine outermost regions, geographically remote from the continent in the Caribbean basin, the Macaronesia area and the Indian Ocean. They are Canarias (ES), Guadeloupe, Guyane, La Réunion, Martinique, Mayotte, Saint-Martin (FR), Madeira and Açores (PT). They are governed by the provisions of the Treaties and form an integral part of the Union.

The growing interdependence of the world’s economies has had a highly differentiated impact on EU regions. While some have been well positioned to take advantage of the new opportunities it offers, others have been hit by job losses, stagnating wages and shrinking market shares as a result of low-cost competitors moving into more technologically advanced sectors (see also Section 2.4 below).

Predominantly rural regions have a GDP per head, in PPS terms, around 70% of the EU average ( Figure 2 ‑ 6 b). Over the period 2001-2019 rural regions close to cities showed convergence to the EU average. This did, however, not hold for remote rural regions where GDP per head slightly decreased relative to the EU. Remote intermediate regions also diverged from the EU average over this period.

The transition regions, however, do not follow the same pattern. From 2005 onwards, growth in these regions was below the EU average, except in 2009. As a result, GDP per head, in PPS terms, diverged from the EU average instead of converging ( Figure 2 ‑ 6 a).

The economic convergence of regions over the period 2001-2019, as noted above, was mainly driven by the catching up of many of the less developed ones, their GDP per head growing faster than elsewhere, except in 2010 and 2011 immediately following the global financial crisis ( Figure 2 ‑5 ). The average picture, however, hides differing trends among less developed regions. While there has been strong growth and significant catching up in those in eastern Europe, many less developed regions in southern Europe have experienced sluggish or negative growth and their GDP per head is diverging away from the EU average (Section 2.3 below examines on these trends further).

Regional disparities in employment and unemployment rates also narrowed from 2000 up to the financial crisis when they widened to reach a new peak in 2013. After then, they began narrowing again, but, in 2020, the disparities in both were wider than in 2008. Disparities in the employment rate remain at much the same level as in 2000.

Note: Disparities are measured by the coefficient of variation (CV) and the mean absolute deviation (MAD). Both are weighted by the population in each region. The analysis is based on the NUTS2 level but regions which are part of the same metropolitan area are combined.

Prior to the 2007-2008 crisis, disparities in GDP per head in the EU were shrinking, mainly because of regions with the lowest levels growing faster than average ( Figure 2 ‑4 ). However, in the years immediately following the crisis, regional disparities widened slightly. There are signs that the long-term process of regional convergence, which was interrupted by the crisis has resumed, although at a very slow pace.

Note: Regions are ranked by the growth rate of GDP per head over the period 2001-2019; Capital city regions are indicated in red.

Note: Regions are ranked by the growth rate of GDP per head over the period 2001-2019; Capital city regions are indicated in red.

Note: Regions are ranked by the growth rate of GDP per head over the period 2001-2019; Capital city regions are indicated in red

Overall, more than a quarter of the EU population live in a region where by 2019 real GDP per head had still not returned to pre-crisis levels. This includes the entire population of Greece and Cyprus, 80% of Italians and a third of Spaniards, but also 75% of the Finnish population and over a third of Austrians. In most of the eastern Member States, GDP per head had returned to pre-crisis levels in all or nearly all regions. However, in Romania and Croatia 40% and 25% of the population, respectively, live in regions where this is not the case.

The 2014-2019 period shows a clear recovery from the Great Recession ( Map 2 ‑3 , Figure 2 ‑3 ). Almost all regions experienced growth in GDP per head, though at a lower rate than in the pre-crisis period. High growth rates were restored in most eastern regions, so contributing again to convergence. By contrast, growth in many north-western regions remained below pre-crisis rates, Ireland being the main exception. In many regions in the hard-hit southern Member States, especially in Portugal and Spain, growth rates recovered, but in Greece and many regions in Italy, growth remained low.

The global financial crisis of 2007-2008 led to GDP per head in the EU declining between 2009 and 2013. Around 60% of the EU population lived in regions with a declining GDP per head ( Map 2 ‑3 , Figure 2 ‑2 ). The regions hit hardest were mainly in the southern EU countries, though also in Romania, Ireland and Finland. In most Greek regions, the reduction in GDP per head averaged over 3% a year. The crisis led to many of the less developed and transition regions growing more slowly (or shrinking faster) than the EU average during this period, so reversing the tendency towards convergence. The process of convergence was, therefore, brought to an end and disparities began to widen again. Most regions in Poland and some in Bulgaria and Romania were notable exceptions.

Between 2001 and 2008, nearly all regions experienced growth in GDP per head ( Map 2 ‑3 ). Overall, growth was above average in both the less developed and the transition regions, with rates of over 5% a year in many of those in eastern Member States. This is in line with mainstream economic growth theories, which predict that growth will tend to be higher the lower the initial level of GDP per head. Most of these regions are in less developed and moderately developed Member States, where for the most part growth was faster than the EU average ( Figure 2 ‑1 ). In Romania and Bulgaria, where the growth rate was particularly high, the catching up was not uniform across the country but was driven by the capital city region. Regions in southern Italy, however, did not follow this pattern of catching-up. They already experienced negative growth in the 2000s even though their GDP per head was well below the EU average.

Over the 2001-2019 period, GDP per head in real terms increased in the vast majority of EU regions ( Map 2 ‑2 ), albeit at a modest rate in most cases. Growth was particularly high in the eastern Member States and Ireland. In most regions in Greece, however, GDP per head fell over this period, as it did in Italy, both in many of the more developed regions in the north and in many of the less developed in the south. At the same time, growth was very low in transition regions in the north of France.

In 2019, over one in four people in the EU (29%) lived in a NUTS 2 region with GDP per head below 75% of the EU average in PPS terms, most of them in eastern Member States, Greece, Portugal, Spain, and southern Italy as well as in the outermost regions ( Map 2 ‑1 ). In Bulgaria, GDP per head was below 50% of the EU average in all regions, except in Yugozapaden, the capital city region.

The main concern throughout the chapter is to highlight the performance of the less developed regions against the more developed ones and of rural areas compared to cities.

This chapter examines recent trends in economic cohesion in regions and cities across the EU, as reflected in GDP per head and in the underlying developments in productivity and employment. It assesses the risk of regions falling into a ‘development trap’ and discusses the factors underlying regional competitiveness, including entrepreneurship, digitalisation and innovation. It also presents an aggregate indicator, the Regional Competitiveness Index, intended to summarise the different dimensions of competitiveness.

Regional economic convergence has stopped in the EU and divergence could become a threat to economic progress (Iammarino et. al., 2017) at a time when globalisation poses new challenges to economic cohesion. While the evidence suggests that the EU economy as a whole has benefited, and continues to benefit, from globalisation, these benefits are not automatically and evenly transmitted to all regions.

Figure 2‑9: Share of population living in regions which experienced very low growth in GDP per head, productivity and employment, 2001-2019, by initial level of GDP per head (index, 2000=100)

·Innovation in the EU remains highly concentrated in capital and other metropolitan regions. In north-western EU countries, good regional connections, high digital readiness, a skilled labour force and an attractive business environment have enabled surrounding regions to benefit from proximity to highly innovative ones. In southern and eastern EU countries, the most innovative regions are less strong and, accordingly, neighbouring regions reap little benefit. These patterns could lead to a widening research and innovation divide between EU regions.

·Transition regions, with a GDP per head between 75% and 100% of the EU average, seem stuck in a ‘development trap.’ Between 2001 and 2019, their growth in GDP per head was far below the EU average, and their productivity growth and employment creation was less than in other regions. Their manufacturing sectors are smaller than those in regions with a lower or higher GDP per head and their innovation and education systems and institutional quality are not strong enough to be competitive at the global level.

·The last two decades have witnessed a modernisation of the agricultural sector, evidenced by a long-term and ongoing increase in productivity and decrease in employment. These developments have been particularly pronounced in the less developed regions, which have experienced a sectoral restructuring of the economy.

·GDP per head in the less developed regions is converging towards the EU average through both faster productivity growth and increased employment. This trend is primarily driven by developments in regions in the eastern Member States whereas many less developed regions in the southern Member States are failing to catch up and experiencing decline and divergence.

·After a long period of convergence, since the crisis in 2008 regional disparities in GDP per head have stopped shrinking. Regional disparities in employment and unemployment rates increased dramatically after the economic crisis. Since 2013, they have started shrinking again, but remain significantly greater than in 2007.

·After the financial and economic crisis years and its aftermath, the EU economy is growing again, with growth being particularly high in low-income countries.

COMMUNICATION FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT, THE COUNCIL, THE EUROPEAN ECONOMIC AND SOCIAL COMMITTEE AND THE COMMITTEE OF THE REGIONS

Iammarino, S., Rodriguez-Pose, A., Storper, M. (2020), Falling into the Middle-Income Trap? A Study on the Risks for EU Regions to be Caught in a Middle-Income Trap. Luxembourg: Publications Office of the European Union, 2020.

European Commission (2021b) EC Communication on the 2030 Digital Compass: the European way for the Digital Decade. COM (2021) 118 final. European Commission and UN-HABITAT (2016), The State of European Cities 2016. Cities leading the way to a better future, Publications Office of the European Union: Luxembourg.

European Commission (2020) Science, research and innovation performance of the EU: A fair green and digital Europe. Publications Office of the European Union: Luxembourg. European Commission (2021a) EC Communication on a long-term vision for the EU’s rural areas – Towards stronger, connected, resilient and prosperous rural areas by 2040. COM (2021) 345 final.

Annoni, P., Dijkstra, L. (2017) Measuring and monitoring regional competitiveness in the European Union. In Huggins, R. and Thompson P. (Eds.): Handbook of Regions and Competitiveness - Contemporary Theories and Perspectives on Economic Development, Edward Elgar Publishing.

The 2019 RCI tracks the performance of all NUTS 2 regions in EU Member States. As in previous editions, the regions that are part of the same functional urban area are combined, which is the case for 6 capital functional urban areas, i.e. those of Vienna, Brussels, Prague, Berlin, Budapest and Amsterdam.

EU regions are divided into five development stages based on their average 2015-2017 GDP per head (in PPS terms) relative to the EU average. The weights attached to the three sub-indices used to calculate the overall RCI differ between stages of development ( Table 2 ‑8 ).

Following the same methodology as previous editions, the indicators are grouped into 11 dimensions of competitiveness capturing aspects that are relevant for productivity and long-term development. In turn, these 11 dimensions are organised into three sub-indices: Basic, Efficiency and Innovation. The Basic group includes five pillars: (1) Institutions, (2) Macroeconomic stability, (3) Infrastructure, (4) Health, and (5) Basic education, which are the key drivers for all economies. As a regional economy develops and its competitiveness increases, a more skilled labour force and a more efficient labour market come into play as part of the Efficiency group, which includes three pillars: (6) Higher education, training and lifelong learning, (7) Labour market efficiency and (8) Market size. At the most advanced stage of development, the Innovation group becomes more important, consisting of three pillars: (9) Technological readiness, (10) Business sophistication, and (11) Innovation. Indicator values are normalised as z-scores, i.e. by calculating the difference between the observed score and the mean score across regions and dividing by the standard deviation.

Data comes from a wide variety of sources, including the Quality of Government Index (University of Gothenburg), Worldwide Governance Indicators (World Bank), Global Competitiveness Index (World Economic Forum), various Eurostat indicators, and the Regional Innovation Scoreboard (DG GROW).

The 2019 edition of the RCI index is based on a set of 74 indicators selected from 84 candidate indicators (some indicators used in 2016 have been replaced). Most indicator values available span the period 2015-2017, some are for 2018, while a few go back to 2014.

The 2019 RCI tracks the performance of all NUTS 2 regions in EU Member States. As in previous editions, the regions that are part of the same functional urban area are combined, which is the case for 6 capital functional urban areas, i.e. those of Vienna, Brussels, Prague, Berlin, Budapest and Amsterdam.

EU regions are divided into five development stages based on their average 2015-2017 GDP per head (in PPS terms) relative to the EU average. The weights attached to the three sub-indices used to calculate the overall RCI differ between stages of development ( Table 2 ‑8 ).

Following the same methodology as previous editions, the indicators are grouped into 11 dimensions of competitiveness capturing aspects that are relevant for productivity and long-term development. In turn, these 11 dimensions are organised into three sub-indices: Basic, Efficiency and Innovation. The Basic group includes five pillars: (1) Institutions, (2) Macroeconomic stability, (3) Infrastructure, (4) Health, and (5) Basic education, which are the key drivers for all economies. As a regional economy develops and its competitiveness increases, a more skilled labour force and a more efficient labour market come into play as part of the Efficiency group, which includes three pillars: (6) Higher education, training and lifelong learning, (7) Labour market efficiency and (8) Market size. At the most advanced stage of development, the Innovation group becomes more important, consisting of three pillars: (9) Technological readiness, (10) Business sophistication, and (11) Innovation. Indicator values are normalised as z-scores, i.e. by calculating the difference between the observed score and the mean score across regions and dividing by the standard deviation.

Data comes from a wide variety of sources, including the Quality of Government Index (University of Gothenburg), Worldwide Governance Indicators (World Bank), Global Competitiveness Index (World Economic Forum), various Eurostat indicators, and the Regional Innovation Scoreboard (DG GROW).

The 2019 edition of the RCI index is based on a set of 74 indicators selected from 84 candidate indicators (some indicators used in 2016 have been replaced). Most indicator values available span the period 2015-2017, some are for 2018, while a few go back to 2014.

In general, higher levels of GDP per head are associated with higher levels of competitiveness. However, this relationship is stronger at lower levels of GDP – among more prosperous regions there is more variation in competitiveness.

The gap between the capital city region and others is particularly wide in France, Spain, Portugal and many of the Eastern Member States. This can be a reason for concern as it puts pressure on the capital city region while possibly leaving resources under-used in other regions.

Capital city regions tend to be the most competitive, except in the Netherlands (where the capital city region is ranked second), Italy (where Lombardia is the most competitive region) and Germany ( Figure 2 ‑ 21 ).

In line with previous editions, the 2019 RCI shows a polycentric pattern with strong performance of most capital city regions and others with large cities, which benefit from agglomeration economies, better connectivity and high levels of human capital.

Regional competitiveness indicates the ability of a region to offer an attractive and sustainable environment for firms and residents to live and work in. Launched in 2010 and updated regularly since, the Regional Competitiveness Index (RCI) is designed to capture the different dimensions of competitiveness for EU NUTS 2 regions. It allows regions to monitor and assess their development over time as compared with other regions. The most recent edition of the RCI was published in 2019. It shows that more than 10 years after the crisis, there is still a clear north-west – south-east divide across the EU ( Map 2 ‑ 11 ).

(1) The single digital gateway refers to an initiative to create a single-point of access to the information, administrative procedures and assistance services that individuals and businesses need to become active in another EU country. By the end of 2023 at the latest, users will be able to perform a number of procedures in all EU member states without any physical paperwork, like registering a car or claiming pension benefits.

To help start-ups and scale-ups, and building on the Single Digital Gateway(1) and existing national and European contact points, the European Commission has set up the Enterprise Europe Network (EEN), which provides ‘Scale-up Advisors’ in all regions to provide advice to SMEs on relevant national and EU regulations, funding and partnering opportunities and how to participate in cross-border public procurement.

To boost investment opportunities from venture capital and make funding more accessible to small and innovative enterprises, the Commission in 2016 launched a pan-European Venture Capital Fund-of-Funds under the Start-Up and Scale-Up Initiative (COM(2016)733 final). This complements other financial instruments under the EU programme for the Competitiveness of Enterprises and SMEs (COSME) and Horizon 2020's Innovfin to facilitate SME access to guarantees, loans and equity capital through local financial institutions in the Member States.

Start-ups and scale-ups need capital. EU start-ups, however, have more difficulty in obtaining venture capital than their US counterparts. EU scale-ups have even more difficulty to grow and remain independent than US firms. An additional problem is that venture capital is usually concentrated in a few places, often in the capital city.

There is a significant positive relationship between the churn rate and average annual growth rate of real GDP in EU NUTS2 regions over the 2008-2018 period ( Figure 2 ‑20 ), primarily reflecting the fact that nearly all regions with a churn rates above 12% had above average GDP growth(i). Among regions with lower churn rates the relationship is weaker and, indeed, many of these had high growth.

The economic concept of creative destruction is described by Schumpeter (1942) as “the process of industrial mutation that continuously revolutionizes the economic structure from within, incessantly destroying the old one, incessantly creating a new one". As a concept it is studied as a possible driver of economic growth, often in an endogenous growth context (see e.g. Aghion and Howitt, 1992). In the literature on firm and employment dynamics, creative destruction is in many cases measured by the average of the rate of firm creation and the rate of firm destruction, also known as the business churn rate.

Data covers NACE sectors B-S (except K), apart from BE, CY, DE, EL, IE, LU, and SI, where they cover B-N (except K) and S95.

High growth enterprises play an important role in the economic growth of cities and regions through their contribution to productivity and innovation (Acs et al., 2008). In 2018, capital metro regions typically had the highest number of high growth firms per head. The only exceptions were Lisbon, Amsterdam, Rome, Paris and Vienna, but even there the number was still above the country average ( Figure 2 ‑ 19 ). In all Member States, the number was higher in metro regions than non-metro regions.

In 2018, the number of newly-created employer firms relative to population tended to be higher in capital metro regions in both more developed and less developed Member States, with birth rates in Budapest and Tallinn being particularly high ( Figure 2 ‑ 18 ). Paris, Rome and Madrid are exceptions, birth rates being lower than in other metro regions in the countries concerned. In many sectors, firms operating in metro regions tend to face more competition because of the larger market and so a greater risk of being forced out of business if they are uncompetitive (Melitz and Ottaviano, 2008; Combes et al., 2012). High death rates, therefore, often go with high birth rates, as in Budapest and Tallinn, though death rates tend to be lower than birth rates, particularly in metro regions.

New enterprise creation is one of the main drivers of economic development and employment creation. New firms can help to open new sectors and higher value-added markets, so contributing to the structural transformation of an economy (Dent et al., 2016). They may also help to increase competitiveness by pushing incumbent enterprises to become more efficient.

Employer Business Demography Statistics at regional level show where firms (with at least one employee) are located in the EU and their dynamics in terms of births, deaths and growth. This section examines indicators of the number of firms relative to population, employees per firm, firm birth rates (firms created relative to population), firm death rates (closures relative to population), and the proportion of ‘high growth’ firms (defined here as firms with at least 10 persons employed growing by over 10% a year over a three-year period).

Firms may locate in more urbanised areas to benefit from agglomeration economies, from ‘matching’, ‘sharing’ and ‘learning’ (Duranton and Puga, 2020). Cities tend to have larger labour markets, allowing better matching between labour demand and supply, and enable better sharing of inputs and infrastructure, while the fact that people work and live in close proximity facilitates learning from each other.

In 2018, the number of firms with at least one employee – termed ‘employer firms’ here – was largest relative to population in Greece, Cyprus, Luxembourg, Slovenia (for which only national data are available) and most parts of Hungary and Estonia ( Map 2 ‑10 ). This may reflect a relative absence of large firms. Although the number of firms varies greatly between regions within Member States, the national context appears to be an important factor. In most countries, the number of firms relative to population is highest in the capital metro regions, except for France, Italy, Austria, and Spain. This is in part because many firms, especially large ones, have their headquarters there. The headquarter function also contributes to the higher number of employees per firm in the capital metro region . In general, non-metro regions tend to have fewer employer firms per inhabitant than metro regions.

The results cover all firms who have invested in the last financial year (excluding “don’t know” and refused responses). The results concern replies to the survey question: “In the last financial year, how much did your business invest in each of the following with the intention of maintaining or increasing your company’s future earnings?”

Policy measures have helped to limit the immediate adverse impact of the pandemic on jobs. However, a higher share of firms expect the COVID-19 outbreak to lead to a decrease in employment in the longer term (19% in less developed and 14% in transition regions compared to 12% in more developed ones). Structural shifts towards a greener and more digital economy and innovation will be important to maintain competitiveness and support economic catch up also in less prosperous regions, to maintain and nurture quality employment opportunities in the longer-term. EIBIS analysis shows that the pandemic has negatively impacted on human capital formation, with fewer adults participating in training and schools being closed across the EU. What is more, school closures are likely to have accentuated regional disparities as less wealthy Member States closed schools for longer. This underscores the need to invest in human capital as part of recovery strategies to mitigate risks of rising territorial and social divergences, looking ahead.

A more challenging investment environment together with structural differences pre-dating the pandemic can hamper adjustment to the emerging recovery phase. Fewer firms in less developed and transition have reacted to the pandemic by becoming more digital, while many in more developed regions are pulling ahead.

Firms in less developed and transition regions operate in a more challenging environment and report obstacles to investment more often; they find considerably more often that their investment is hindered by uncertainty, energy costs, and access to transport infrastructure and finance.

EIBIS results show that cuts to investment activity triggered by COVID-19 came on top of lower initial investment activity, particularly in less developed regions. Here, 79% of firms undertake investment, compared to 85% in transition, and 87% in more developed regions. Firms in less developed and transition regions tend to be smaller and fewer to export compared to more developed regions. Firms’ investment activities in less developed and transition regions tend to be tilted towards tangibles; a lower share of firms targets investment towards research and development compared to peers in more developed regions, where more active innovators (firms that heavily invest in R&D) are located ( Figure 2 ‑17 ).

The European Investment Bank’s Investment Survey (EIBIS), an annual corporate survey that gathers insights on the investment landscape in the EU, helps shed light on the effects of the COVID-19 crisis on investment and how these link to regional cohesion. For this, firms’ responses are grouped depending on their location in less developed, transition, and more developed regions.

The pandemic has highlighted gaps among regions and societal groups. Firms across the EU were hit by the COVID-19 shock to different extents, depending on sectoral activities and their ability to adapt to the pandemic situation. The crisis accelerated structural economic and societal change, creating some risks for cohesion as firms are adjusting at different speeds to the emerging recovery phase, marked by a stronger emphasis on digitalisation.

These results confirm that digitalisation may further fuel the research and innovation divide, at least between Member States. Given the increasing importance of digital technologies for enterprises to remain competitive, this is a cause for concern from a cohesion perspective. Since technology take-up is an important driver of economic convergence, less developed Member States risk falling further behind rather than catching-up, if their businesses do not innovate by adopting digitalisation. Moderately developed Member States may also see their capacity to compete diminished if they fail to do likewise, so risking falling into, or remaining in, a development trap (as indicated in Section 2.3 above).

The full definitions of the five indicators are: (1) the maximum contracted download speed of the fastest fixed line internet connection of at least 100 Mb/s; (2) enterprises with business processes automatically linked to those of their suppliers and/or customers; (3) enterprises with ERP software package to share information between different functional areas; (4) enterprises with e-commerce sales of at least 1% turnover; (5) enterprises purchasing at least 1% of the total online.

A similar pattern is seen for the take-up of e-commerce and e-business technologies ( Figure 2 ‑16 ). A sufficiently fast internet connection is required for such take-up. On average, some 46% of enterprises in the EU have broadband with a speed at least 100 Mb/s, but the figure is smaller in less developed Member States. Businesses in less developed Member States also lag behind in terms of the take-up of two specific e-business solutions, namely the use of business processes which are automatically linked to those of their suppliers or customers and the use of ERP (Enterprise Resource Planning) software to share information between different functional areas. The same is the case for e-commerce sales and online purchases. Both the share of enterprises with e-commerce sales of at least 1% of turnover and the share with online purchase of at least 1% of the total are smaller in less developed Member States, although the difference with other Member States is larger for the latter share

The take-up of digital technologies in the EU masks pronounced differences between Member States. For each of the three technologies, businesses in less developed countries lag behind, the take-up being highest in highly developed Member States.

The take-up of Cloud Computing in 2020 was greater than for the other two technologies ( Figure 2 ‑ 15 ), and the share of enterprises using it was twice as large as in 2014, a rate of increase, which, if it continues, will enable the 2030 target to be achieved. The take-up of big data and AI remains much smaller, which might be a result of these being newer and possibly less generally applicable from a business perspective.

Digital technologies have the potential to boost more inclusive and sustainable growth by spurring innovation, generating efficiencies and improving services. The current Commission has put the green and digital transition, the so-called ‘twin transition’, on top of the political agenda as the two trends that will shape Europe and its future. A goal of the EU is to boost the digital transformation of businesses by encouraging the take-up of three digital technologies: cloud computing services, use of big data and Artificial Intelligence (AI). The objective is that 75% of European enterprises will have taken these up by 2030.

Although challenges remain, new practices in public administration seem to have emerged at national, regional and local level. In particular, recent studies, based on policy-maker perceptions and case-studies (Hegyi et al., 2021; Guzzo and Gianelle, 2021), suggest that the Smart Specialisation experience has improved coordination and strengthened the network of relations between regional and local actors, as well as making the decision-making process and the governance of innovation policy more inclusive. It seems also to have helped reorganise and/or establish coordination bodies, platforms, thematic working groups and clusters. Nevertheless, the effectiveness of coordination between the public and private sectors and within public authorities remains an issue in several regions. More efforts are needed in the future in this regard, along with strengthening the skills and resources to perform policy functions. A clear and, if possible, dedicated structure of governance has proved to be important in this respect.

Strategies do not necessarily match the current economic structure as reflected in the sectoral division of employment, but they more often prioritise sectors in transformation, as measured by growth rates of employment. Smart Specialisation eligibility criteria seem to have been generally well applied in selecting projects and the resulting ERDF investments in research and innovation largely match the priority areas selected.

A recent study (Prognos and CSIL, 2021) shows that in most regions, the prioritisation of investment was based on a broad and inclusive ‘entrepreneurial discovery process’, which in most cases was specifically set up for formulating the Smart Specialisation strategy. About half of the 180 strategies, as well as about half of the ERDF funding available for these, concerned projects in the Agrofood & Bioeconomy (21%), Health & Life Sciences (15%) or ICT & Industry 4.0 (15%) sectors. Although the extent of prioritisation differs between the regions, there is evidence that the selected priorities closely reflect the scientific and technological profile of regions and public and private sector strengths.

Although it is still too early to assess the impact of Smart Specialisation on innovation, jobs and productivity, there is already some evidence of how the policy has been implemented on the ground and its effect on policy making.

Smart Specialisation was an integral part of Cohesion Policy in the 2014-2020 period. A total of 180 Smart Specialisation strategies were formulated in this period, with ERDF investment of over EUR 40 billion (EUR 68 billion including national co-financing).

Smart Specialisation strategies were introduced in 2014-2020 as an ex ante condition for all investment priorities under Thematic Objective 1 of the ERDF. A distinct feature is that Member States or regions need to identify priorities for investment through an ‘entrepreneurial discovery process’, involving key innovation stakeholders, business, and all actual or potential innovation actors that may possess crucial knowledge about new activities to establish in the country or region..

In practical terms, the Smart Specialisation approach concentrates resources into carefully defined “priority areas”. These priority areas can be framed in terms of knowledge fields or activities (not only science-based but also social, cultural, and creative ones) or sub-systems within an economic sector or cutting across sectors. They can also correspond to specific market niches, clusters, technologies, or applications of technologies to specific societal and environmental challenges. These priority areas should at the same time be in line with the region’s existing assets and be able to take advantage of innovation opportunities.

Conceived in the 2014-2020 programming period, Smart Specialisation strategies are defined by Regulation (EU) 1301/2013 as “the national or regional innovation strategies which set priorities in order to build competitive advantage by developing and matching research and innovation own strengths to business needs in order to address emerging opportunities and market developments in a coherent manner, while avoiding duplication and fragmentation of efforts.”.

Smart Specialisation is a place-based approach to the governance of innovation policy that focuses investment in research and innovation on selected areas of activity, identified through a wide and inclusive process to mobilise the local knowledge of relevant stakeholders, including businesses, public bodies, research organisations and civil society.

In general, the RIS confirms the wide diversity of EU regions in terms of innovation performance, so highlighting the fact that innovation has a strong regional dimension. Because of this, measures supporting innovation, including Cohesion policy programmes, need to take explicit account of the regional or local context when devising the kind of support to provide. As it is inherently place-based, the Smart Specialisation approach helps in this regard.

Note: In cases where the RIS score is only available at NUTS1 level, it is assumed that the same score applies to the latter’s constituent NUTS2 regions. Calculations for both years are based on 2021 population data and level of development classification.

There is a close relationship between the level of development of regions and the innovation score ( Figure 2 ‑ 14 ). In 2021 about 70% of the population of less developed regions live in an ‘emerging innovator’ region, which is twice as much as in 2016. This indicates that a large number of less developed regions that used to be moderate innovators have become emerging innovators. Furthermore, none of them live in a ‘strong’ or a ‘leader innovator’ region. Accordingly, during the last five years, the less developed regions have fallen further behind in terms of innovation, rather than catching up with the other regions. At the other end of the spectrum, ‘leader innovators’ are almost exclusively in the group of more developed regions, with only 2% of the population in transition regions living in a region in this category in 2021. The majority of ‘strong innovators’ are also in the more developed regional group, with 84% of the population of these regions in 2021 living either in a ‘strong’ or a ‘leader innovator’ region, up from 70% in 2016.

Indicator values are normalised by using the min-max procedure, i.e. the difference between the observed score and the maximum score across all regions is calculated and then divided by the range between the minimum and the maximum scores across all regions. The overall RIS score is calculated as the unweighted average of the indicator scores. The RIS then classifies regions into four innovation performance groups based on their overall RIS score relative to the EU average: ‘leader innovators (26 EU regions), strong innovators (55 EU regions), moderate innovators (69 EU regions), and emerging innovators (65 EU regions). A more detailed breakdown of these performance groups is obtained by splitting each group into a top third, middle third, and bottom third.

The 2021 edition of the Regional Innovation Scoreboard (RIS) provides a comparative assessment of innovation systems across regions. It is based on data for 21 of the indicators used in the European Innovation Scoreboard. This set of indicators covers higher education, scientific publications, ICT skills, R&D expenditure, business innovation, and patenting. Data come from a variety of sources including Eurostat, SCOPUS (Science-Metrix), the Community Innovation Survey (Eurostat and National Statistical Offices) and the European Union Intellectual Property Office (EUIPO).

The most innovative regions in the EU by this measure are Oberbayern (Germany), Hovedstaden (Denmark), Etelä-Suomi (Finland) and Stockholm ( Map 2 ‑ 9 ). Despite some regional variation within countries, the ranking of regions largely matches that of Member States, suggesting that indicator values at the regional level are affected by national characteristics. Most regional ‘innovation leaders’ are in countries which are also identified as ‘innovation leaders’ or as ‘strong innovators’, and almost all of the regional ‘moderate ‘and ‘modest’ innovators are in countries categorised in the same way. However, regional ‘pockets of excellence’ are evident in some ‘moderate innovator’ countries, including capital city regions in Czechia, Spain, and Lithuania as well as País Vasco in Spain, while some regions in ‘strong innovation’ countries lag behind.

The Regional Innovation Scoreboard (RIS) 2021 highlights the key role innovation plays in regional development. The RIS, an extension of the European Innovation Scoreboard (EIS), assesses the innovation performance of regions on the basis of a subset of the indicators included in the EIS. In 2021, it covers 215 regions in the EU, plus 30 regions in Norway, Serbia, Switzerland and the UK.

In 2019, expenditure on R&D relative to GDP exceeded the Europe 2020 target of 3% only in a small number of NUTS 2 regions, accounting for just 12% of EU population ( Table 2 ‑7 ). These are all more developed regions in the north-west of the EU, except Dresden (Germany) which is a transition region. None of the less developed regions met the 3% target, with expenditure on average over 2 pp below the target.

In general, regions with the highest R&D expenditure tend to be the most developed and often include capital cities (Belgium and Germany are notable exceptions) ( Figure 2 ‑ 13 ). Of the 20 regions with the highest expenditure, 19 are more developed with GDP per head above the EU average, while two third of the 50 regions with the lowest expenditure are less developed with GDP per head below 75% of the average.

R&D expenditure in the EU is highest in the north-western regions (at an average of 2.7% of GDP in 2019) and lowest in the east (1.3%) and south (1.4%). At the NUTS2 level, spending is highest, at over 7% of GDP, in Braunschweig and Stuttgart in Germany and Brabant Wallon in Belgium ( Map 2 ‑8 ).

Expenditure on R&D in the EU amounted to 2.2% of GDP in 2019 ( Figure 2 ‑ 11 ) and increased only marginally over the previous two decades (from 1.8% of GDP in 2001). The expenditure rate increased in all Member States, except for Sweden and Finland, where it had already reached a high level in 2001, and Luxembourg. Despite the overall increase, in most Member States the expenditure rates for the most part remain well below those in other highly developed economies, especially Japan (where expenditure was 3.2% of GDP in 2019) or the US (where it was 3.1%). There is also no evidence of convergence in rates within the EU, countries with comparatively low R&D expenditure in 2001 having the smallest increase in spending over the 2001-2019 period, suggesting a widening research and innovation divide between Member States.

A widely used indicator of innovation capacity, rather than performance, is expenditure on R&D relative to GDP, which is a measure of input into the innovation process, or the effort made, rather than of output. As in the case of patents, however, R&D expenditure is likely to underestimate innovation activity, particularly in sectors outside industry where non-technological and non-research-based innovation is common.

Source: Eurostat. DG-REGIO elaboration. The GINI coefficient is not provided for countries with only one or two NUTS-2 regions.

Leading industrial regions in Europe follow patterns and hierarchies symmetric to those of capital regions. Higher levels of both inward and outward FDI characterise advanced regions in the Forward GVC integration economies such as Bayern, Baden-Württemberg, Hessen, Nordrhein-Westfalen, Niedersachsen and Rheinland-Pfalz (Germany), Zuid-Holland and Noord-Holland (the Netherlands), Sydsverige (Sweden), and Pomorskie and Malopolskie in Poland. Similarly, some key industrial regions in the Low GVC integration countries display relatively high levels of both inward and outward FDI: Piemonte (Italy), Cataluña, País Vasco, Galicia and Andalucia (Spain). Flanders (Belgium), in the High GVC integration category, follows similar patterns, while industrial eastern EU regions in the Backward GVC integration group mostly show internationalisation profiles skewed towards inward FDI.

The Forward GVC integration group comprises the most innovative countries in terms of R&D expenditures (as well as patents), Poland and Romania being exceptions. Within this group there is a relatively high inter-regional dispersion of GDP as measured by the Gini coefficient ( Figure 2 ‑11 ). Conversely, Low GVC integration economies show low values of R&D (and patents), but also have big economic disparities. The High GVC integration countries show varying economic disparities, while Backward GVC integration countries show low shares of R&D expenditure (except Denmark) and lower economic disparities.

Two measures of GVC participation can be distinguished: (a) Backward Linkages: share of foreign value-added in the total exports of a country; and (b) Forward Linkages: domestic value-added embodied in exports of intermediates that are further re-exported to third countries, expressed as a ratio of gross exports. By looking at the relative position of each country with respect to the EU average, it is possible to identify four broad groups of economies:

Technological change coupled with the intensification of global value chains (GVCs) have spurred the need to place national and regional economic development and innovation policy in an open and interdependent framework. Multinational enterprises (MNEs), by carrying out different forms of investment abroad, are considered key actors behind connectivity and global economic integration of countries and regions worldwide, while also being critical players in international trade flows. Often described as “two sides of the same coin”, (Krugman, 2007), trade and investment seem to be intertwined in a more complex manner within GVCs (OECD, 2018 p. 31). In fact, trade flows can be equity led or non-equity led. The former involves networks of foreign affiliates established via foreign direct investment (FDI), which are highly engaged in GVCs (e.g. Altomonte et al., 2012) while non-equity-led trade involves more contractual partners and arm’s length external suppliers (Taglioni and Winkler, 2014). As such, trade in GVCs and FDI are complementary phenomena that need be taken simultaneously into account when trying to capture the geographical and functional dimension of global connectivity.

Metropolitan areas tend to offer an environment that is particularly conducive to the development of new ideas, products and processes. A vast literature explains the reasons for this – the presence of a creative and skilled work force, specialised clusters of economic activity, universities and research institutes. There are clear differences in patenting activity between metropolitan (metro) regions (around 167 applications per million inhabitants) and non-metro regions (around 58 per million inhabitants) ( Figure 2 ‑ 10 ). In quite a few metro regions, however, applications are less than in non-metro regions in the same country, indicating that not all metro regions offer a favourable innovation environment. Still, the distinct spatial pattern and concentration in metropolitan areas of patent applications are further indications of a research and innovation divide in the EU.

Over the period 2016-2017, 122 patent applications per million inhabitants were registered at the European Patent Office ( Map 2 ‑7 ). These show a distinct spatial pattern, regions with most applications being located mostly in the north-western Member States and in northern Italy. At the NUTS 3 level, Ludwigshafen in Germany, home to BASF, had the highest number (3 224 per million inhabitants in the period), followed by Erlangen, home to a major Siemens site (2 558) and Zuidoost-Noord-Brabant in the Netherlands (2 529), home to Philips. The degree of concentration suggests a regional innovation divide between the most advanced Member States and regions and the others.

Measuring innovation is widely recognised as challenging (OECD and Eurostat, 2018). The most commonly used indicator, the number of patent applications, gives only an approximate measure of the real innovation activity because it captures only innovations registered at the European Patent Office. These relate mainly to technological innovation in industry, while many if not most innovations in services, which are often intangible, remain unpatented. Nevertheless, though limited, patents provide a useful means of comparing performance of technological innovation across regions.

However, concern has risen about a growing research and innovation divide, linked to geographical concentration, both within Member States and across the EU, of the most innovative firms and research centres. While concentration can result in positive externalities of research and innovation, the core areas are very often located in more developed regions, so widening geographic disparities. This research and innovation divide may be further fuelled by the ongoing process of digitalisation.

Innovation is an important driver of long-run productivity growth and, as such, is a key factor in supporting the competitiveness of firms. This is especially important for firms in the EU, which increasingly have to compete with firms in developing regions of the world, such as in South East Asia, which benefit from cheaper labour, less labour market regulation, and fast technological catch-up (World Economic Forum, 2019). The capacity to innovate, and to take up innovation produced elsewhere, is of prime importance, especially since, unlike cost-reduction strategies, innovation is, in principle, without bounds, and so is central to sustaining growth over the long-term.

COMMUNICATION FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT, THE COUNCIL, THE EUROPEAN ECONOMIC AND SOCIAL COMMITTEE AND THE COMMITTEE OF THE REGIONS

Significant effort is still required regarding treatment. In the EU, around 7% of urban waste water failed to meet secondary treatment (biological) standards in 2018, while over 16% did not meet more stringent standards (removal of phosphorus and nitrogen). Almost 79% of regions in EU provide at least secondary treatment to 90% of their urban wastewater, but this share falls to 57% for more stringent treatment. Less than 30% of urban wastewater receives tertiary treatment in Croatian regions, some regions in Italy, Romania and Spain and in a number of French and Portuguese outermost regions ( Map 3 ‑9 ).

The considerable investment made in improving urban wastewater treatment has helped to reduce concentrations of organic matter and nutrients in surface waters. In 2018, more than 98% of urban wastewater was collected, though there are still a number of agglomerations where infrastructure needs to be built or improved. Only around 89% of wastewater was collected in Croatia and 85% in Cyprus, while in Romania, the figure was less than 80%, with just 57% being collected in Sud-Muntenia.

The Urban Waste Water Treatment Directive (UWWTD) has a key role in reducing water pollution in the EU by requiring Member States to collect and treat urban wastewater. Its objective is for all wastewater to be collected and suitably treated. Implementing the Directive requires significant investment in new infrastructure but also in the maintenance and extension of existing facilities.

Most EU citizens benefit from good water services (such as drinking water supply, and waste water collection and treatment) but access to those services is still lacking in a number regions, notably rural areas and less developed regions.

The condition of water bodies in the EU is a concern. Only 40 % of these are in good ecological state and many wetlands are in a poor condition . Even though various sources of pollution have been reduced over the past decade, the pressure from nutrients, hazardous substances and over-abstraction of water remains high. This implies that the objective set in the Water Framework Directive (2000/60/EC) of achieving good qualitative and quantitative status of all water bodies by 2015 is still not reached.

Essential for human health and well-being, water is also a key resource for agriculture, certain industries, energy production and transport. Water and wetland areas are also necessary for the provision of a number of ecosystem services (e.g. floodplains) and indispensable for preserving biodiversity as habitats for many species.

These EU initiatives have set targets to tackle environmental challenges via concerted action and systemic solutions. Their delivery will greatly depend on support from EU and national policy and funding instruments.

The EU has launched many policy initiatives to address these challenges, putting in place a broad range of legislation to reduce air, water and soil pollution. These have produced substantial benefits over recent decades. EU citizens enjoy some of the best water quality in the world and over 18% of the EU land area has been designated as protected for nature. As part of the European Green Deal, the European Commission adopted the EU Biodiversity Strategy 2030, which acknowledges nature restoration as a key contribution to both climate change mitigation and adaptation, the Farm to Fork Strategy, the Zero pollution action plan, the EU forest strategy and the EU Soil Strategy. The 8th Environmental Action Plan is designed to support the objectives of the European Green Deal and the transition towards a climate-neutral, resource-efficient and regenerative economy while improving the status of ecosystems.

As pointed by a series of recent scientific reports from the EEA, IPCC, IPBES, IRP and UN Environment, current trends in production and consumption are fundamentally unsustainable.

The EU faces unprecedented challenges of environmental sustainability, notably from accelerating biodiversity loss, degradation of ecosystem services, depletion of scarce resources and various forms of pollution, with the associated risk to human health and well-being.

In order to limit exposure and the increase of fatalities linked to extreme heat, a wide range of measures can be taken, including improved design and insulation of houses, schools and hospitals, education or early warning systems. This risk also needs to be taken into consideration in urban planning in order to minimise the urban heat island effect. In that perspective, urban green infrastructure can play an important role, notably by increasing tree and vegetative cover, installing green or reflecting roofs, or using cool pavements (see section 3.5).

The exposure of the population to the risk of extreme temperature considerably varies across EU Member States and regions. Risks of being exposed to extreme heat should increase in southern Europe while milder winters could reduce significantly exposure to extreme cold, nearly 10-fold with 3°C global average warming by the end of this century ( Map 3 ‑8 ). Heatwaves, human exposure and fatalities are projected to increase everywhere in Europe but Cyprus, Greece, Malta and Spain could see a 40-fold increase in mortality from heatwaves if no adaptation and mitigation actions are taken.

Extreme heat events are projected to happen more frequently and become more intense with climate change. The number of people exposed to heatwaves in the EU is projected to grow from 10 million/year (average 1981-2010) to nearly 300 million/year in a scenario with 3°C global average warming by the end of this century. As a result, the number of fatalities from extreme heat could increase up to nearly 100,000 per year if no mitigation measures are taken, which is significantly higher than the current 2,750 annual deaths.

The buckling of railway lines is also likely to occur more frequently with global warming so increasing maintenance costs. The biggest increases (of up to 10% with global warming by 3°C) are projected for regions in Germany and southern Spain, because of stress-free temperatures being likely to be exceeded most often. Significant increases are also likely in regions in Belgium, France, Sweden, Finland, Poland and Czechia.

Most of the increased maintenance costs are on tertiary and rural roads, which are generally managed by local authorities. Since their road maintenance budgets already tend to be constrained, damages from climate change could be particularly problematic for them.

Road maintenance costs are also projected to rise in all EU regions as a result of more frequent spells of extreme heat. The most significantly affected countries in terms of additional cost to maintenance are Bulgaria, Poland, Greece, Ireland and Romania. Future risk can be alleviated by upgrading roads or doing more frequent maintenance.

Nearly all regions in the EU are expected to experience increasing flood damage to their infrastructure as a results of climate change, particularly those prone to flooding in north-western and Eastern Europe, where the damage could in some case be over 6 times the present damage with global warming of 3°C. For most southern regions, damage to transport infrastructure from floods is projected to increase less dramatically, but could still be over twice as high as today ( Map 3 ‑7 ).

As would be expected, flood risk is concentrated in areas prone to flooding with high-value infrastructure, such as motorways and electrified railways. Some 95% of potential flood damage comes from roads and railways, with airports and seaports accounting for only 4%. The estimated cost of potential damage to railways is particularly high, at almost twice that of roads, reflecting the much higher costs of reconstruction and their location in lower lying terrain.

A recent study estimates the direct effects of flooding and heatwaves (two of the most damaging climate-related hazards according to a 20 year review by the UN Office for Disaster Risk Reduction) on the transport network in the EU, covering the modes of roads, railways, airports, and seaports. For each hazard, the effect is estimated as the change in expected annual damage for global warming levels of 1.5, 2, and 3°C relative to 1981–2010.

The EU has an extensive transport network, with around 5 million km of paved roads, 0.5 million km of railways, over 2 400 airports, and almost 2 000 seaports, with a combined estimated value of around EUR 9 trillion. This is particularly susceptible to climate hazards and so is generally built to withstand the variations in temperature as indicated by historical observations, or according to regional standards of construction. However, rises in average temperatures or greater frequency of extreme weather events as a result of increased GHG emissions are likely to lead to increased economic losses.

Investments in green infrastructure can also provide an efficient mean to enhance EU coastal defences against sea level rise. In particular, protecting and restoring costal ecosystems such as seagrass meadows and coral reefs can buffer the impacts of storms and help to reduce coastal erosion while bringing simultaneous benefits for biodiversity and natural resources.

Aggregating the results for coastal sections to the country level shows the Netherlands to have the highest BCR under a high emissions scenario (18), followed by Greece (12), France and Belgium (11 for each). By contrast, the BCR is low – though still over 1 – in Bulgaria, Finland, Romania, Croatia and Malta (3 or less in each case).

When benefits and costs are aggregated across coastal sections of NUTS2 regions, the benefits to cost ratio (BCR) is highest in urban centres ( Map 3 ‑6 ). Adaptation brings large net economic benefits in the Ionian Islands (a BCR of 30 under a high emissions scenario), País Vasco (27), Aquitaine (16), Calabria (11.3), Basse-Normandie (14), Pays de la Loire (13), Puglia (11) and Alentejo (11).

The analysis suggests that the average increase in the height of co