Blogs

Is smart tourism something tourist destinations only talk about, or also really implement?

By Dejan Križaj, Miha Bratec, Peter Kopić and Tadej Rogelja, University of Primorska

The focus of the research is on the adoption and implementation of technological innovations to analyse the Smart Tourism projects implemented in Europe according to the stringent technological criteria of contemporary Smart Tourism definitions.

Smart Tourism followed in the footsteps of the earlier concept of sustainable tourism and quickly established itself as the reference adjective when discussing tourism in politics, economics, and academia. In the latter, the debate has been lively, and although there are many different conceptualizations, academics seem to agree that Smart Tourism is based on the use of novel technologies that improve the quality of visitor and local experiences, while enabling destinations to take steps towards achieving their sustainability goals. However, as it happened in the past with the term “sustainable”, the adjective “smart” seems to be heavily misused when describing the various transformations that tourist destinations and cities are currently facing. Mostly, it dominates the marketing discourse, with many destinations trying to use this “smart” concept because it gives them a competitive advantage over other tourist destinations based on uniqueness and differentiation.

Based on our study, the reality of developing smart solutions within these destinations is mostly still in its infancy. More specifically, we, in detail, analyse:

  1. What is the real content of the Smart Tourism projects currently implemented within Europe and supported by substantial EU (European Union) funding?
  2. What are the characteristics of the Smart Projects and what kind of technology solutions are used in them?
  3. Can we really see the rapid technological progress in tourism services that the marketers of Smart Destinations promise?
  4. What do the currently implemented projects tell us about the future of Smart Tourism and Smart Destinations?

Summary of key findings:

Our work differed from most methods used in other studies that rely on the construction of conceptual models, frameworks, or indicator systems based on the evaluation of Smart City or

Smart Tourism goals, statements, strategies, and initiatives. The presented study goes a step further and tries to understand which technological innovations exactly were adopted and how they contribute to projects’ smartness. In order to better distinguish between conventional and advanced, interconnected technology, we have placed a special focus on Smart Actionable attributes of the projects analyzed. From what we could perceive in the selected projects, four smart technology trends can be identified: 1) Connectivity and Big Data, 2) Connectivity and Intelligent Algorithms, 3) Big Data and 4) “smart” projects with mainly well-represented technology that does not exploit the Smart Actionable possibilities.

In our initial online resource search, we encountered the vast majority of projects that were touted as “smart” but did not address any of the newer aspects of ICT infrastructure, such as interconnectivity and interoperability of integrated technologies. They were therefore excluded from our study, leaving only 35 projects, which we analysed in detail and assigned to the four groups mentioned above. This confirms our preliminary findings that there is a lot of hype and little substance (e.g., smart washing) regarding Smart Tourism projects. This problem stems in part from the fact that there are different, everchanging definitions and meanings of the term Smart Tourism. Subsequently, different stakeholders and entities adopt different meanings and set different priorities based on their viewpoints and schools of thought.

See full paper: https://doi.org/10.3390/su131810279

Hindrances to Access to Housing in a Tourist City, pre- and post-COVID19: evidence from Barcelona

By Antonio Paolo Russo and Riccardo Valente, University Rovira i Virgili

This piece illustrates some of the early results from the study of Barcelona as an exemplary ‘overtouristed’ city in which access to affordable housing and its relationship with employment is at stake. Our insights seek to influence the debate about policy options for an inclusive port-pandemic recovery.

The SMARTDEST project (H2020 ref. 870753) focuses on forms of social exclusion emerging in the context of urban areas that are the hub of global mobilities, such as tourism.

Barcelona is one of the most celebrated examples of invention of a successful tourist city through urban planning, place marketing, cultural valorisation and innovative governance since the early 1990s; but also one that came to be subject to the highest level of tourism pressure, feeding a wide societal and political debate on social justice in the ‘overtouristed’ city. Besides, as a place increasingly dependent on tourism jobs and businesses (and especially so after the economic downturn of the 2008 financial crisis), Barcelona has been severely exposed to the next systemic crisis, that of the COVID-19 pandemic.

The key focus of the SMARTDEST case study in Barcelona is on housing affordability and its enmeshment with labour conditions in the tourist sector. The key assumption is that tourism growth produces benefits that are unevenly distributed across society and spatial scales, but it also entails social costs that affect long-term residents, for whom access to housing is becoming increasingly difficult, or tourism sector workers, that are more than others subject to precarious employment conditions and a high degree of ‘invisibility’ or informality.

Figure 1. Residential stability in the 73 neighbourhoods of Barcelona (2016-2019)

Our early results from a pre-pandemic analysis show how the progressive penetration of short-term rentals promoted via platforms like Airbnb is subtracting a sizable share of the housing stock from the long-term residential market. In our analysis, the spread of Airbnb accommodations during 2014-2019 period, as well as the levitation of housing prices and rental fees, were found to be associated with a reduction in the share of long-term residents (those who were living in the same neighbourhood for more than 5 years), an effect that is not significant in relation to the spread of the conventional accommodation supply. Discounting for other factors which may explain population change, we observe a high degree of residential instability in tourism-intensive neighbourhoods, with residents displaced to another neighbourhood or out of the city altogether (See Fig. 1).

We also looked at patterns of residential mobility among tourism workers between 2013 and 2019. Our analysis reveals that being employed in tourism-related sectors is associated with lower incomes and higher rates of precariousness compared to employments in non-tourism sectors. Such unfavourable labour conditions have a particular impact on female workers, that are more likely to be displaced out of Barcelona, while maintaining their main occupation in the city.

To conclude, affordable housing is a critical asset to ‘remain citizen’ in a tourist city like Barcelona, as is for many other cities that are studied in SMARTDEST. This seems to be increasingly a hindrance for vulnerable sectors of the population, and it is remarkable that the very model that feeds tourism growth also produces an engrossing share of precarious workers, those more likely to be affected by rising housing costs.

In the light of the above, pursuing the objective of reaching pre-COVID19 levels of tourism activity is likely to reproduce past exclusionary trends. If the global pressure on the housing market has only temporarily subsided (at the end of the summer season of 2021 evidence seems to point at a sharp reprise of the activity of short-term rentals), the situation of tourism workers and other vulnerable sectors has worsened substantially, because of high rates of unemployment. The post-pandemic future of Barcelona thus may a bleak one, in which social gaps are heightened and the very sense of social cohesion is at risk. In this sense, recovery efforts need to be based on a different approach to the planning and regulation of tourism mobilities and their local impacts, aligning with Sustainable Development Objectives like the reduction of social inequalities, which may imply steering away from a growth model which has shown all its limitations both in the pre-pandemic period and in its current developments.

Living apart together? Mobile professionals and long-term residents in Lisbon’s city centre

By Franz Buhr, Institute of Geography and Spatial Planning, University of Lisbon

In the last ten years, not only have tourist arrivals to Lisbon increased exponentially, but the city has also become a hotspot for other kinds of transient populations. Digital nomads, ‘expats’, lifestyle migrants, and other transnationally mobile professionals are increasingly present in the city’s social landscape. What are the impacts of these new temporary residents in the city’s dynamics?

Let us go for a tour around the neighbourhoods of Santos and São Bento in Lisbon. You will find centuries-old hilly streets, the tram tracks, tile façades, and… Nordic coffee shops, hip cocktail bars, and brunch eateries! Not long ago, these shops were either abandoned, derelict, or housed small family businesses such as traditional Portuguese bakeries or Cape Verdean restaurants. Now, these two neighbourhoods are probably the epicentre of a new kind of commercial dynamics attracting tourists and locals, but particularly appealing to digital nomads, ‘expats’, and other foreign residents whose purchase power is (more often than not) well above the Portuguese average.

During the most severe months of the COVID-19 pandemic, when there were virtually no tourists around, some of these specialty coffee shops, artisanal bakeries, and patisseries survived on the basis of their foreign-resident clientele. In one of our SMARTDEST interviews, the owner of a specialty coffee shop in the area argued that 95% of his customers were of foreign origin. “Lots of Germans, French, Americans… They come to Portugal but keep working for their countries and have a lot more economic capacity than those being paid Portuguese-level salaries” he stated.

Once considered ‘crossing points’ to the more touristic areas of the city, Santos and São Bento are now attracting their own visitors. The SMARTDEST team in Lisbon asked local residents if they also visited, bought, or ate at these new gourmet cafés and restaurants. Our preliminary results point to what one resident called ‘parallel worlds’: on the one side, traditional forms of commerce frequented by the local elderly population; on the other, new gourmet restaurants and trendy shops where one finds tourists, but mainly high-income foreign residents. Although some of these transient populations find short-term rentals within these same neighbourhoods, their consumption geographies seem to rarely intersect or interact with those of long-term residents.

Another research participant, mother of three children and living in the area for 25 years, said that “the ambiance feels very different now, because buildings have been renovated, trash is always collected, gardens look beautiful (…), and it’s nice to have that shop selling beautifully-made croissants, but they are super expensive and we won’t buy croissants every day. It’s all made for people in transit (de passagem)”.

Can these two ‘parallel worlds’ interact with each other? Do long-term residents feel excluded in some way? Are traditional forms of commerce and retail doomed to disappear? These are some of the critical questions to be discussed collectively at the future CityLab organised by the SMARTDEST project with local stakeholders and residents.

Opportunities and Concerns in Hosting World University Games: the case of Turin 2025

By Samantha Cenere and Loris Servillo from Politecnico di Torino

Big sporting events have been always considered important city branding strategies and opportunities to launch major urban requalification projects. The Universiadi constitutes a peculiar kind of event that merges these objectives with the specific goal to position a city on the global map of university students’ destination.

Big events represent unique occasions for those cities that aim at revitalising their economies and launching important requalification projects. Indeed, these events are considered able to promote the image of a city and attract visitors, trigger regeneration, accelerate the implementation of ongoing projects, and provide the financial support needed to construct new infrastructures. Albeit these events present a great variety in terms of size, typology, and impact, sporting events are usually considered the most fruitful ones for cities aiming to capitalise on both the organisation of the event and its legacies to implement their urban agendas. Olympic Games constitute indeed the prototypical example of mega events.

Despite their relatively small scale if compared to the Olympics, so-called Universiadi are a type of sporting events that cities compete to host. Invented in 1959, the Universiadi are the university students Olympic Games, an event that aims at supporting the encounter between Higher Education and sport.

Turin (one of SMARTDEST cases study) has recently won the bid to host the Winter Universiade 2025, thus replicating the success already obtained in 2007. This news was welcomed with great enthusiasm by a city that during the last 20 years has based a relevant part of its growth strategy on the imaginary of a ‘university city’.

As for other big events, hosting the Universiade will allow to attract high flows of people from abroad (both athletes and visitors) and generate positive impacts both on the local economy and in terms of urban development. According to the plans and the promises made by the institutions that took part in the implementation of the bid – namely, the two main Higher Education institutions of the city, the City, the local agency for university sports (CUS), and the regional agency deputy for the right to university education (EDISU) –, the Winter Universiade will bring to Turin around 3,000 athletes and attract around 10,000 visitors. Indeed, as explained by the President of CUS, local encouragement to the sporting culture and high-level sport facilities in particular represent crucial assets for an urban Higher Education system that aims at becoming increasingly attractive for international students. According to him, the latter have to be considered like tourists by a post-industrial city in search for a new identity.

But the most relevant and lasting effect is represented by the investments made on the construction of student accommodation facilities, in line with the capacity of other big and mega events to act as a means of realising relevant infrastructural change. The event will provide the city with almost 1,800 bed places, thanks to the creation of four athletes villages that, once the event will be closed, will be converted into student residences.

However, as for other big events, the Universiade raises various concerns by those segments of the local population that view as problematic those urban growth strategies that pivot on the attraction of mobile populations rather than on the provision of services to residents. Besides the criticism to the use of public fundings to host the event (40 million euros esteemed, of which 28 from the Government), the main concern regards the locations chosen to become athletes’ villages and then student residences. Indeed, according to some local opposition groups, the opening of these facilities in locations such as the former Maria Adelaide Hospital and a green public area in the neighbourhood Parella would result into the loss of public services for residents.

Measuring Cities´Smartness: Navigating through an Ocean of Indicators

By Josep Antoni Ivars Baidal from University of Alicante

Smart cities and smart destinations have become widely used buzzwords with different meanings and interests. Institutional self-proclamations of smart city / destination are so frequent that it is interesting to specify clearly what constitutes smartness and how it is measured. An undoubtedly complex but necessary task.

The pioneering work coordinated by R. Giffinger (2007) established the six basic characteristics of the smart city and specified them in 74 indicators to build the first European smart city ranking, focused on medium-sized cities (http://www.smart-cities.eu/download/smart_cities_final_report.pdf). Since this study to date, there have been countless initiatives to assess the level of smartness of cities. These initiatives are aimed at a variety of purposes: scientific work, rankings, indexes, standards or indicators systems integrated in urban/tourism management programs. These contributions recall the wide use of indicators since the 1990s to measure sustainability, a dimension that, on the other hand, being integrated in the smart city/destination concept, has generated specific analyzes around the best way to conceptualize and measure the relationship between sustainability and smartness.

The European Commission has supported different projects based on the evaluation of smart cities initiatives, such as Mapping Smart Cities in the EU (2014) (https://op.europa.eu/es/publication-detail/-/publication/78882e80-fc4a-4a86-9c39-2ad88ab89f9b) or CITYkeys (2017) (http://www.citykeys-project.eu/), aimed at the creation of smart city indicators that can function as Key Performance Indicators for tracking the progress towards city and project objectives. This approach is interesting for international comparison of smart city performance and for policy analysis leading to improved urban management. For a similar purpose, standards related to smart cities have been developed by the International Organisation for Standardisation (ISO). In particular, ISO 37122: 2019 (Sustainable cities and communities-Indicators for smart cities) in conjunction with ISO 37120: 2018 (Sustainable cities and communities – Indicators for city services and quality of life).

In a recent research paper, A. Sharifi (2020) (https://www.sciencedirect.com/science/article/abs/pii/S2210670719314404?via%3Dihub) has examined thirty-four smart city assessment schemes showing the prevalence of indexes of different nature, above all market-oriented as the Cities in Motion Index (https://citiesinmotion.iese.edu/indicecim/) or the Innovation Cities Index (https://www.innovation-cities.com/city-rankings-2021/), together with academic contributions, such as the Lisbon ranking for smart sustainable cities (https://www.sciencedirect.com/science/article/pii/S2210670718308138). These indicators are structured according to the typical dimensions of smart city: economy, people, governance, environment, mobility, living, and data; with logical variations based on the objectives and methodology used.

How is tourism and its urban implications reflected in these indicator systems? The analysis of these systems evidences a very low presence of direct tourism indicators, a logical consequence of systems that try to measure a complex reality in a holistic way. This marginal role of tourism indicators prevents the establishment of correlations or cause-effect relationships between tourism and its urban effects, fundamentally those related to processes of social exclusion, which are also under-represented in the evaluation schemes of smart cities. In this context, the SMARTDEST project is an opportunity to contribute to a better measurement of the relationships between urban smartness, tourism and other forms of mobility and social exclusion processes.

Can Airbnb be blamed for all housing issues? – The case of Ljubljana

By Tadej Rogelja, Miha Bratec, Dejan Križaj from University of Primorska

 

Slovenia is among the EU countries with the highest rate of housing shortage. We have focused on the capital Ljubljana and examined the causes that have led to such a situation. The reason on the one hand is the relatively old and poorly maintained housing stock and, on the other hand, the short-term-rental platform Airbnb. But what did the COVID-19 pandemic reveal?

 

Slovenia is among the EU countries with the highest rate of housing shortage. We have focused on the capital – Ljubljana and examined the causes that have led to such a situation. The reason for such a situation is, on the one hand, the relatively old and poorly maintained housing stock and, on the other hand, the sharing platform Airbnb.

 

The Slovenian capital of Ljubljana, with a population of around 300.000 is one of the smallest capitals in Europe and arguably on Europe’s most sustainable destinations, experiencing tremendous growth in terms of visitor numbers and press recognition within the last 10 years. The city is located in the Osrednjeslovenska Region (Central Slovenia) and it is the strongest area in terms of economic development, and is the administrative, economic, cultural, and scientific centre of the country. On the other hand, Slovenia is also among EU countries with the highest housing deprivation rates. In 2018, more than a fifth of its population lived in poor housing conditions. One of the reasons for the high housing deprivation rate is the relatively old and poorly maintained housing stock (IMAD, 2020). The state also abolished systemic sources of funding, did not develop new supply institutions and hindered the construction of public housing stock. National policies are also reflected in municipal policy, which has neglected the housing topic for the last 30 years since Slovenia’s independence. This played a major role in the housing policy when the socialist real estate market was privatized, and inhabitants had the right to purchase the apartments in which they were living for a price way below the market value. Due to this policy, 80% of Slovenians live in their own properties today and only 8% in rental flats. Consequently, the share of public housing in Ljubljana owned by the municipality fell from 42% (42,000 dwellings) in 1992 to 3% (4200) as of 2019 (IŠSP & FDV 2019). With the stagnation of the housing policy, Ljubljana has reached a point where few people can afford to buy an apartment while renting one equally puts a comparatively high burden on one’s disposable income.

 

Let us now add Airbnb to the whole story. Historically, Ljubljana has not been a prime tourist destination, but between 2014 and 2018, tourist demand increased significantly, leading to a sudden shortage of suitable accommodation. Peer-to-peer accommodation was a perfect solution at this time. The market was flooded with tourists so quickly that the government did not have time to take regulatory measures to prevent externalities. As a result, locals today experience very high prices and cannot afford long-term rentals. According to Milič (2021) from Capital Genetics which focuses on corporate finance, capital growth, valuation of business and real estate in Slovenia and other countries in Southeast Europe, prices have gone crazy. Currently, the average price of a used apartment in Ljubljana is already over € 3100 per square meter. Second-hand housing prices have risen by 50% in the last five years. Official statistics did not capture the additional supply of beds because many locals did not report their short-term rental activities. Figure 1 illustrates the large discrepancy between the number of beds in private accommodation reported by the official statistics of the Statistical Office of the Republic of Slovenia and the number of beds listed on Airbnb according to AirDNA. Thus, in 2018, approximately 2,038 beds were not registered on Airbnb and so failed to pay taxes from their commercial activities (Dolnicar, 2021).

Figure 1: Number of arrivals and overnight stays in Ljubljana (Source: Statistical Office of the Republic of Slovenia, 2019)

 

In addition to that, many Ljubljana residents reported the lack and high price of parking spaces as negative consequences of tourism. On the other hand, according to Airbnb˙s data, most apartments listed offered parking, which can be quickly combined into a meaningful whole. Moreover, a more detailed investigation revealed that Ljubljana’s accommodation listings on Airbnb often recommend that tourists use the public parking spaces near the property, which puts a significant strain on the public infrastructure and results in locals not finding parking spaces in front of their homes (Dolnicar, 2021).

 

But can Airbnb be so easily blamed for most of the housing issues in Ljubljana? Though the discourse went into such a direction, the pandemics showed a rather different picture. When tourism and especially short-term rentals plummeted in 2020, this only led to short term effects such as more offers on the long-term rental market, yet the prices for both housing rentals and purchase kept growing and reached record numbers by spring of 2021. All these leads to indicate that the housing issues in Ljubljana are much more complex and the growth of tourism within the last decade and Airbnb-related short-term rentals only played a minor role in sky-rocketing real estate prices. The real reasons behind them need to be further explored, but most likely have to deal with failed restructuring of the sector following the abortion of socialism and inefficent state and local housing policy formulation.

Forecasting Tourist Mobility and Overcrowding thanks to Agent Based Models

By Itzhak Omer and Amit Birenboim, Tel Aviv University

Overcrowding is a main negative externality that is associated with tourism. However, data on street level crowding is usually not available for studying this phenomenon. Using Agent based modelling, we can generate synthetic data of tourist mobility that forecasts street level tourist congestion.

Agent-based models (ABM) enable reference to various individuals’ travel behaviour attributes and to the simultaneous effect of the street network structure and land uses on movement flows. In the Jerusalem case study, ABM is used to represent the different movement patterns of local residents and tourists, and the exposure / interaction between them at the street level. The ABM simulation is based on the following ‘basic’ attributes of agents’ travel behaviour that were found most relevant in previous studies:

 

(i) The attraction/obstruction level of land uses as a destination or as intermediate paths, with distinction between agent types (local residents versus tourists) in this respect;
(ii) Scale/radius for movement and sensitivity to distance: represents the maximal distance available for movement from origin to destination according to destination types and preferences of nearby destinations within this radius;
(iii) Personal status: represents socio-demographic properties, such as age and gender;(iv) Distance type: three types of agents were defined: metric, topological, and angular. Each agent type (local resident and tourist) chooses the relevant shortest path – in terms of metric, topological (the number of turns or direction changes), or angular (cumulative angular change), respectively – between origin-destination pairs.
The ABM was designed with the NetLogo (ver.5.3.1) environment and is associated to geographical layers within ArcGIS software (i.e., street-segment, land uses). Data model is enriched by quantitative data that was collected at the sub urban level such as socio-demographics at the census tract level.
In later stages of the project, the ABM is intended to be used as a decision supporting tool. Using the ABM we will generate forecasted /simulated movement patterns of local residents and tourists according to various scenarios that are related to tourist behavior and tourist-oriented plans or expected trends. Such use of the ABM may help forecasting the implications of changes in the volume and spatial distribution of hotel/Airbnb rooms on local residents-tourists exposure at the street level. The model will also assist to evaluate the implications of urban and infrastructure changes on car usage and walking behavior of various types of agents (e.g., local population, tourists) under different assumptions of technology adoption levels and pricing. Outputs will include, among other things, indices of inclusion and inequality.

Touristic labour in Europe: how to compare it across different European regions

By Niklas Pernhaupt, Lukas AlexanderYuri Kazepov and Elisabetta Mocca from University of Vienna

As one of 12 research partners we are busy to contribute to the success of the SmartDest project.

The core research team at the University of Vienna consists of four people: Prof. Yuri Kazepov, Elisabetta Mocca PhD, Niklas Pernhaupt MA and Lukas Alexander MA. In SmartDest we are leading the empirical work of WP3 and provide transversal support to the case study leaders in task 3.1, 3.3 and 4.3. Moreover, we participate in various tasks in WP2, WP4, and WP5. We also planned a steering group meeting for September 2020 in Vienna, which had to be called off due to travel restrictions.

The previous few months we spent on refining our output of WP 2. More concretely, we conducted a systematic literature review on tourism typologies, where we analysed over 350 scientific publications. The results are going to be presented at the ATLAS Conference on the 3rd of June 2021 in Rotterdam. In addition to our review, we are trying to find a way to compare the quality of touristic labour across different regions. To do so, we first attempt to find a comparable approximation of tourism work. Different destinations come with different forms of tourism work. We are trying to find occupations that are likely common to most regional destinations throughout Europe. After we find our approximation of tourism work, we will look at different dimensions of job quality in the tourism sector. Which regions are characterised by contractual insecurity? Which regions show job insecurity in the sense of persons having to work multiple jobs, persons wishing to work more hours, and persons who are looking for another job? Which regions exhibit relatively bad working conditions? These three dimensions will then be summed up to an index of formal touristic labour quality and weighted by the socio-political context in which they are embedded. Here, we will explore which regions offer ‘flexicurity’ – e.g., a safety net to protect workers against the negative aspects of flexible labour.

 

Trends of European Regional Tourism: 2008 to 2018

Author: Anna Bornioli, Erasmus UPT

The SMARTDEST report published in September 2020 is a preliminary exploration of the dimension of tourism and related mobilities at regional level across the EU territory in the period 2008-2018 and of regional trends of social unbalances across the EU. To these aims, a series of indicators to describe key dimensions of mobilities and social unbalances were selected by the researchers across multiple sources (including Eurostat, Labor Force Survey, AirDNA). These have been mapped on the EU geography and discussed, and are being collected in a work-in-progress database, organised at regional (NUTS2) level, and available at https://doi.org/10.5281/zenodo.4058290 in its preliminary version. Although the analysis does not include 2020 trends, thus not focusing on the impact of Covid-19 pandemic on mobilities and social unbalances, several lessons relevant to the post-Covid-19 recovery can be learnt.

A collection of 53 maps was produced. The regional (NUTS2) scale of this analysis was dictated by data availability, as the majority of the related statistics are only published or reliable at this scale. As a consequence, this collection of maps gives a preliminary overview of the geographical trends, without focusing on territorial nuances. Nevertheless, urban trends of tourism and mobilities can already be observed, since NUTS2 regions including large cities generally correspond with their metropolitan dimension.

Here we summarise the main tourism trends looking at tourism stays and their evolution over time, tourism pressure and stress, and the international character of destinations.

 

Tourism stays in 2018

Tourism mobilities in absolute numbers in 2018 (Figure 1) were more intense in coastal regions, particularly in the (Western) Mediterranean arc; in mountain regions, especially in the Alpine arc; in highly urbanised regions, especially capital city regions. Trends of Short-Term Rentals (STR), a form of tourist accommodation that is not fully accounted for in official statistics, mirror the ‘official’ statistics on arrivals (Figure 2). STR stays are highest in coastal regions and urban regions, especially in the south of Europe, France, the UK, Iceland, and Denmark. These figures are possibly also influenced by national regimes of regulation.

 

 

Growth of tourism 2008-2018

There appears a clear outlook of sustained growth of tourism mobilities ‘landing’ on

European regions in the period 2008-2018, with very few exceptions (Figure 3). In absolute terms, it is especially Southern and Mediterranean regions, islands, and

capital regions that have seen the largest growth of arrivals at tourism accommodation establishments. The only regions that had a decrease of the number of tourism stays are the regions in light blue.

In relative terms, the picture presented offers a further piece of the puzzle (Figure 4): while the most mature destination regions continued to grow, there is also a process of ‘catching up’ of regions that were less attractive and that grew substantially in the 10-year period. The regions that are strongly above the European average are mostly located in Eastern Europe, the Coastal region of Croatia, Portugal, the UK, Benelux, and Iceland. Cities and urbanized (coastal) regions have also had a sustained dynamic of growth over the 2008-2018 decade. Among the capital regions, Amsterdam, Berlin, Brussels, Lisbon stand out.

 

Tourism stress and pressure

Tourism stress and pressure (overnight stays in relation to space or to the resident population respectively) have also increased over the reference period almost anywhere in Europe. In 2018, the number of overnight stays in tourist accommodation establishments in relation to the resident population (Figure 5) was especially high the East Alpine arc, coastal Croatia, Mediterranean island regions, capital city regions, coastal regions, and some of the less urbanised coastal and rural regions in Northern Europe.

 

International markets and potential vulnerability

The analysis also highlighted that some regions tend to be more reliant on international markets, having a large share of international visitors. These are large and smaller islands, border regions, but also Croatia, Baltic and Italian regions, among others (Figure 6). In addition, Iceland, the UK, Portugal, and Greece have become more reliant on international tourists since 2008.

These regions, being more exposed to international fluctuations, might be less ‘resilient’ to international crises such as the current covid-19 pandemic.

 

Conclusions

We identified the regions in Europe where pre-covid tourism was especially strong and where it was growing the most. These tend to be Mediterranean and Southern Europe regions, islands, and urban and capital regions. Subsequent analyses took a step forward and identified four typologies that illustrate different forms and degrees of attractiveness for tourists and related mobilities, based on the collection of maps presented here [link to https://smartdest.eu/a-typology-of-eu-tourist-regions-facing-social-inclusion-issues/ ].

typology of tourist regions

A typology of EU tourist regions facing social inclusion issues

By Antonio Paolo Russo, from Universitat Rovira i Virgili

As a first stage of the research approach of SMARTDEST, we have constructed a typology of European regions that illustrate different forms and degrees of attractiveness for tourists and related mobilities, and matched with a wide range of social indicators showcasing trends of social exclusion. The spatial patterns devised provide an interesting canvas to further examine how territorial structures, geographical specificities and policy regimes may play a role in explaining these variations, and inform postCOVID recovery towards policy reforms that bring forwards socially resilient tourist cities and regions.

As a first stage of the research approach of SMARTDEST, we have constructed a typology of European regions that illustrate different forms and degrees of attractiveness for tourists and related mobilities. This typology is then matched with a wide range of social indicators showcasing trends of social exclusion. The objective of this piece of research is to identify key inclusion challenges for groups of regions, having similar profiles in terms of their capacity and evolution to attract mobile populations. The spatial patterns devised provide an interesting canvas to further examine how territorial structures, geographical specificities and policy regimes may play a role in explaining these variations. This analysis refers to a context of steady intensification of tourism and international mobility that has characterised the last decades, to come to an abrupt halt with the sanitary emergency of COVID-19 in 2020, with an expected long tail of disruptions in global and local mobility systems. Looking into the near past goes in the way of understanding how tourism mobilities could have become enmeshed with social inequalities; the hindrances provoked by COVID-19 have been opening new relevant avenues of social exclusion, which the recent literature claims to be overlapping and heightening, and not substituting, pre-existing ones. Our analysis should therefore be informing the process of recovery, and underline the key policy challenges that are at stake in the debate as to whether tourism should bounce back to ‘business as usual’ and pre-COVID trends once the emergency is over, or whether this could be an important opportunity for reforms that bring forward social resilience in the face of the transformative and exclusionary power of tourism mobilities on places.

The indicators used to obtain this basic regional typology were selected from a wide range of measures of tourism and related mobilities considered in preliminary tasks of the SMARTDEST project. These include absolute and relative measures of tourism movement in space and in relation to the resident population (intensity and pressure indexes), for international and domestic markets. Whenever possible and relevant, these indicators have been stratified for areas that have different degrees of urbanisation. We also considered net migration rates for age groups, which the literature relates with different motivations for displacement; the mobility of Erasmus students; and a measure of the penetration of Airbnb supply in relation to the total population which is a proxy of the attractiveness of regions for visitors using this kind of platform-mediated  accommodation structures (generally not accounted for in official tourism movement statistics). All these indicators are calculated in stocks, taking 2018 as the most recent year for which there is an almost complete data cover, as well as in change rates, taking 2008 (the period immediately preceding the effects of global financial crisis) and 2013 (marking the start of the post-crisis recovery) as reference years. The technique used for obtaining the final typology has been 4-means clustering on a selection of such indicators after having eliminated redundancies.

The resulting geographical configuration is illustrated in the figure below. The first type, FAST INTERNATIONALISATION, includes only four regions in the European space (Iceland, Northern Ireland, the North-West of England, and the north of Serbia). These are relative newcomers in international tourism that have made a scale jump in the last decade, presenting themselves with an attractive destination profile especially for their rural and small and medium-sized towns. They have been experiencing a strong growth of tourism over the last decade and specifically of the share of international tourists, and are therefore subject to a relatively high tourism pressure (with low growth in cities and towns, high in rural areas). They are relatively unattractive as a site of migration for more senior cohorts but boast high crude migration rates for the younger migration cohorts.

The second class, LOW INTENSITY, includes 92 regions that are characterised as poorly attractive regions for tourism and other migrations but are subject to a rising tourist pressure in cities and towns, have a low and decreasing share of international tourism, and a moderate offer of Airbnb. This is a large set of regions across the core of Europe and stretching to its periphery. These regions are characterised by general low levels of attractiveness for visitors although they have been experiencing recent growth of the tourist intensity in cities and towns. The domestic market is the driving force of tourism development and wherever they have been experiencing some growth this has been mostly accompanied by an expansion of non-traditional forms of hospitality like short-term rentals mediated by digital platforms (as Airbnb). It is noteworthy that in spite of their relatively low tourist dimension, these regions can be moderately attractive for working age adults and senior migrants, maybe precisely on account of the ‘low pressure’ to which they are subject. The context of these regions varies to a great extent, from regions in the European core (as in Germany, France, Belgium and Switzerland as well as Southern Holland) to inland and predominately rural regions of Spain, regions in the Eastern periphery (Poland, Slovakia, Romania), the south of Finland, north of Sweden, the Italian South and Albania.

The third class, STEADY GROWERS, includes 53 regions whose profile is of being attractive and growing regions for tourism, with highest and growing pressure in rural areas, have a high foreign student population in relation to their size, a high and growing share of international tourism. These regions are mostly situated in the Mediterranean coastal and island regions (including almost the whole of Portugal), the Atlantic archipelagos except the Canaries; and extend to regions in Great Britain, the inner part of the Netherlands, Luxembourg, most Scandinavian and Baltic regions, and almost the whole of Greece, plus some ‘capital city regions’ like London, Prague and Bucharest. These are mature destinations for tourism that have not stopped growing and becoming more internationalised in the last decade, registering the highest pressure in non-urban areas, and are poorly attractive for working age younger adults, but moderately attractive for other migrations including under 25 and over-50-year-old workers.

Finally, the fourth class, TOURISM STARS, includes 15 regions that stand out as very attractive for tourism, especially urban, and all migrations, experiencing a moderate growth concentrated in towns and cities; they are subject to a large penetration of Airbnb, and experience a high share of international tourism but seeing a relative growth of the domestic market. These are some of the most visited destinations in Europe and at the same time preferred destinations for migrants of all age groups. Tourist pressure over the last decade has been mostly growing in urban and intermediate areas, and this has been accompanied by a high level of penetration of platform-mediated supply; yet in general the attraction of tourism (the international market in particular) is decelerating, for having possibly met some capacity thresholds. These regions include Catalonia, Madrid, the Balearic and Canary archipelagos, the Algarve region of Portugal, Paris and the South of France, the northeast of Italy, the whole of Croatia and Ireland, and two other capital city regions, North Holland (the region of Amsterdam) and Berlin.

The subsequent step of this analysis has been to calculate the average means of the score of a selection of social indicators in the four classes of regions in this typology, and test that these differences are significant. We have included in this exercise:

  • Health indicators (self-reported perception on health state by participants to the EU-SILC survey)
  • Housing indicators (self-reported perception on quality of housing, financial access to housing and rent values by participants to the EU-SILC survey)
  • Poverty and deprivation indicators (self-reported perception on conditions of dependency, lack of access to basic commodities and consumption, etc.)
  • Labour indicators proceeding from the Labour Force Survey and especially pointing at the dimension of regional employment in the tourism sector and at the characteristics of workers in atypical conditions or earning low salaries

The full discussion of results is available in the SMARTDEST Delverable 2.3, which can be retrieved at https://smartdest.eu/results/#project-reports. Here we only wrap up the most important insights.

A key aspect explored by the literature – but not in a systematic way and using an established metrics – is how positive and negative externalities from tourism development balance out (geographically and socially) and whether population change processes which could be triggered by tourism development may be shadowing an underlying process of social exclusion. In this sense, we have singled out the small group of FAST INTERNATIONALISATION regions as the most problematic to this respect: they present a profile of being places where access to housing represents a burden for women and a heavy burden for non-European foreigners and where a sizable share of the over-65 population lives in overcrowded households, and these hindrances do not balance out through the share of population that derive rents from property, which tend to be the lowest among the four types considered. They present the worst profile in terms of conditions of poverty and deprivation, the female population being particularly affected. They also have the large shares of workers in the tourist with elementary occupations (or others) having atypical work profiles and while they offer good opportunities also in term of salary to foreigners and women, they seem to offer them worse condition in terms of protection. The LOW INTENSITY regions present quite an opposite profile – though they derive much lesser benefits from tourism and other inward flows of migration, they show very little of the hindrances through which tourism growth may sustain pathways of social inequality and exclusion.

The other two categories, STEADY GROWERS and TOURISM STARS, are a mixed bag. The former group of regions have not reached a stage of development in which tourism pressure could be considered excessive (also on account of the relative spread of tourism activity out of urban areas), especially in relation to housing affordability, and they have some the best profile in terms of salaries paid. Their trajectory of development has been more paced, having had the opportunity to become embedded in new structures of institutional and social capital, yet the trends indicate that they may resent from an increasing specialisation in tourism, which makes them particularly vulnerable to systemic crises like the one that we are currently living with COVID-19. Finally, TOURISM STARS are in their majority characterised as places where the intensification of tourism in areas otherwise economically buoyant, of their very strong degree of specialisation in tourism, could have tipped some threshold which challenge social inclusion, for instance nuancing a high level of polarisation (for instance between homeowners and tenants), deprivation, and work conditions. That the already high level of concentration in urban areas has not grown in the last decade in average as much as in other regional types is not preventing the tourism economy to increase its dimension and lead to a structural deflation of employment conditions.

These findings may thus inform on some of the key challenges that should be taken into account in the European urban and regional policy agenda when the ‘tourist dimension’ and pace of evolution of regions is considered as a driver of social change, such as housing affordability, socio-spatial polarization, the casualization and precarious nature of tourism work or the effects that the reconfiguration of space brought about my global mobilities in their anchoring to place has on the most vulnerable segments of resident communities. These areas of concern will be the object of in-deep scrutiny in further stages of the SMARTDEST project both at pan-European and at case study level.