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2.6.1 Econometric model specification and estimation results

Tourism demand has been modelled as a function of a set of explanatory variables reflecting the characteristics of a country that are most likely to influence the country’s tourism

attraction potential. The functional form of the model is a log-log regression model, displayed in Equation (3.1): i i i i i i i X X X X X u Y = ₀ + ₁ln ₁ + ₂ln ₂ + ₃ln ₃ + ₄ln ₄ + ₅ln ₅ + ln

β

β

β

β

β

β

Where the dependent variable is the number of tourist arrivals per country (Yi) and the explanatory variables are the characteristics of the trip (x1), country socio-economic and demographic situation (x2), climate conditions (x3), cultural and natural heritage (x4) and the features of the country’s biodiversity profile (x5). The coefficients can be interpreted as the elasticities of the number of tourist arrivals with respect to the different dependent variables7. In order to analyse the differences in the structure of demand across international and domestic tourism flows, the number of international and domestic arrivals in each country have been regressed against the previously described explanatory variables, running two separate models. Estimation results are presented in Table 2—3.

As we can see, GDP of the destination country has a positive and significant impact on the number of both international and domestic tourist arrivals. This result steams two possible interpretations. As regards international tourism, a higher GDP per capita in the country of destination may be read as an indicator of the degree of development. A developed country will have more and/or higher quality, accommodations and infrastructure that make the destination attractive from the tourist point of view. As far as the domestic tourism flows are concerned, the positive impact of GDP per capita can be interpreted as an income effect, since residents in countries having achieved a higher income level will have higher ability to pay for travelling.

Secondly, population density is also found to exert a positive and significant impact on tourism flows, in both the international and domestic segment, even though its coefficient is significantly higher for domestic tourists. This may signal that the more densely populated a country, the more its nationals will tend to spend their holiday in their own country. The same reasoning holds for the country area. On the one hand, a larger country presents a variety of different landscapes and cultural sites, and therefore it attracts a higher proportion of both international and domestic tourists. On the other hand, larger countries supply ceteris paribus a larger amount of accommodation possibilities. As far as the climatic variables are

7 _{The model has been run for the total number of tourists visiting each country. International and}

domestic tourists arrivals have been included among the explanatory variables. The difference of their respective coefficients has been found to be significant with a confidence level of 95%. Therefore, it has been decided to run the same model for international and domestic tourist arrivals. The results of those models are presented in the remainder of this paper.

concerned, the average annual temperature is negatively correlated to tourist arrivals in both models. However, this magnitude reveals to be statistically significant only in the domestic sub-sample. This result signals that this market segment is more sensitive to potential temperature increases.

As observed before, the habitat diversity component is represented by the share of country surface covered by forest and wetland habitats. Different econometric patterns emerge across the international and domestic segment. In fact, the number of international arrivals is not influenced by forest habitats, while the domestic segment is positively influenced. Once more, this result suggests that the structure of preferences among the two segments differ. As regards the species diversity component, the number of bird species has a negative influence on international tourist arrivals, while the number of mammal species is found to exert a positive effect. In turn these indicators do not show any impact on the domestic tourism flows. The Biodiversity Index for bird species is positively correlated to domestic tourism flows, which could signal a higher interest in the conservation status of bird species than in the number of different species. The share of country surface mapped as protected area and the number of UNESCO World Heritage sites have a positive impact on the number of international tourist arrivals. An interesting result concerns the impact of the presence of coastal areas. As a matter of fact, countries having access to the coast are found to attract a higher number of domestic tourists, signalling a higher sensitivity of domestic tourism demand, compared with international demand, to the possibility to access the coast.

**Insert Table 2—3 about here**

These results contain several insignificant variables; this suggests the presence of sample size and multicollinearity problems. Therefore a stepwise removal of insignificant variables has been performed and the results are displayed in Table 2—4 for international tourists and in Table 2—5 for domestic tourists. These results show that three variables, namely GDP per capita, population density and country surface are consistently significant across the two models. The number of bird and mammal species, the share of country surface mapped as protected area and the number of world heritage sites are significant when international tourist flows are considered. On the other hand, the extension of forests, the score of the biodiversity index for bird species and the country’s average temperature are significant as regards domestic flows. The removal of insignificant variables does not substantially affect the explanatory power of the models.

2.6.2 A model for coastal tourism flows

The subsequent step of this analysis involved the estimation of an econometric model focusing on a sub-sample of tourism flows, which only refers to coastal areas. It is generally intuitively understood what is meant by coastal zone, it is difficult to place precise boundaries around it, either landward or seaward. The coastal zone is generally defined as the part of the land affected by its proximity to the sea, and that part of the sea affected by its proximity to the land as the extent to which man's land-based activities have a measurable influence on water chemistry and marine ecology (Van den Bergh and Nijkamp, 1998). In addition, the coastal zone may vary in territorial depth from one area to another depending on the issues to be considered. Despite the challenging task of defining it, the landward part of the coastal zone can play an important role for human settlement and tourism (EEA, 1995).

In order to proceed with the estimations, it has been chosen to disaggregate tourism data at the NUTS II level. Then we took into account domestic and international tourism flows going towards these NUTS II regions, in particular those having direct access to the coast. The model structure is analogous to the one presented section 2.6.1. Two additional explanatory variables were considered: the length of the coastline and the beach surface. These are interpreted as relevant characteristics of a country’s coastal area. In addition to that, the number of NUTS II regions having access to the coast, out of the total number of regions of each country, has been considered as a proxy for the potential for seaside recreation and coastal tourism.

The estimation results, as shown in Table 2—6, reiterate some of the results obtained from the previous model. As a matter of fact, GDP per capita and population density in the country of destination prove to exert a positive and significant impact on international and domestic tourist arrivals. The length of the coastline appears to be positively correlated with the number of tourists choosing the country’s coastal regions as their destination. However, the coefficient is remarkably higher for domestic tourists, thus confirming that domestic tourists seem to be more influenced by the possibility to access the coast, when making decisions regarding their destination.

The number of UNESCO World Heritage sites produces a positive impact on international tourist arrivals. On the other hand, species and habitat diversity indicators do not exert a significant influence on either of the two demand components. The only significant exception is the Biodiversity Index for bird species, which proves to be positively correlated to domestic arrivals, consistently with the findings of the previous model.

As already noted in the previous paragraph, the results displayed in Table 2—6 contain several insignificant variables and multicollinearity appears to be an issue. A stepwise removal of the insignificant variables has been performed and the results are displayed in Table 2—7 for international tourists and in Table 2—8 for domestic tourists. These results show that GDP per capita, is significant across the two models. As far as international tourists are concerned, the extension of forested areas, the number of mammal species and the number of World Heritage sites are significant in the restricted model. When domestic tourists are considered, population density, the length of the coastline, the number of bird species, the biodiversity index for birds, the number of world heritage sites and the country’s average yearly precipitation are significant in the restricted model. Again the removal of insignificant variables does not substantially alter the explanatory power of the models.