Tourist Intensity in the World, 1995–2015: Two Measurement Proposals

The work emphasizes the importance of measuring the tourist intensity of the economies that are oriented to tourism activity, with the aim of avoiding subjective arguments and being more related to perception than with the empirical contrast of the data. A tourist intensity index is proposed, which is made up of four essential variables: GDP, tourist spending, population, and the number of tourists. However, at the same time, it is complemented by a measure of tourist density, which helps to better understand the proposed index. This allows for the classification of countries according to the resulting index, and to calibrate their position in the set of tourist economies. This can be very useful for the application of economic policies aimed at correcting externalities that are generated in the advanced development of mass tourism.


Introduction
A concept that has been disseminated insistently in recent times is that of saturation and/or intensity in the leading mass tourism economies, after the great boom in tourism economy as of 1950 [1]. The media have echoed this issue with certain insistence, often times using arguments and experiences of a subjective nature-which are not to be ignored-but with a lack of reliable empirical data to allow reasonable-and homogenised-contrast between different geographic areas. Indeed, despite the importance of tourism on an international scale, there is no single consensus regarding how to measure tourism intensity. This is understood, by some authors, as the number of overnight stays per resident [2][3][4]; while others adduce the number of tourist arrivals and overnight stays [5,6]. In both cases, the models are basically related to the life cycle of the tourism product [7], in its different stages of development. There are also contributions from study cases of tourism destinations with high intensities, in order to identify threats and possible innovative solutions. In each contribution, the definition of tourism intensity is different: it may be the relationships of tourists with respect to the permanent population; or the number of annual tourists divided by the km 2 of territory; sometimes, it is even calculated as overnight stays per 1000 inhabitants, or number of arrivals per 100 inhabitants [8][9][10][11][12][13][14].
At the same time, empirical contributions concerning the sustainability of tourism-besides the theoretical contributions in this field-incorporate decisive elements for analysing the sector. From the abundant bibliography available, we highlight these recent contributions: [15][16][17][18][19][20][21][22]. In short, intensity, saturation, and sustainability-and even, the notion of governance-have ended up being concepts that are increasingly used by social scientists to analyse tourism phenomena [23][24][25][26][27][28][29][30]. This methodologically disparate situation is what justifies the main purpose of this research: the proposal of two specific instruments for measuring tourism intensity. (There exists important official documentation regarding tourism intensity. By way of example, [31][32][33][34][35]. Bed count and/or overnight stays are usually the The authors define two different indices, the Tourism Intensity Index and the Tourism Density. The Tourism Intensity Index (TII) adopts demographic and economic variables obtained from the WTO for all countries. This ensures the homogeneity of the selection of the variables for drawing up the index. Namely, this is defined as follows: where T is the number of tourists, P the population, TR tourism revenue, GDP the gross domestic product, and the subscript i is used for a specific country, and w for the world. The WTO provides, amongst many other indicators and always from a general perspective, the relationship between the amount of inbound and domestic tourism and the population, as well as the percentage of the GDP of inbound tourism spending from 1995 to 2015 for all the countries in the world. It must be indicated, however, that these data are not always available and, occasionally, neither are they available for the same country for all the years analysed. This led to a methodological problem that we attempted to solve by focusing on the trends of the series.
While carrying out the calculation of the TII, we added a new geographical aspect: the Tourism Density (TD) of the country, that is, the number of tourists per km 2 : If the demographic component of the TII is multiplied by the population ratio per km 2 of the country, the tourism density will be obtained. This calculation is reached by combining the information from the WTO with that gathered from the World Bank concerning the population and number of km 2 for each of the countries.
Having defined the contents of the two indices, the calculation of the TII for all the countries between 1995 and 2015 is presented below. Countries were ranked from greater to lower tourism intensity, depending on the mean value of the indicator over this period, and were classified according to whether they had a very high TII (mean greater than 1500), a high TII (mean lower than 1500 and Sustainability 2018, 10, 4546 5 of 20 over 500), a medium TII (mean lower than 500 and greater than 100), or a low TII (mean lower than 100). This enabled us to obtain four large blocks of countries by comparing to 100, which would be the average world value. Let us look at this in greater detail.

Countries with a Very High Tourism Intensity Index
The countries classified as having a very high TII are the ones listed in Table 1. In Figure 1, the set of countries with the greatest TII and the evolution of these indicators from 1995 to 2015 can be observed.

Countries with a Very High Tourism Intensity Index
The countries classified as having a very high TII are the ones listed in Table 1. In Figure 1, the set of countries with the greatest TII and the evolution of these indicators from 1995 to 2015 can be observed. The interpretation of these materials is clear: except for the territory of Macau (made up of the mainland of Macau, connected to Asia, and the two islands of Taipa and Coloane), the rest are archipelagos located in the Caribbean (Anguilla, Antigua and Barbuda, Aruba, and the Bahamas) and the Maldives in the Indian Ocean. All of these have the highest TII and, by far, the greatest in this group, we find to be Macau, one of the largest gambling centres in the world, followed by Aruba, which boasts the best casinos in the Caribbean.
In Table 2, the two basic components of the very high TII are broken down, by country, for 2014 (it must be noted that for 2015, much of the data is still not yet available). Both Macau and the Maldives reveal a dependency on tourism spending that is greater than 90% of their GDP; but the large number of tourists, with respect to the population produced in Macau, places it in the first position, far ahead of Aruba, in second place. The interpretation of these materials is clear: except for the territory of Macau (made up of the mainland of Macau, connected to Asia, and the two islands of Taipa and Coloane), the rest are archipelagos located in the Caribbean (Anguilla, Antigua and Barbuda, Aruba, and the Bahamas) and the Maldives in the Indian Ocean. All of these have the highest TII and, by far, the greatest in this group, we find to be Macau, one of the largest gambling centres in the world, followed by Aruba, which boasts the best casinos in the Caribbean.
In Table 2, the two basic components of the very high TII are broken down, by country, for 2014 (it must be noted that for 2015, much of the data is still not yet available). Both Macau and the Maldives Sustainability 2018, 10, 4546 6 of 20 reveal a dependency on tourism spending that is greater than 90% of their GDP; but the large number of tourists, with respect to the population produced in Macau, places it in the first position, far ahead of Aruba, in second place.

Countries with a High Tourism Intensity Index
The countries classified with a high TII are the ones given in Table 3. Standing out in this group are many archipelagos that are famous for their tourism industry: Barbados, Bahrain, Belize, Dominica, Fiji, Grenada, Jamaica, Malaysia, Mauritius, Montserrat, Saint Lucia, Saint Kitts and Nevis, Seychelles; and, also, countries such as Austria, Croatia, Spain, the United States, Estonia, France, Greece, and Hong Kong, among others. In Table 4, the five-year evolution of the TII is given, from 1995 to 2015, for the group of countries whose mean value for the period is greater than 500 and less than 1500. In Table 5, the TII components for 2014 are broken down into their demographic and economic aspects. From the demographic point of view, the greatest pressure is received by Australia, followed by Bahrain and Taiwan, whereas, at the economic level, Seychelles reveals the greatest dependency followed by Saint Lucia, Dominica, Fiji, and Belize (it should be noted that for some countries, there are no data available in 2014).

Countries with a Medium Tourism Intensity Index
The countries classified as having a medium TII are shown in Table 6. In Table 7, the five-year evolution of the TII can be observed from 1995 to 2015, for countries whose value for the period is greater than 100 and less than 500. As this is a mean value for the whole 20-year period, some higher magnitudes for the TII may be found, as this is not a mean value but, rather, the specific value of the TII for that particular year. Upon analysing the data in Table 8, the country with the greatest demographic pressure is Korea, followed by Japan; in both cases, economic dependence is small. At the opposite end of the scale, we find Jordan, followed by Albania and Panama, with important economic dependence, but with controlled demographic pressure.

Countries with a Low Tourism Intensity Index
The countries classified as having a low TII are listed in Table 9. In Table 10, the five-year evolution of the TII from 1995 to 2015 can be appreciated for the countries with a mean less than 100; many are excluded from the classification, due to a lack of data available in the WTO for the period analysed. From Table 11, it can be deduced that, within the group, the country with the greatest economic dependence is the Solomon Islands, followed by Bhutan, Togo, and Sri Lanka. In no case is significant demographic pressure visible. The classification demonstrates the relevance of the Caribbean as a key area in global tourism intensity, a reality that describes its nature of island economies [36,55].
Below, we present the calculation of the Tourism Density (TD) for all the countries from 1995 to 2015. With these data, a ranking was drawn up from greater to lower tourism density, based on the mean value of the indicator in this period, and the countries were classified according to whether they have very high TD (mean greater than 10,000 tourists per km 2 ), high TD (mean less than 10,000 tourists per km 2 and over 1000 tourists per km 2 ), medium TD (mean less than 1000 tourists per km 2 and greater than 300 tourists per km 2 ), or low TD (mean less than 300 tourists per km 2 and greater than 100 tourists per km 2 ), enabling us to obtain, in turn, four large blocks of countries.
In Figure 2, the evolution of Tourism Density, from 1995 to 2015, can be observed in Macau-the country with the greatest tourism density in the world-far greater than the next two, which are Hong Kong and Singapore. Macau goes from a tourism density of 210,100 tourists per km 2 in 1995 to 480,726 tourists in 2014 (there are no data available for 2015), representing 128.8% growth over this period.
Far behind, but still classified as having very high tourism density, we find Hong Kong and Singapore, whose evolution from 1995 to 2015 is shown in Figure 3. Hong Kong goes from a density of 6532 tourists per km 2 in 1998 to 25,183 tourists per km 2 in 2014, namely, 285.5% growth over the period (Table 13). Singapore goes from 9034 tourists per km 2 in 1995 to 16,390 in 2014, up 81% (Table 13).
than 100 tourists per km 2 ), enabling us to obtain, in turn, four large blocks of countries.
In Figure 2, the evolution of Tourism Density, from 1995 to 2015, can be observed in Macauthe country with the greatest tourism density in the world-far greater than the next two, which are Hong Kong and Singapore. Macau goes from a tourism density of 210,100 tourists per km 2 in 1995 to 480,726 tourists in 2014 (there are no data available for 2015), representing 128.8% growth over this period. Far behind, but still classified as having very high tourism density, we find Hong Kong and Singapore, whose evolution from 1995 to 2015 is shown in Figure 3. Hong Kong goes from a density of 6532 tourists per km 2 in 1998 to 25,183 tourists per km 2 in 2014, namely, 285.5% growth over the period (Table 13). Singapore goes from 9034 tourists per km 2 in 1995 to 16,390 in 2014, up 81% (Table  13).  In Figure 4, the evolution of density from 1995 to 2015 is revealed for the set of countries that were classified as having high Tourism Density.  In Figure 4, the evolution of density from 1995 to 2015 is revealed for the set of countries that were classified as having high Tourism Density.
The classification was drawn up based on the mean tourism density value between 1995 and 2015. It must be remembered that, in the case of high tourism density, the mean value must be less than 10,000 tourists per km 2 and over 1000 tourists per km 2 . As it is a mean value for the whole 20-year period, some values greater than 10,000 tourists per km 2 can be found in Figure 4, as this is not a mean value but, rather, the specific value for the corresponding year. Leading this group is Bahrain, which goes from a density of 3255 tourists per km 2 in 1995 to 13,556 tourists per km 2 in 2014, representing 216.5% growth ( Table 13). The evolution of tourism density is very different among the countries classified as having high tourism density. At one end, we find the Maldives, whose tourism density increased 327.6% between 1995 and 2014; and at the other end, is Japan, which decreased 18.3% between 2008 and 2014 (in this figure, the impact of the earthquake in 2011-the strongest recorded in Japanese history-can be clearly seen). In Figure 4, the evolution of density from 1995 to 2015 is revealed for the set of countries that were classified as having high Tourism Density. The classification was drawn up based on the mean tourism density value between 1995 and 2015. It must be remembered that, in the case of high tourism density, the mean value must be less than 10,000 tourists per km 2 and over 1000 tourists per km 2 . As it is a mean value for the whole 20year period, some values greater than 10,000 tourists per km 2 can be found in Figure 4, as this is not a mean value but, rather, the specific value for the corresponding year. Leading this group is Bahrain, which goes from a density of 3255 tourists per km 2 in 1995 to 13,556 tourists per km 2 in 2014, representing 216.5% growth ( Table 13). The evolution of tourism density is very different among the countries classified as having high tourism density. At one end, we find the Maldives, whose tourism density increased 327.6% between 1995 and 2014; and at the other end, is Japan, which decreased 18.3% between 2008 and 2014 (in this figure, the impact of the earthquake in 2011-the strongest recorded in Japanese history-can be clearly seen).
In Table 12, the five-year evolution, between 1995 and 2015, is outlined for the Tourism Density of the set of countries classified as having medium tourism density. In this group, we find all the countries whose average tourism density over the period considered is less than 1000 tourists per km 2 and greater than 300 tourists per km 2 .  In Table 12, the five-year evolution, between 1995 and 2015, is outlined for the Tourism Density of the set of countries classified as having medium tourism density. In this group, we find all the countries whose average tourism density over the period considered is less than 1000 tourists per km 2 and greater than 300 tourists per km 2 . European countries appear, for the first time, in the classification: England, France, the Czech Republic, Belgium, Spain, Cyprus, Italy, Switzerland, and Luxembourg. Table 13 shows the evolution of tourism density, which is very different among the European countries classified in the group of medium tourism density. On the one hand, we find Spain, whose density increased 27.8% from 1999 to 2014, going from 318 tourists per km 2 to 406 tourists per km 2 ; and, on the other hand, we find Switzerland, with a drop of 10.5% between 1998 and 2014, from 423 tourists per km 2 to 379 per km 2 .

Conclusions
First of all, the main aim of this research was to design an index to calculate tourism intensity, a key concept for any tourism destination, as it can affect the wellbeing of both residents and the tourists themselves. We established this by consulting the databases of the UNWTO for the period 1995-2015, using four determining vectors: the GDP, tourism spending, the population, and the number of tourists. We thereby built a compound index that is more convincing than tourism analysis using only one of the aforementioned indicators. However, our aim was above all descriptive, without going into-which would require considerable length-the economic characteristics of the groups of countries we were able to group together, based on the calculation of the TII. Classifying a destination according to whether it has a very high, high, medium, or low tourism intensity, may be useful when it comes to determining the type of policies that must be defined in order to eliminate negative externalities and to boost positive ones. Along these lines, we believe an analysis of tourism densities (TD) is also important, and we put forward a second proposal for measuring tourism intensity based on a specific formula, with data from the TII and from the World Bank. Both methodologies enabled different rankings to be drawn up, broken down according to tourism intensity, with some specific parameters: very high, high, medium, and low.
Secondly, it is emphasised that the region with the highest tourism intensity in the world is Macau, a destination that focuses its economy on gambling, as it boasts numerous casinos in its territory. The TII is 6656, the mean between 1995 and 2015 (100 would be the mean world value), with a TD of 48,726 tourists per km 2 in 2014. The case of Macau is not unique; in fact, another relevant conclusion is that island economies are the ones with the greatest TII. In this regard, the present study follows the lines marked in a previous one [36], in which it was also determined that the highest tourism densities in the world were found in archipelagos and, specifically, in the Cayman Islands, British Virgin Islands, and the Balearic Islands.
Thirdly, we can affirm that the changes taking place in tertiary economies in the process of economic globalisation are very fast. The different tourism intensities will, undoubtedly, condition regulations of all kinds: urban, fiscal, and pertaining to the tourism industry itself. Hence, the importance, decisive in our opinion, of measuring tourism economy with tools that are innovative (the use of bioeconomy criteria is another key research path to studying the evolution of tourism economies more accurately, based on biophysical indicators that affect sustainability processes; see [56][57][58][59][60][61][62][63]). Based on this observation, a whole range of opportunities for, and threats to, tourism economies open up. As for the former, the dynamic competitiveness of the productive systems consists not only of the ability to adapt to changes in demand, but to do so in the shortest possible time. Indeed, the speed with which local actors process and put information into practice is crucial, and this can be boosted through cooperation between the different productive units. The agility whereby this information is systematised is related to three essential factors, amongst others: firstly, the productive resources of companies, depending on their critical mass or size; secondly, human capital and the implementation of regional and local innovation systems, since their availability may favour finding new possibilities of efficient productive combinations, in order to respond to changes in demand; finally, the function of leadership, which the public sector would have to take on with effective synergies with private capital.
We would like to highlight the fact that the empirical precision of tourism intensity, quite apart from perceptions that are subjective or have clear political intentions, is what we are seeking in this line of research, with the result of drawing up the Tourism Intensity Index (TII) and the Tourism Density index (TD). They have two main potentials: they enable a homogeneous comparison, using four main indicators that come from institutions with open consultation databases-the UNWTO and the World Bank; and they determine a specific numerical magnitude which, at least, eschews subjective observation which, despite always being respectable, may be biased.
The tourist intensity is affecting many regions specialized in the leisure industry. These are factors of concern: problems of demographic congestion, high consumption of natural and energy resources, ecological impacts that affect the landscape, and even our own cultural values. All these elements, which are clearly sensed in our research, force policy makers to act in very clear directions. The most important, without a doubt, will be the environmental sustainability of the tourist territory. This territory is the main context, the basic natural capital that is a claim for visitors. We understand that magical recipes cannot be given in economic policy. However, at the same time, we think that the objectives to be achieved would be to encourage renewable energies, a strong technological renovation that makes processes more efficient (production of electricity by photovoltaic means, for example), the possibility of promoting a specific environmental taxation for tourism and, last but not least, betting on the formation of a human capital specialized in mass tourism in all its aspects.
Lastly, our results are of a macroeconomic nature, and are usually assigned to nation states. However, we think that, as research assumptions, the indices and the methodology we have constructed would be equally applicable to a more regional-scale analysis. The sustainability of tourism should be treated from regional perspectives (as has also happened with studies of economic history in the industrial field). This is why this can be a good line of future research: the disaggregation of data on a regional scale. Now, the research that is developed in the future will have the limits that will be marked by the availability of statistical records. This is fundamental. However, at the same time, the proportion of these new measurement indices can be contrasted with others that should be investigated. In this sense, we are working on the preparation of biophysical indicators for the tourist economy, which will complement the two indices presented in this work.
Author Contributions: C.M. develops the methodological part of the paper defining the indexes and E.V. performs the calculations of the defined indices. The writing of the article and the conclusions has been elaborated jointly. And all authors read and approved the final manuscript.