The Impact of High-Speed Rail Systems on Tourist Attractiveness in Italy: Regression Models and Numerical Results

Abstract

This paper evaluates the impact of high-speed rail systems on tourist attractiveness in Italy. The analysis is carried out with reference to provincial capitals, only some of which are served by high-speed railway lines. To achieve this objective, two multiple linear regression models were specified and calibrated, which relate arrivals and presences in accommodation facilities to several factors that could influence the tourist destination: cultural, historical, and monumental heritage, commercial activities, recreational activities, accessibility, etc. Both models showed that the availability of high-speed railway services is an important factor in the choice of tourist destination, being, moreover, the only accessibility variable found to be significant; furthermore, the elasticity of tourist demand to this factor was significant too.

Full text

Sustainability 2022, 14, 13818. https://doi.org/10.3390/su142113818 www.mdpi.com/journal/sustainability
Article
The Impact of High-Speed Rail Systems on Tourist
Attractiveness in Italy: Regression Models and Numerical
Results
Mariano Gallo
1,
* and Rosa Anna La Rocca
2
1
Department of Engineering, University of Sannio, 82100 Benevento, Italy
2
Department of Civil, Building and Environmental Engineering, University of Naples ‘Federico II’,
80125 Napoli, Italy
* Correspondence: gallo@unisannio.it
Abstract: This paper evaluates the impact of high-speed rail systems on tourist attractiveness in
Italy. The analysis is carried out with reference to provincial capitals, only some of which are served
by high-speed railway lines. To achieve this objective, two multiple linear regression models were
specified and calibrated, which relate arrivals and presences in accommodation facilities to several
factors that could influence the tourist destination: cultural, historical, and monumental heritage,
commercial activities, recreational activities, accessibility, etc. Both models showed that the availa-
bility of high-speed railway services is an important factor in the choice of tourist destination, being,
moreover, the only accessibility variable found to be significant; furthermore, the elasticity of tourist
demand to this factor was significant too.
Keywords: tourism; high-speed rail; accessibility; regression models
1. Introduction
The accessibility of a site is one of the main factors influencing its ability to attract
residential, commercial, and industrial settlements. In the literature, it is possible to find
numerous works that confirm this assumption. Good accessibility makes a place easy to
reach (passive accessibility) and allows those who live or work there to easily reach other
places (active accessibility), making it more attractive to live in or establish a commercial
or industrial activity.
From the tourism point of view, the accessibility of a site or a city can influence the
choice of users when planning a trip, thus indirectly affecting the destination
attractiveness; in fact, tourists tend to prefer an easily accessible destination over another
that is more difficult to reach.
The accessibility of a place, in general, and even more so in tourism, is strongly
influenced by the available public transport services. In this context, high-speed rail
transport systems play an important role in the tourist development of a location,
increasing its tourist accessibility as well as its general accessibility.
The purpose of this paper is to study the impact that high-speed rail (HSR) systems
may have on tourism. To achieve this, two linear regression models were calibrated and
specified to estimate tourist flows as a function of several accessibility variables, including
the number of runs of high-speed rail services, as well as variables of cultural and tourist
assets consistency. The models were calibrated with data from 111 provincial capitals in
Italy, with reference to the year 2018, which is not affected by the impact of the COVID-
19 pandemic. Although similar models could be calibrated for other Western countries,
the Italian case study is significant because high-speed services are not widespread: of 111
Citation: Gallo, M.; La Rocca, R.A.
The Impact of High-Speed Rail
Systems on Tourist Attractiveness in
Italy: Regression Models and
Numerical Results. Sustainability
2022, 14, 13818. https://doi.org/
10.3390/su142113818
Academic Editors: Lóránt Dénes
Dávid, Laszlo VASA and Setiawan
Priatmoko
Received: 6 October 2022
Accepted: 23 October 2022
Published: 25 October 2022
Publisher’s Note: MDPI stays
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Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
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Attribution (CC BY) license
(https://creativecommons.org/license
s/by/4.0/).

Sustainability 2022, 14, 13818 2 of 35
provincial capitals, 49 are not served by high-speed rail at all, and 41 are served with no
more than 10 rides per day.
In this study, we consider high-speed services not only those which exceed a
maximum speed of 300 km/h (such as Trenitalia Frecciarossa services) but also those
which reach a maximum speed of 250 km/h (Trenitalia Frecciargento services) and 200
km/h (Trenitalia Frecciabianca services), according to the UIC definition: “High-speed rail
combines many different elements which constitute a ‘whole, integrated system’: an infrastructure
for new lines designed for speeds of 250 km/h and above; upgraded existing lines for speeds of up
to 200 or even 220 km/h, including interconnecting lines between high-speed sections” [1].
The limitations of the proposed models lie in their applicability only to the Italian
case, but similar models can be specified and calibrated in other territories with the same
approach proposed in the paper; from a temporal point of view, the models can be re-
calibrated with reference to years different from the one under study, just as they can be
applied after the construction of new high-speed railway lines to check whether the
predictions remain valid. This work can contribute to the evaluation of investments in
high-speed rail transport systems at the regional and national levels.
The goals of this study are basically twofold: (i) to verify whether and to what extent
the presence of high-speed services has a real impact on tourist attractiveness; (ii) to carry
out this verification through quantitative methods (mathematical models, in our case) that
also provide a numerical estimate of the corresponding impact.
The paper is articulated as follows: Section 2 examines the background of the
problem; Section 3 describes the data; multiple linear regression models are specified and
calibrated in Section 4; an application to the city of Benevento is described in Section 5;
conclusions and research perspectives are summarised in Section 6.
2. Background
2.1. Key Tourism Data
Over the past sixty years, tourism has steadily grown in both volume and
importance, becoming one of the key pillars of the world economy. In 2018, for the ninth
consecutive year, except for a period of crisis (2007–2009), international arrivals have
grown (Figure 1).
Data from the UN World Tourism Organisation [2] show that international tourist
arrivals worldwide have reached 1.4 billion two years ahead of forecasts; it is due in part
to the easiness of travelling, lower travel costs, the simplification of obtaining a visa, and
some other factors that act as enablers for the expansion of tourism. Among destinations,
Europe is the most popular, accounting for 51% of total arrivals in 2018 (710 million). Italy
is the fifth destination in the top ten most visited countries in the world, with 62 million
international arrivals (+7% from 2017 to 2018).
Arrivals (billions) Revenues (trillions)
Figure 1. Tourism arrivals (left) and revenues (right) in the world from 2009 to 2019 [2].
The data show that leisure activities and holidays are still the main purposes of the
trip. The analysis of the purpose is useful for understanding the needs of tourism demand

Sustainability 2022, 14, 13818 3 of 35
in terms of amenities and ancillary services that play a strategic role in the competition
between tourist destinations. This is consistent with the traditional definitions of tourism:
“Tourism is a social, cultural and economic phenomenon which entails the movement of people to
countries or places outside their usual environment for personal or business/professional purposes.
These people are called visitors (which may be either tourists or excursionists; residents or non-
residents) and tourism has to do with their activities, some of which involve tourism expenditure.”
[3,4].
Tourism is a crucial economic sector for Italy: in 2018, tourism had direct and indirect
effects on GDP of 5% and 13.2%, respectively; furthermore, tourism directly generated 6%
and indirectly 15% of total employment.
The attractiveness of ‘destination Italy’ is facilitated by the presence of a wide system
of heritage attractions in terms of historical villages, archaeological sites, cities of art,
cultural tradition, significant landscape, and seaside resorts. This system consists of 4026
museums, galleries, or collections, 570 monuments and monumental complexes, and 293
archaeological areas and parks. Moreover, 2371 municipalities host at least one museum,
and there are 58 locations included in the UNESCO world heritage, positioning Italy at
the top of the world ranking [5].
Analysing the movement of tourists by region, the data show that Veneto is the
leader with over 19 million arrivals, followed by Lombardy with around 17 million; a
second group includes Emilia Romagna, Tuscany, and Lazio, forming a central backbone
throughout the country; Basilicata and Molise, on the other hand, fall into the lowest class
of values (Figure 2).
2.2. Literature Review
In the state-of-the-art, we can identify three groups of works: (1) papers studying
models for estimating tourism demand; (2) papers studying the impact of HSR services
on aspects different from tourism; (3) papers studying the impact of HSR services on
tourism.
Tourism demand estimation is a topic widely covered in the literature. A review on
the subject can be found in [6].
There are several papers based on modelling time series and proposing forecast
models based on them. Cho [7] compared three different approaches to forecasting tourist
arrivals (exponential smoothing, univariate ARIMA, and Artificial Neural Networks,
ANN) and found ANN to be the best method. Palmer et al. [8] proposed ANN-based
models for forecasting tourism time series. Akin [9] proposed an approach for the
selection of the best models for tourism demand estimation, starting from the comparison
between three models used to determine time series; she defined a set of rules to identify
the most suitable model according to the available data. Spatial interaction models for
estimating tourism flows were proposed in [10]. Chan and Lim [11] analysed tourism
seasonality in New Zealand using spectral analysis. An approach based on evolutionary
fuzzy systems was proposed in [12]. Hassani et al. [13] proposed the use of Singular
Spectrum Analysis (SSA) to predict tourist arrivals in the USA; the authors highlight that
there are significant advantages of the proposed approach over more traditional ones such
as ARIMA, exponential smoothing, and ANN. Li et al. [14] proposed a model based on
principal component analysis and artificial neural networks for estimating tourism
volumes based on time series. Other works on time series-based tourism flow forecasting
can be found in [15,16], which proposed approaches based on structural time series, and
[17], which also refers to stochastic nonstationary seasonality. Chu [18] proposed a
fractionally integrated autoregressive moving average approach to forecasting tourism
demand in Singapore. Andrawis et al. [19] applied time series to tourism in Egypt, while
Nelson et al. [20] studied a case study in Hawaii.

Sustainability 2022, 14, 13818 4 of 35
Figure 2. Movement of tourists by region.

Sustainability 2022, 14, 13818 5 of 35
There are many recent papers in the literature studying the impacts of HSR services
from different perspectives. Cheng and Chen [21] studied the impacts on the capacity of
traditional passenger and freight rail services.
Impacts on social exclusion/inclusion were studied by Dobruszkes et al. [22], who
highlighted that the users of such services are predominantly “...male, higher income, highly
educated and belonging to higher social occupational groups”; Ren et al. [23], who studied the
impact of HSR services on social equity in China; and Cavallaro et al. [24], who studied
the spatial and social equity aspects related to HSR lines in Northern Italy.
Several papers have studied the impacts of pollution and/or greenhouse gas emis-
sions, including the work of Fang [25], who studied the impact on air pollution in China,
showing that it tends to decrease in regions where services are present, compared to un-
served areas; Jia et al. [26], who studied the impact on CO2 emissions in China, showing
that there are significant reductions in greenhouse gas emissions; Strauss et al. [27], who
studied the impact of HSR services on air transport demand and overall CO2 emissions.
Several other papers have studied the impacts of HSR services on property values,
including Huang and Du [28], who studied the effects on land prices in China, showing
that the impact is significant, particularly in urban areas; Okamoto and Sato [29] examined
the impacts of HSR services on land values, focusing on a region in Japan; Zhou and
Zhang [30] studied the impacts on both property values and GDP.
The impact of HSR services on the industry was studied by Tian et al. [31], who ex-
amined the impact on service industry agglomeration in peripheral cities, showing that
HSR facilitated economic growth in core cities at the expense of peripheral cities. The cor-
relation between HSR services and the evolution of the high-tech industry in China was
studied by Xiao and Lin [32], showing that the impact was significant, especially in cities.
Chang et al. [33] studied the impact of the extension of the HSR network on industrial
movement patterns in China’s Greater Bay Area, showing that the expansion of the net-
work led to a decentralisation of large industries. Zhang et al. [34] studied the impact of
HSR on consumption in China.
Studies on the impact of HSR services on tourism are also numerous. In particular,
the countries where these impacts have been most studied are China, Spain, and Italy. The
main works concerning China were proposed by Wang et al. [35], who studied the effects
of the HSR network on regional tourism development; Jin et al. [36], who studied the im-
pact of HSR on winter tourism in a specific region; Wang et al. [37] studied the impact on
urban tourism; another study was proposed by Yin et al. [38]. Zhang et al. [39] studied the
impact of HSR on tourism mobility and the value of tourist firms. Zhou et al. [40] studied
the effects of HSR on regional tourism economies in China. Campa et al. [41], on the other
hand, studied the impact of HSR on tourism in both Spain and China.
The impacts of HSR services on tourism have been extensively studied in Spain.
Pagliara et al. [42] studied the impact on tourism for the Madrid case study, while Albalate
and Fageda [43] and Albalate et al. [44] studied the impact on tourism more generally.
Two other works have been proposed by Guirao and Campa [45] and Guirao et al. [46].
The main studies referring to Italy are those by Pagliara et al. [47] and Pagliara and
Mauriello [48], who studied the impact of HSR on tourism in Italy through statistical anal-
ysis.
Masson and Petiot [49] examined the impact on tourism attractiveness in a specific
case study: the line between Perpignan (France) and Barcelona (Spain).
Other studies [50–52] investigated the potentialities of HSR for the tourism develop-
ment of regions. Recently, also the Italian Minister of Culture Heritage indicated the use
of HSR connections as a factor in revamping tourism after the pandemic event, especially
in South Italy [53].
The research methodology adopted in this work involved the following main phases:
(1) Identification of the variables that can influence the choice of a tourist destination
and data collection; in particular, three types of variables were identified: (a) varia-
bles related to tourism supply; (b) variables related to accessibility; (c) other variables

Sustainability 2022, 14, 13818 6 of 35
that can influence the choice of destination (commercial activities, being a regional
capital, etc.).
(2) Specification and calibration of multiple linear regression models capable of relating
data on tourist attractiveness to the variables identified above. In this phase, the
goodness of the models will be assessed through statistical tests, also to verify
whether the assumption of linearity between dependent and independent variables
is valid.
(3) Analysis of the results obtained and verification of the performance of the models by
means of sensitivity analysis and an application to a case study.
These steps were preceded by a comprehensive analysis of the state-of-the-art.
In this paper, two multiple linear regression models are proposed to identify the
main variables influencing tourist mobility in Italy; in addition to accessibility variables,
including the presence of HSR services, data on the quantity of cultural heritage are con-
sidered in the model. As is shown in the following sections, of all parameters referring to
accessibility, only the one related to HSR services was significant; other parameters, such
as distances from airports or other municipalities, instead, were not statistically significant
in explaining tourist flows.
To our best knowledge, the proposed approach was not proposed before in Italy, and
similar studies are not available. Indeed, the studies available in the literature refer to the
evolution of tourism following the implementation of new HSR services, but without
providing models or quantitative methods capable of relating the variables of tourist at-
tractiveness to the presence of rail links.
3. Data
Various data sources were used in this study. The main tourism data are taken from
ISTAT (Italian Institute of Statistics) and quantify monthly arrivals and presences, classi-
fied by the origin and category of accommodation (hotel and non-hotel). These data were
available at the regional, provincial, and municipal levels. Here, ‘arrivals’ correspond to
the registration of customers in the accommodation facility, while ‘presences’ correspond
to the total number of nights spent in a facility; therefore, in this study, the term ‘presence’
is equivalent to the term ‘overnight stay’; in the following, we use the term ‘presence’ to
be congruent with the ISTAT terminology. In the development of this work, the data on
tourist movements refer to 2018, so that they are not affected by the COVID-19 pandemic
event.
Overall, in Italy, there were 128.1 million arrivals and 428.8 million presences, with
an average stay of 3.35 nights. The regional data on arrivals and presences are reported in
Table 1, while Table 2 shows the same data with reference to the provinces of the regional
capitals. It can be seen that Veneto, Lombardy, Tuscany, and Lazio are the regions with
the most arrivals, while the provinces with the most arrivals are Rome, Venice, Milan, and
Florence, with an obvious correlation with the attractiveness of the capital cities.
On the other hand, the most attractive regions in terms of presence are Veneto, Tren-
tino-Alto Adige, Tuscany, and Emilia-Romagna and the provinces Venice, Rome, Trento,
and Milan. The difference between regional arrivals and presences, clearly linked to the
average length of stay, is related to the type of holiday, often weekly, in Trentino-Alto
Adige (mainly in winter periods) and Emilia-Romagna (mainly in summer periods).
Table 3 reports the data on arrivals and presences for the 111 Italian provincial capi-
tals, on which the models have been specified and calibrated. The cities of Rome, Milan,
and Venice have over 5 million arrivals and the same cities, with the addition of Florence,
have over 10 million presences per year.

Sustainability 2022, 14, 13818 7 of 35
Table 1. Arrivals and presences in accommodation facilities in Italian regions [54].
Region Arrivals Region Presences
Veneto 19,563,348 Veneto 69,229,094
Lombardy 16,757,628 Trentino-Alto Adige 51,416,000
Tuscany 14,188,009 Tuscany 47,618,085
Lazio 12,575,617 Emilia-Romagna 40,647,799
Trentino-Alto Adige 11,925,777 Lombardy 39,115,354
Emilia-Romagna 11,458,497 Lazio 36,684,847
Campania 6,234,863 Campania 21,689,412
Piemonte 5,276,117 Apulia 15,197,186
Sicily 4,998,055 Liguria 15,183,243
Liguria 4,718,832 Sicily 15,135,259
Apulia 4,065,979 Piemonte 15,100,768
Sardinia 3,280,894 Sardinia 14,940,111
Friuli-Venezia Giulia 2,610,097 Marche 9,656,538
Umbria 2,436,857 Calabria 9,277,810
Marche 2,256,564 Friuli-Venezia Giulia 9,022,550
Calabria 1,825,863 Abruzzo 6,335,072
Abruzzo 1,643,087 Umbria 5,937,298
Aosta Valley 1,254,191 Aosta Valley 3,606,289
Basilicata 892,087 Basilicata 2,603,622
Molise 138,570 Molise 448,600
Table 2. Arrivals and presences in accommodation facilities in main provinces [54].
Province Arrivals Province Presences
Rome 11,131,197 Venice 36,628,413
Venice 9,677,150 Rome 32,245,018
Milan 7,718,958 Trento 18,156,000
Florence 5,245,117 Milan 15,717,859
Trento 4,415,851 Florence 15,281,325
Naples 4,149,784 Naples 14,199,255
Turin 2,505,985 Turin 7,248,575
Bologna 2,372,172 Perugia 5,099,833
Perugia 2,044,661 Bologna 4,729,192
Genoa 1,663,121 Genoa 4,055,435
Aosta 1,254,191 Aosta 3,606,289
Palermo 1,138,322 Palermo 3,286,743
Bari 1,096,477 Ancona 2,681,080
Ancona 754,777 Bari 2,475,938
Trieste 513,529 Catanzaro 1,524,800
Cagliari 460,221 Cagliari 1,463,800
L’Aquila 388,955 Trieste 1,188,103
Catanzaro 340,207 L’Aquila 919,851
Potenza 277,562 Potenza 743,220
Campobasso 101,579 Campobasso 363,210
We underline that the data used does not allow, at this territorial scale, to distinguish
tourist trips from those for other reasons (work, business, study, etc.) and does not include
stays in holiday homes or those trips that do not include a stay in an accommodation

Sustainability 2022, 14, 13818 8 of 35
facility (one-day tourist visits, stays with relatives or friends, etc.); despite all these limi-
tations, we believe that these data are the best available for the analyses we wish to con-
duct.
On the supply side, the accommodation establishments (see Tables 4 and 5) show the
clear prevalence of Veneto and the Province of Venice, decidedly higher also than Lazio
and the Province of Rome.
Data on supply have not been used as possible explanatory variables in our models,
since there is a direct relationship between supply and demand (supply increases where
there is more demand) that could invalidate the modelling analysis aimed at identifying
the other variables that can influence tourist flows.
Table 3. Arrivals and presences in the provincial capitals [54].
City Arrivals Presences City Arrivals Presences City Arrivals Presences
Agrigento 172,984 267,359 Foggia 56,170 110,233 Pistoia 64,806 146,976
Alessandria 75,018 151,947 Forlì 108,429 224,361 Pordenone 58,238 135,653
Ancona 148,582 349,187 Frosinone 17,207 35,843 Potenza 37,526 66,897
Andria 19,045 33,947 Genoa 905,829 1,907,159 Prato 220,120 427,121
Aosta 96,542 190,356 Gorizia 29,987 69,111 Ragusa 156,569 514,334
Arezzo 229,458 434,418 Grosseto 249,998 1,120,975 Ravenna 621,961 2,744,504
Ascoli Pic. 35,946 78,574 Imperia 65,455 213,928 Reggio Cal. 77,631 186,424
Asti 46,461 95,349 Isernia 10,696 25,398 Reggio Emil. 199,571 367,647
Avellino 17,911 36,301 La Spezia 234,596 506,269 Rieti 28,383 68,260
Bari 446,394 838,627 L’Aquila 69,378 129,211 Rimini 1,856,268 7,460,300
Barletta 42,545 96,165 Latina 60,943 177,546 Rome 9,771,745 28,992,098
Belluno 60,526 169,303 Lecce 265,301 697,291 Rovigo 50,417 97,853
Benevento 36,252 80,144 Lecco 41,945 101,555 Salerno 252,455 621,362
Bergamo 350,418 636,535 Livorno 164,382 337,434 Sassari 70,487 144,404
Biella 44,465 110,423 Lodi 21,348 53,200 Savona 100,986 213,191
Bologna 1,543,053 3,059,546 Lucca 241,168 505,880 Siena 509,650 1,056,456
Bolzano 337,366 692,409 Macerata 40,962 221,122 Siracusa 253,732 749,719
Brescia 276,848 590,988 Mantua 124,472 214,375 Sondrio 14,434 25,645
Brindisi 77,227 159,399 Massa 192,668 834,724 Taranto 89,757 258,619
Cagliari 256,533 573,579 Matera 344,813 547,530 Teramo 21,373 64,449
Caltanissetta 27,164 104,567 Messina 36,342 83,604 Terni 95,998 209,827
Campobasso 13,203 23,237 Milan 5,695,214 12,058,835 Turin 1,290,390 3,800,003
Carbonia 11,024 31,997 Modena 270,411 571,425 Trani 48,023 94,761
Caserta 137,709 275,494 Monza 95,488 229,490 Trapani 72,211 173,769
Catania 474,025 975,888 Naples 1,376,589 3,684,905 Trento 360,388 1,016,951
Catanzaro 53,875 153,539 Novara 61,373 144,790 Treviso 159,924 332,341
Cesena 81,801 156,232 Nuoro 27,135 57,811 Trieste 414,003 929,492
Chieti 42,703 142,345 Oristano 68,568 147,137 Udine 210,598 389,112
Como 344,675 708,510 Padua 710,774 1,650,362 Urbino 77,951 487,446
Cosenza 58,823 105,306 Palermo 676,652 1,454,795 Varese 118,315 239,815
Cremona 70,569 136,761 Parma 386,160 735,127 Venice 5,255,499 12,118,298
Crotone 25,740 135,517 Pavia 57,577 110,439 Verbania 199,176 914,556
Cuneo 46,831 105,640 Perugia 425,875 959,070 Vercelli 25,626 82,678
Enna 36,529 62,901 Pesaro 228,445 776,171 Verona 1,198,279 2,495,943
Fermo 56,934 420,468 Pescara 143,025 256,164 Vibo Valen. 19,111 59,023
Ferrara 248,146 450,436 Piacenza 156,715 302,724 Vicenza 271,381 619,810
Florence 3,909,073 10,592,202 Pisa 782,288 1,882,097 Viterbo 88,661 201,896

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Table 4. Accommodation facilities in Italian regions [54].
Region Accommodation Facilities
Veneto 72,363
Lazio 22,177
Emilia-Romagna 15,950
Tuscany 14,376
Trentino-Alto Adige 13,622
Lombardy 9845
Friuli-Venezia Giulia 7689
Apulia 7418
Campania 7185
Sicily 7155
Piemonte 7066
Marche 6935
Sardinia 5242
Liguria 5176
Umbria 4208
Calabria 3512
Abruzzo 3028
Basilicata 1409
Aosta Valley 1270
Molise 515
Table 5. Accommodation facilities in main provinces [54].
Province Accommodation Facilities
Venice 41,906
Rome 19,126
Naples 3453
Perugia 3417
Trento 3330
Florence 3312
Bologna 2177
Milan 2068
Ancona 2049
Turin 2006
Bari 1723
Genoa 1299
Aosta 1270
Palermo 1192
L’Aquila 852
Trieste 832
Cagliari 735
Catanzaro 545
Potenza 544
Campobasso 374
Once the dependent variables had been identified and the corresponding data col-
lected, the possible explanatory (or independent) variables were examined; these varia-
bles are the factors that could influence the choice of a touristic destination. Five categories
of variables have been identified:
(a) Variables related to the supply of historical/cultural assets:

Sustainability 2022, 14, 13818 10 of 35
1. Number of state cultural sites [55];
2. Number of cultural heritage items [56];
3. Employees in libraries, archives, museums, and other cultural activities [57];
4. Consistency of historic urban fabric (elaboration on data) [58].
(b) Variables related to the supply of entertainment/amusement activities:
1. Employees in creative, artistic, and entertainment activities [57];
1. Employees in leisure and entertainment activities [57].
(c) Variables related to the supply of commercial activities:
1. Employees in retail trade (excluding motor vehicles and motorbikes) [57].
(d) Accessibility variables:
1. Number of direct runs on high-speed rail services (based on 2018 data);
2. Distance from Leonardo da Vinci airport in Rome (main Italian hub);
3. Distance from the nearest international airport;
4. Population-weighted road accessibility;
5. Total road travel time to all other possible destinations;
6. Total road travel distance to all other possible destinations.
(e) Importance variables:
1. Dummy variable (0/1) indicating the regional capital.
Not all variables refer to the same year. The most recent data on employees date back
to the last census, which is carried out every ten years, but there are no better or more
reliable statistical sources. On the other hand, the data on State places of culture and the
stock of cultural assets, although referring to different years and before 2018, can be con-
sidered valid because the variation of these numbers over the years is negligible.
The following subsections describe the sources of the data and how they were ob-
tained or derived.
3.1. Variables Related to the Supply of Historical/Cultural Assets
The number of cultural sites is a figure taken from [55] and refers to fortified archi-
tecture, archaeological areas, historical monuments, monuments of industrial archaeol-
ogy, funerary monuments, archives and libraries, churches and places of worship, villas
and palaces, archaeological parks, museums and galleries, parks and gardens. Only those
under state jurisdiction and management are considered, and therefore, this variable does
not include all possible cultural goods. This variable is indicated as scsi, where i indicates
the city.
The same source, but with reference to 2017 [56], provides the total number of cul-
tural assets, understood as architectural assets, archaeological assets, parks, and gardens.
This variable is indicated with tchi.
The data on employees in libraries, archives, museums, and other cultural activities
are taken from the ISTAT census [57]; clearly, the number of employees in this sector is
assumed to be a proxy for the supply of the same type of activity to tourists. This variable
is indicated with musi.
The size of the historical urban fabric was estimated from ISTAT data [58] by calcu-
lating the percentage of houses built before 1919. This variable is indicated with huci.
The values of these variables for the provincial capitals are shown in Table A1 in
Appendix A.
3.2. Variables Related to the Supply of Entertainment/Amusement Activities
The data on employees in creative, artistic, and entertainment activities and employ-
ees in recreational and leisure activities are taken from the ISTAT census [57]. In addition,
in this case, it is assumed that these data represent a proxy for the supply of this type of

Sustainability 2022, 14, 13818 11 of 35
activity on the territory. The values for the provincial capitals are reported in Table A2 in
Appendix A, and the variables are indicated, respectively, by acei and reei.
3.3. Variables Related to the Supply of Commercial Activities
The data on retail trade employees (excluding motor vehicles and motorbikes) are
taken from the ISTAT census [57] and are assumed to be a proxy for the commercial offer
in the territory. The values for the Provincial capitals are reported in Table A3 in Appendix
A. This variable is indicated with reti.
3.4. Accessibility Variables
The tourist accessibility of a place, particularly a city, is determined by several factors
depending on the infrastructures and transport services available. The data source or cal-
culation methods for these variables are described below.
3.4.1. Number of Direct Runs on High-Speed Rail Services
This variable indicates the number of runs of Italian high-speed lines. The data refer
to the number of runs of this type of service arriving/departing from the station of the
municipality; for some municipalities, this value is zero, if not served by this type of ser-
vice. This variable is indicated with hsri.
3.4.2. Distance from Rome’s Leonardo da Vinci Airport (Italy’s Main Hub)
The calculation of this variable, as well as all the following variables based on times
or distances, required the construction of a graph of the national road network. This graph
was implemented starting from the ‘OpenStreetMap’ database, correcting some connec-
tion errors and considering only the roads of the main network: all motorways; all primary
roads with separated carriageways and their ramps; all main trunk roads (typically state
roads and regional roads); some secondary roads necessary to ensure the full connection
of the network.
Overall, this model represents 202,628 km of roads; Table 6 reports the extension of
the network, while Figure 3 shows the overall graph. In addition to the length of the dif-
ferent road sections, which is necessary to calculate the distance between municipalities,
it is also necessary to attribute a speed to each link, to calculate the corresponding travel
time. In this work, we consider the use of the free-flow speeds sufficient, i.e., uncongested
conditions, assuming the values reported in Table 7.
Table 6. Extension of the road network.
Type of Road Total Length Links
Motorways 14,592 23,463
Motorway ramps 2735 15,913
Primary roads 30,216 140,917
Primary road ramps 1403 16,717
Secondary roads 59,229 217,638
Secondary road ramps 432 7965
Tertiary roads 66,504 218,655
Tertiary road ramp 249 5852
Trunk roads 10,100 27,112
Trunk road ramps 2823 23,978
Other roads 14,345 98,456
Total 202,628 796,674

Sustainability 2022, 14, 13818 12 of 35
Figure 3. Road graph.
With this model, the matrix of times and the matrix of distances between all the mu-
nicipalities were generated; these matrices have a dimension of 8091 × 8091, being 8091
the Italian municipalities according to the 2011 ISTAT surveys. This matrix was simplified
into a 111 × 8091 matrix, considering that the indicators were calculated only for the pro-
vincial capitals.
From this matrix, the variables in question were calculated as:
dAFi = 1/di,Fiumicino ∀i
where:
di,Fiumicino is the distance between municipality i and Leonardo Da Vinci airport in Fium-
icino (hundreds of km).

Sustainability 2022, 14, 13818 13 of 35
Table 7. Free-flow speed.
Type of Road Free-Flow Speed [km/h]
Motorways 120
Motorway ramps 40
Primary roads 90
Primary road ramps 40
Secondary roads 70
Secondary road ramps 30
Tertiary roads 50
Tertiary road ramp 25
Trunk roads 70
Trunk road ramps 30
Other roads 40
3.4.3. Distance from the Nearest International Airport
This variable was calculated as:
dAIi = min (di,AI) ∀i
where:
di,AI is the distance between municipality i and international airport AI (hundreds of km).
The international airports considered, those with the most traffic in each region, are
listed in Table 8.
Table 8. International airports.
Region Airport Municipality
Piemonte Turin–Caselle Caselle Torinese
Aosta Valley - -
Lombardy Milan–Malpensa Ferno
Trentino-Alto Adige Bolzano Bolzano
Veneto Venice–Tessera Venice
Friuli-Venezia Giulia Trieste–Ronchi dei Legionari Ronchi dei Legionari
Liguria Genoa–Sestri Genoa
Emilia-Romagna Bologna–Borgo Panigale Bologna
Tuscany Pisa–San Giusto Pisa
Umbria Perugia Perugia
Marche Ancona–Falconara Falconara Marittima
Lazio Rome–Fiumicino Fiumicino
Abruzzo Pescara Pescara
Molise - -
Campania Naples–Capodichino Naples
Apulia Bari–Palese Bari
Basilicata - -
Calabria Lamezia Terme Lamezia Terme
Sicily Catania–Fontanarossa Catania
Sardinia Cagliari–Elmas Elmas
3.4.4. Population-Weighted Road Accessibility Function Variable
For the calculation of this variable, the ‘gravity-based measures’ model proposed by
Hansen [59] was adopted. The general formulation of the model is as follows:
Ai = ∑j Wjβ f(ci,j, α)

Sustainability 2022, 14, 13818 14 of 35
where:
Ai is the indicator measuring the accessibility of zone i;
Wjβ is a measure of the importance of zone j, based on activities, services, population,
and so on;
β is a coefficient of the model;
f(ci,j, α) is an impedance function, based on generalised cost, distance, etc., between
zone i and zone j.
We have calculated the accessibility indicator as:
Ai = ∑j inhj ti,j−1
where:
inhj is the number of inhabitants in the municipality j;
ti,j is the travel time in hours between municipality i and municipality j.
3.4.5. Total Travel Time by Road with All Other Possible Destinations
For each provincial capital, i, we calculated the total travel time (h x 10−3) from all
other Italian municipalities and calculated the variable as the reciprocal, with the follow-
ing formula:
ttoti = 1/∑j ti,j ∀i
3.4.6. Total Road Distance to All Other Possible Destinations
For each provincial capital, i, we calculated the total travel distance (km x 10−5) from
all other Italian municipalities, based on the implemented graph, and calculated the vari-
able as the reciprocal, with the following formula:
dtoti = 1/∑j di,j ∀i
where di,j is the distance between capital city i and municipality j.
The values of all accessibility variables are reported in Table A4 in Appendix A.
3.5. Importance Variables
We consider a dummy variable indicating whether the city is a regional capital (1) or
not (0). The values of these variables, indicated with capi, are reported in Table A5 in Ap-
pendix A.
4. Regression Models
The impact and significance of the explanatory variables on the tourism phenomenon
are assessed with multiple linear regression models. These models relate the dependent
variables (in our case, presences and arrivals) to the explanatory variables (independent)
that may affect them.
Linear regression models take the following general form:
Y = β0 + β1 X1 + β2 X2 + ... + βk Xk + ... + βm Xm
where:
Y is the expected value of the dependent variable;
β0 is a coefficient of the model, which does not depend on the independent variables
(intercept of the regression line);
βk are the coefficients of the model, which together with β0, have to be calibrated;
Xk are the independent variables.
Any model must be specified and calibrated. The specification phase consists of de-
fining which of the independent variables can be included in the model; the calibration
phase consists of finding the coefficient values that can best reproduce the observed val-
ues of the independent variables for that specification.

Sustainability 2022, 14, 13818 15 of 35
The observed data of the independent variables are denoted by yi and ordered in a
vector y; the vector y has as many elements as the number of municipalities on which we
are going to calibrate the model (in our case, 111 municipalities). The values that the in-
dependent variables assume for each observation are also called ‘predictors’ and indicated
with xi,k, where i represents the provincial capital and k the independent variable; these
values can be ordered in a matrix, x, which has as many rows as the number of cities and
as many columns as the number of independent variables plus one (coefficient β0: the el-
ements of the first column of the matrix are equal to 1). The coefficients βk can be ordered
in a vector β that has as many elements as the number of coefficients. Finally, we need to
add the vector of statistical errors, ε, which has as many elements εi as the number of cities.
With these notations, it is possible to write:
yi = β0 + ∑k βk xi,k + εi ∀i
or, in matrix form:
y = x β + ε
This formula represents, in short, the relationship between the observed data, y, and
the independent variables, x. The calibration of the model consists in searching for the
vector of coefficients, β, that minimises the vector of statistical errors, ε; in the theoretical
case in which all statistical errors are equal to 0, the model would perfectly reproduce all
the observed data.
If we denote by xi the i-th row of the matrix x, we can write:
yi = xi β + εi
whence:
εi = yi − xi β
The optimal values of the coefficients can be obtained using the generalised least
squares method, which minimises the sum of squares of the statistical errors; the corre-
sponding optimisation model can be written as follows:
βopt = Arg β min ∑i (yi − xi β)2
The ability of a model to reproduce observed data, and thus its goodness, is measured
by several indicators; one of them is the coefficient of determination, R2, which is calcu-
lated as:
R2 = 1 − (∑i (yi − xi β)2)/(∑i (yi − y^)2)
where y^ is the average of the yi values; this indicator measures the ability of the yi varia-
bles to explain the model, and the closer its value to 1 (statistical errors equal to 0 and
perfect reproducibility of the observed phenomenon), the greater the goodness of the
model.
The coefficient of determination always increases (or at least does not decrease) as
the number of explanatory variables increases. To avoid this problem, it is possible to use
the adjusted coefficient of determination, R2adj, which penalises the inclusion of variables
that are not necessary to explain the phenomenon; this indicator is calculated as:
R2adj = 1 − ((n − 1)/(n − p − 1))·(1 − R2)
where n is the number of observations and p is the number of degrees of freedom (df) in
the model. Clearly, as the number of explanatory variables, i.e., degrees of freedom, in-
creases, the value of R2adj decreases with respect to the value of R2, the more so as there are
few observations. In our case, with 111 observed data, we do not expect a great difference
between the two values, which, in any case, will be calculated to verify the goodness of
the model.

Sustainability 2022, 14, 13818 16 of 35
The coefficient of determination cannot, however, be the unique indicator to evaluate
the goodness of a model. Indeed, it does not always decrease (it usually increases) with
the number of variables k, even if some of them are not useful to explain the phenomenon.
The other indicators that must be used to evaluate the model are the hypothesis tests that
are able to measure whether the parameters adopted in the model are indeed significant
to reproduce the phenomenon. In this study, we use the F-test, obtained from the analysis
of variance, and the t-test, concerning the significance of each independent variable. We
will assume that a model is acceptable if the significance F is close to 0 (at least < 0.05) and
if the t-test of each coefficient βk is higher [lower] than t95 [−t95] for positive [negative] βk,
where t95 is the value of the t-student distribution corresponding to the degrees of freedom
(df) of the model with 95% confidence. The degree of freedom of a model is equal to the
number of independent variables xk of the model. The values of t95 for the different degrees
of freedom (1 to 10) are reported in Table 9.
Table 9. Values of t95 as a function of model degrees of freedom.
df 1 2 3 4 5 6 7 8 9 10
t95 6.314 2.920 2.353 2.132 2.015 1.943 1.895 1.860 1.833 1.812
The specification and calibration procedure used in this study is based on a trial-and-
error approach, based on the values of the Pearson correlation coefficients between the
dependent variable and one of the independent variables. The correlation coefficient is
calculated as the ratio of the covariance between two variables, σxy, and the product of the
standard deviations, σx and σy:
ρxy = σxy/σxσy
This coefficient can assume values between −1 and 1; the higher the absolute value of
the index, the more the two variables are correlated with each other, either positively or
negatively, depending on the sign. The value of the correlation index indicates the possi-
bility that the independent variable has a significant influence, within the model, on the
dependent variable; therefore, in the trial-and-error procedure, variables with a higher
absolute correlation index will be tested first, verifying if the sign is physically admissible.
After a variable has been introduced, the model will be calibrated, and it will be checked
whether the inserted variable is significant. If it is, the variable is kept in the model and
another one is added; if it is not, another variable is tried. To be valid, a model must have
all the independent variables significant, i.e., they must respect the minimum values of
the indicator t-test, and a sign of the corresponding coefficient that has a physical mean-
ing; among all the calibrated models that respect these conditions, those with the greatest
coefficient of determination are preferable. This first phase leads to a model with all sig-
nificant variables and with a coefficient of determination greater than all the other models
tested; from this model, we try to introduce other variables and, then, to eliminate a vari-
able and replace it with another, to test other possible combinations.
In Table 10, we report the correlation coefficients of each explanatory variable with
the independent variables in decreasing order of value.
All specified and calibrated models are summarised in Table 11, for arrivals, and in
Table 12, for presences, where, for each model, the considered variables, the R2 and R2adj
indicators, the significance F, the model coefficients and, for each variable, the t-test value,
whose limit value is also reported, and the validity or not of the model are indicated.
Overall, 18 models for estimating arrivals and 19 models for estimating presences
were calibrated; of these models, five models for estimating arrivals and five models for
estimating presences were valid in terms of significance and sign of the coefficients. At
the end of the procedure, model no. 16 for estimating arrivals and model no. 18 for esti-
mating presences were identified as the best. These models have the maximum values of
R2 and R2adj, and comply with all the significance tests. The values of the coefficients of

Sustainability 2022, 14, 13818 17 of 35
determination are sufficiently high in both cases (0.909 for arrivals and 0.885 for
presences). It is important to note that in both models, the only accessibility variable found
to be significant is the one related to high-speed rail services, hsri. The other accessibility
variables were not statistically significant.
Figures 4 and 5 show the scatter diagrams comparing the actual and estimated val-
ues.
The best models are formulated as follows:
ARRi = −102072.43 + 385.52 acei + 16.23 reti + 17327.57 hsri + 266.53 tchi + 1322.12 musi (1)
PREi = −190198.61+ 2085.68 acei + 695.11 tchi + 3616.30 musi + 36022.00 hsri (2)
The analysis of these models highlights the following aspects:
(a) The intercept assumes a negative value. This property permits the models to be used
only for overall evaluations of the entire set of municipalities (remember that, having
used the generalised least squares method, the sum of the values estimated by the
model for all the municipalities is equal to the sum of the true values). The application
to a specific municipality could give implausible values, and for municipalities with
less tourist importance, negative values.
(b) The variables linked to creative, artistic, and entertainment activities, total cultural
assets, the presence of libraries, museums and other cultural activities, and direct
rides on high-speed services always appear in both models. In the arrivals model, the
variable related to commercial activities is also significant, while it is not statistically
significant for presences. This indicates that commercial activities have a greater in-
fluence on shorter-duration trips than longer ones. In all cases, the variables closely
linked to the tourist offer of the place of destination are significant.
(c) Among the accessibility variables, only the one representing high-speed rail services
is statistically significant in estimating arrivals and presences. The other accessibility
variables, at least for the provincial capitals, are not influential.
Table 10. Correlation coefficients.
Correlation Coefficients with the
Independent Variable ‘Arrivals’
Correlation Coefficients with the
Independent Variable ‘Presences’
Variable Correlation coefficient Variable Correlation coefficient
acei 0.896 acei 0.895
reei 0.862 reei 0.859
reti 0.853 scsi 0.810
hsri 0.831 reti 0.807
tchi 0.814 tchi 0.802
scsi 0.811 musi 0.795
musi 0.786 hsri 0.792
dAFi 0.593 dAFi 0.659
A
i 0.487
A
i 0.463
capi 0.459 capi 0.423
dAIi 0.401 dAIi 0.378
ttoti 0.167 ttoti 0.157
dtoti 0.160 dtoti 0.151
huci 0.042 huci 0.014

Sustainability 2022, 14, 13818 18 of 35
Table 11. Models to estimate arrivals.
Order Variable\Model n. 1 2 3 4 5 6
1 acei x x x x x x
2 reei x
3 reti x
4 hsri x x x
5 tchi x x
6 scsi x
7 musi
8 dAFi
9 Ai
10 capi
11 dAIi
12 ttoti
13 dtoti
14 huci
df 1 2 2 2 3 4
R2 0.802 0.805 0.809 0.843 0.882 0.882
R2adj 0.800 0.801 0.806 0.841 0.879 0.878
Significance F 3.95 × 10−40 5.26 × 10−39 1.41 × 10−39 3.29 × 10−44 1.71 × 10−49 2.61 × 10−48
Intercept 119335.88 81652.36 74442.07 25967.88 −109065.03 −100210.31
Coeff. 1 1582.96 1328.28 1244.25 1123.73 895.11 910.88
Coeff. 2 587.88 19.31 22463.20 15465.48 16185.66
Coeff. 3 424.05 453.40
Coeff. 4 −2410.34
Coeff. 5
Coeff. 6
t95 6.314 2.920 2.920 2.920 2.353 2.132
t-test_1 21.013 5.790 6.781 10.295 8.713 8.522
t-test_2 1.175 2.019 5.344 4.015 3.972
t-test_3 5.918 5.086
t-test_4 −0.557
t-test_5
t-test_6
Significant Yes No No Yes Yes No
Sign Yes Yes Yes Yes Yes No
Valid Yes No No Yes Yes No

Sustainability 2022, 14, 13818 19 of 35
Order Variable\Model n. 7 8 9 10 11 12
1 acei x x x x x x
2 reei
3 reti
4 hsri x x x x x x
5 tchi x x x x x x
6 scsi
7 musi x x x x x x
8 dAFi x
9 Ai x
10 capi x
11 dAIi x
12 ttoti x
13 dtoti
14 huci
df 4 5 5 5 5 5
R2 0.904 0.905 0.904 0.905 0.904 0.904
R2adj 0.901 0.900 0.900 0.900 0.900 0.900
Significance F 4.40 × 10−53 6.27 × 10−52 7.34 × 10−52 6.67 × 10−52 7.41 × 10−52 7.26 × 10−52
Intercept −70162.32 −48702.83 −50856.26 −67253.63 −69240.09 −74550.90
Coeff. 1 643.63 676.99 646.74 636.86 642.07 643.10
Coeff. 2 19847.82 19229.64 19966.26 20251.14 19896.88 19886.56
Coeff. 3 255.53 258.35 257.74 262.26 257.34 257.22
Coeff. 4 1259.62 1290.13 1250.42 1276.81 1261.47 1257.56
Coeff. 5 −82544.79 −1437.26 −53521.53 −596.02 79818.04
Coeff. 6
t95 2.132 2.015 2.015 2.015 2.015 2.015
t-test_1 6.090 5.620 5.985 5.948 5.940 6.056
t-test_2 5.526 5.120 5.413 5.462 5.431 5.505
t-test_3 3.497 3.517 3.446 3.509 3.340 3.484
t-test_4 4.987 4.987 4.798 4.984 4.949 4.953
t-test_5 −0.583 −0.155 −0.467 −0.078 0.216
t-test_6
Significant Yes No No No No No
Sign Yes No No No No Yes
Valid Yes No No No No No

Sustainability 2022, 14, 13818 20 of 35
Order Variable\Model n. 13 14 15 16 17 18
1 acei x x x x x x
2 reei x
3 reti x x x
4 hsri x x x x x x
5 tchi x x x x x x
6 scsi x
7 musi x x x x x x
8 dAFi x
9 Ai
10 capi
11 dAIi
12 ttoti
13 dtoti x
14 huci x
df 5 5 5 5 6 6
R2 0.904 0.905 0.908 0.909 0.909 0.909
R2adj 0.900 0.901 0.903 0.905 0.904 0.904
Significance F 7.26 × 10−52 5.00 × 10−52 1.10 × 10−52 6.06 × 10−53 8.32 × 10−52 7.62 × 10−52
Intercept −74550.90 −117824.59 −111915.76 −102072.43 −95802.97 −133145.62
Coeff. 1 643.10 655.79 335.07 385.52 397.19 303.96
Coeff. 2 19886.56 20012.16 714.33 16.23 16.29 18.82
Coeff. 3 257.22 230.40 18753.19 17327.57 17833.27 17674.16
Coeff. 4 1257.56 1296.14 255.40 266.53 288.79 264.87
Coeff. 5 79818.04 5288.22 1347.49 1322.12 −1774.62 1295.14
Coeff. 6 1317.71 99949.32
t95 2.015 2.015 2.015 2.015 1.943 1.943
t-test_1 6.056 6.148 1.786 2.505 2.537 1.502
t-test_2 5.505 5.559 1.978 2.269 2.268 2.270
t-test_3 3.484 2.940 5.229 4.690 4.612 4.717
t-test_4 4.953 5.061 3.543 3.710 3.328 3.673
t-test_5 0.216 0.892 5.324 5.303 −0.461 5.104
t-test_6 5.262 0.623
Significant No No No Yes No No
Sign Yes Yes Yes Yes No Yes
Valid No No No Yes No No

Sustainability 2022, 14, 13818 21 of 35
Table 12. Models to estimate presences.
Order Variable\Model n. 1 2 3 4 5 6
1 acei x x x x x x
2 reei x
3 scsi x x x x
4 reti
x
5 tchi
x
6 musi
x
7 hsri
8 dAFi
9 Ai
10 capi
11 dAIi
12 ttoti
13 dtoti
14 huci
df 1 2 2 3 3 3
R2 0.800 0.802 0.832 0.834 0.856 0.868
R2adj 0.798 0.798 0.829 0.829 0.852 0.864
Significance F 6.55 × 10−40 1.06 × 10−38 1.36 × 10−42 1.46 × 10−41 6.33 × 10−45 6.73 × 10−47
Intercept 251819.80 164437.06 −194347.93 −142672.93 −311053.50 −131440.04
Coeff. 1 4320.20 3729.62 3239.32 3670.73 3025.87 2491.73
Coeff. 2 1363.22 46339.90 47182.15 12875.49 37193.78
Coeff. 3
−25.72 1134.04 3909.62
Coeff. 4
Coeff. 5
t95 6.314 2.920 2.920 2.353 2.353 2.353
t-test_1 20.891 5.911 10.634 7.162 10.520 8.175
t-test_2 0.991 4.545 4.615 1.044 4.025
t-test_3
−1.046 4.238 5.390
t-test_4
t-test_5
Significant Yes No Yes No No Yes
Sign Yes Yes Yes No Yes Yes
Valid Yes No Yes No No Yes

Sustainability 2022, 14, 13818 22 of 35
Order Variable\Model n. 7 8 9 10 11 12
1 acei x x x x x x
2 reei
3 scsi x x x x x x
4 reti
5 tchi
6 musi x x x x x x
7 hsri x x x x x x
8 dAFi x
9 Ai x
10 capi
x
11 dAIi
x
12 ttoti
x
13 dtoti
14 huci
df 4 5 5 5 5 5
R2 0.880 0.883 0.880 0.882 0.880 0.881
R2adj 0.875 0.877 0.874 0.876 0.874 0.875
Significance F 7.43 × 10−48 3.00 × 10−47 1.11 × 10−46 4.46 × 10−47 1.11 × 10−46 6.83 × 10−47
Intercept −147521.11 −320838.73 −142610.04 −136636.34 −148655.35 −559850.42
Coeff. 1 1969.65 1701.92 1970.38 1874.88 1971.85 1989.77
Coeff. 2 22547.13 20257.72 22560.66 27215.72 22472.83 21104.26
Coeff. 3 4334.67 4076.01 4333.01 4509.78 4330.52 4465.27
Coeff. 4 37138.76 43157.54 37171.69 39546.60 37082.77 35183.38
Coeff. 5 703651.96 −360.66 −491020.93 683.10 7719931.11
t95 2.132 2.015 2.015 2.015 2.015 2.015
t-test_1 5.909 4.608 5.802 5.526 5.748 5.958
t-test_2 2.269 2.034 2.247 2.597 2.183 2.101
t-test_3 6.130 5.665 6.000 6.298 5.979 6.206
t-test_4 3.249 3.618 3.159 3.432 3.186 3.033
t-test_5 1.629 −0.013 −1.357 0.030 0.986
Significant Yes No No No No No
Sign Yes Yes No No Yes Yes
Valid Yes No No No No No

Sustainability 2022, 14, 13818 23 of 35
Order Variable\Model n. 13 14 15 16 17 18 19
1 acei x x x x x x x
2 reei
x
3 scsi x x x x x
4 reti
x x
5 tchi
x x x
6 musi x x x x x x x
7 hsri x x x x x x x
8 dAFi
9 Ai
10 capi
11 dAIi
12 ttoti
13 dtoti x
14 huci x
df 5 5 5 5 5 4 5
R2 0.881 0.880 0.884 0.881 0.886 0.885 0.886
R2adj 0.875 0.875 0.878 0.876 0.880 0.881 0.881
Significance F 7.53 × 10−47 9.76 × 10−47 1.92 × 10−47 6.22 × 10−47 9.17 × 10−48 7.48 × 10−49 7.34 × 10−48
Intercept −509712.73 −228911.02 −268157.62 −103344.33 −218173.27 −190198.61 −149673.90
Coeff. 1 1991.25 1999.84 1066.82 2351.66 2030.69 2085.68 2413.48
Coeff. 2 21403.22 20872.86 2095.06 22291.56 7785.26 695.11 −20.61
Coeff. 3 4424.06 4357.63 22283.38 −23.99 597.61 3616.30 681.14
Coeff. 4 35462.46 37500.76 4596.37 4220.73 3636.55 36022.00 3536.93
Coeff. 5 6198042.71 8976.50 34058.84 40696.45 33766.31 39222.63
t95 2.015 2.015 2.015 2.015 2.015 2.132 2.015
t-test_1 5.952 5.884 1.840 4.835 6.189 6.587 5.132
t-test_2 2.134 1.985 1.891 2.245 0.664 3.175 −0.943
t-test_3 6.186 6.127 2.270 −1.078 2.263 4.779 3.103
t-test_4 3.057 3.263 6.453 5.907 4.789 3.347 4.643
t-test_5 0.879 0.501 2.985 3.423 2.985 3.474
Significant No No No No No Yes No
Sign Yes Yes Yes No Yes Yes No
Valid No No No No No Yes No

Sustainability 2022, 14, 13818 24 of 35
Figure 4. Model for estimating arrivals: comparison between real and model data.
Figure 5. Model for estimating presences: comparison between real and model data.
To evaluate the importance of high-speed services with respect to the other factors, a
sensitivity analysis was carried out, increasing the overall values of each variable by 10%

Sustainability 2022, 14, 13818 25 of 35
and evaluating the percentage increase in the number of arrivals and presences. The re-
sults are summarised in Tables 13 and 14.
Table 13. Results of the sensitivity analysis for arrivals.
Total Arrivals Variable Total Arrivals by Model
(+10% of Variable) Percentage Variation
48,758,419
acei 49,623,298 +1.77%
reti 49,918,715 +2.17%
hsri 50,352,555 +3.27%
tchi 50,427,540 +3.42%
musi 49,581,833 +1,69%
Table 14. Results of the sensitivity analysis for presences.
Total Presences Variable Total Presences by Model
(+10% of Variable) Percentage Variation
124,871,320
acei 129,550,341 +3.75%
tchi 129,224,383 +3.49%
musi 127,123,549 +1.80%
hsri 128,185,344 +2.65%
The analysis of these results leads to the following considerations:
• High-speed rail services have an important impact on the flow of arrivals and
presences in accommodation facilities. The elasticity is greater for arrivals, where an
increase of +10% in supply can be estimated as a +3.27% increase in arrivals, while
this value is reduced to +2.65% for presences. In both cases, the values are significant:
for arrivals, the elasticity is second only to that linked to total cultural assets, while
for presences, it is third, being also preceded by creative, artistic, and entertainment
activities.
• A comparison of the model’s elasticities between arrivals and presences shows that
there is practically the same elasticity for the variable on total cultural heritage
(+3.42% arrivals and +3.49% presences), highlighting how this explanatory variable
has more or less the same effect on all stays, regardless of their duration. On the other
hand, creative, artistic, and entertainment activities have a greater elasticity on arri-
vals than on presences, showing a tendency to influence shorter stays more.
• Museums, libraries, and other cultural activities have practically the same elasticity,
as total cultural heritage, on both arrivals and presences (+1.69% arrivals and +1.80%
presences).
• Commercial activities, as already mentioned, show an influence only on arrivals and,
therefore, a greater influence on shorter stays.
From the calibration of these models and analyses, it can be concluded that the im-
pact of high-speed rail services on tourism flows, as measured by arrivals and presences
in accommodation establishments, is significant. For arrivals, the elasticity of the variable
is high, of the same order of magnitude as for the total number of cultural assets. For
presences, it is lower, but still very significant. Another fact to note is that, of all the acces-
sibility variables considered, high-speed services are the only statistically significant.

Sustainability 2022, 14, 13818 26 of 35
5. An Application to a Case Study
The calibrated models were applied to a specific case study, the city of Benevento.
Benevento is a small-medium-sized provincial capital with about 60,000 inhabitants (only
36 out of 111 provincial capitals have fewer residents than Benevento), but it has several
important historical/archaeological sites, including the monumental complex of Santa So-
fia, a UNESCO World Heritage Site, the Arch of Trajan, the Roman Theatre and the Rocca
dei Rettori, as well as several museums and churches of great value. The accessibility of
the city, however, is not as good as the artistic and historical heritage: the railway connec-
tions with the regional capital (Naples) are not efficient and have a modest frequency,
while Trenitalia’s Frecce services connect the city on the Rome-Bari route, for a total of
only 28 runs, as the sum of those arriving and departing.
Currently (2018 data, pre-COVID), annual arrivals in accommodation amount to
36,252 (on average, 99 per day), while presences stand at 80,144 (on average, 220 per day),
with an accommodation supply of 57 establishments for a total of 1039 beds; therefore,
there are about 35 arrivals and 77 presences per bed; for comparison, the city of Naples
has about 87 arrivals and 232 presences per bed.
A new HS railway line is currently under construction, which will serve a Naples-
Benevento-Bari route, with a maximum line speed of 250 k m/h. As it is un der constr uction ,
the frequency of services has not yet been established, but it can be assumed that the ser-
vice will be organised on 12 pairs of daily runs, increasing the service to a total of 40 daily
runs, as the sum of arriving and departing runs.
Assuming the same elasticity as estimated in Section 4, the new services would in-
crease the current services by 42.8% and, therefore, could lead to an increase, other factors
unchanged, of 15.8% in arrivals (+5728) and 11.3% in presences (+9056), increasing the
overall annual occupancy rate from 21.1% to 23.5%.
These results further underline how high-speed railway lines can have a significant
impact on tourist attractiveness.
6. Conclusions
High-speed rail transport systems have proved to be an important tool for spatial
development all over the world. Increased accessibility due to HSR services has a positive
impact on industrial and commercial activities, increases property values, and reduces
emissions of pollutants and greenhouse gases. This paper studied the effects on tourism
in Italy, taking provincial capitals as territorial reference units. The calibrated multiple
linear regression models showed that HSR services are the most important accessibility
factor for tourism and that their impact is significant in terms of arrivals and presence in
accommodation facilities.
We can summarise the results of this study as follows: (a) among all the accessibility
variables, the availability of HSR services has a significant impact on tourism attractive-
ness in Italy; (b) the elasticity analysis showed that the influence of this variable is of the
same magnitude as the variables related to the offer of historical–cultural assets and crea-
tive, artistic and entertainment activities; (c) the application to the case study of Benevento
showed that the presence of HSR services is fundamental for the city’s tourism develop-
ment.
Based on these results, we can reasonably affirm that HSR services represent a stra-
tegic factor for the development and promotion of tourism in Italy. With an equal histor-
ical and cultural offer, the locations better served by rail transport show a higher attrac-
tiveness. This factor should be included in the assessments of policymakers when invest-
ing in HSR systems. Indeed, from a practical point of view, the possibility of forecasting
the impact on tourism of HSR services (and of the infrastructures they use) makes it pos-
sible to make more conscious decisions in the choice of investments, being then able to
derive from the results also obtained the impacts on the socio-economic development of
the territories involved.

Sustainability 2022, 14, 13818 27 of 35
In this regard, further developments of this research will be aimed at extending the
study to municipalities that are not provincial capitals and at considering multimodal
transport accessibility variables, directly considering the interchanges between different
transport systems and their role in improving the sustainable way of enjoying territory.
Author Contributions: Conceptualization, M.G. and R.A.L.R.; methodology, M.G. and R.A.L.R.;
model, M.G.; numerical results, M.G. and R.A.L.R.; resources, M.G.; data curation, M.G. and
R.A.L.R.; writing—original draft preparation, M.G. and R.A.L.R.; writing—review and editing,
M.G. and R.A.L.R. All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: All available data are quoted in the References or available in Appen-
dix A.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
This appendix reports the input data used in the paper.
Table A1. Variables on the supply of historical/cultural assets.
City scsi tchi musi huci City scsi tchi musi huci
Agrigento 7 151 17 5.96 Mantova 14 355 49 22.38
Alessandria 9 135 11 11.37 Massa 3 169 0 9.53
Ancona 13 663 4 9.62 Matera 5 210 9 5.53
Andria 1 90 0 5.55 Messina 8 396 4 7.51
Aosta 2 56 1 8.77 Milan 61 1691 271 8.49
Arezzo 16 520 1 10.39 Modena 29 467 12 11.26
Ascoli Pic. 19 943 46 17.02 Monza 2 127 6 7.43
Asti 9 174 4 14.06 Naples 56 1528 34 20.38
Avellino 5 97 6 1.66 Novara 7 275 1 3.29
Bari 16 641 3 6.32 Nuoro 5 67 51 1.87
Barletta 5 184 1 17.59 Oristano 3 69 39 3.57
Belluno 2 267 2 15.31 Padua 10 1295 1 5.63
Benevento 8 323 0 2.99 Palermo 32 799 1223 5.95
Bergamo 11 653 18 12.69 Parma 27 802 3 12.02
Biella 6 255 4 16.15 Pavia 13 477 5 14.12
Bologna 73 1791 170 12.76 Perugia 33 735 208 9.69
Bolzano 9 97 9 7.98 Pesaro 10 479 1 7.40
Brescia 18 794 3 15.04 Pescara 7 118 5 2.61
Brindisi 5 107 3 4.30 Piacenza 14 652 1 8.36
Cagliari 29 541 96 5.70 Pisa 20 702 27 17.26
Caltanissetta 3 124 0 7.97 Prato 13 210 10 6.74
Campobasso 4 214 6 10.59 Ragusa 2 165 1 5.16
Carbonia 4 270 48 1.22 Ravenna 22 708 7 5.04
Caserta 7 150 0 10.70 Reggio Cal. 20 336 3 0.81
Catania 9 396 7 10.03 Reggio Emil. 13 347 4 7.75
Catanzaro 12 191 0 3.17 Rieti 2 175 0 15.13
Cesena 12 300 1 4.47 Rimini 9 363 4 1.29

Sustainability 2022, 14, 13818 28 of 35
City scsi tchi musi huci City scsi tchi musi huci
Chieti 7 176 0 5.68 Rome 153 6239 1946 3.80
Como 8 300 5 12.13 Rovigo 2 250 13 5.26
Cosenza 7 177 1 11.62 Salerno 14 310 5 8.10
Cremona 8 211 8 18.07 Sassari 7 313 58 8.26
Crotone 6 47 0 1.77 Savona 9 675 0 16.17
Cuneo 8 190 2 11.91 Siena 42 1675 47 27.05
Enna 1 80 1 6.48 Siracusa 11 254 4 3.74
Fermo 5 423 0 11.56 Sondrio 2 75 9 14.34
Ferrara 23 1699 1 13.19 Taranto 5 390 2 2.85
Florence 104 2093 105 23.98 Teramo 6 176 0 17.77
Foggia 7 275 2 1.77 Terni 11 196 13 6.43
Forlì 10 436 6 8.28 Turin 57 1729 53 8.57
Frosinone 1 30 1 1.68 Trani 4 100 0 18.48
Genoa 58 4356 157 20.23 Trapani 1 103 0 7.67
Gorizia 3 356 0 14.87 Trento 2 139 23 12.97
Grosseto 5 322 3 4.17 Treviso 2 876 23 4.67
Imperia 9 365 1 18.42 Trieste 38 1496 20 24.34
Isernia 5 141 0 22.11 Udine 5 474 71 6.72
La Spezia 9 344 24 15.77 Urbino 8 504 0 29.74
L’Aquila 7 690 0 14.04 Varese 6 157 1 11.45
Latina 9 46 3 0.99 Venice 26 3790 1031 20.59
Lecce 10 584 1 5.39 Verbania 1 139 0 22.74
Lecco 7 103 0 20.12 Vercelli 3 199 0 15.51
Livorno 8 283 83 22.23 Verona 22 1711 6 11.48
Lodi 6 165 3 9.85 Vibo Valentia 3 147 3 3.58
Lucca 11 438 11 30.56 Vicenza 8 880 6 9.68
Macerata 4 370 1 12.11 Viterbo 14 487 0 14.35
Table A2. Entertainment/amusement variables.
City acei reei City acei reei City acei reei
Agrigento 18 34 Foggia 30 47 Pistoia 51 23
Alessandria 89 63 Forlì 59 86 Pordenone 111 114
Ancona 73 101 Frosinone 13 40 Potenza 14 87
Andria 14 14 Genoa 425 357 Prato 108 90
Aosta 71 11 Gorizia 42 16 Ragusa 28 56
Arezzo 99 75 Grosseto 47 138 Ravenna 173 331
Ascoli Piceno 36 25 Imperia 19 52 Reggio Cal. 17 81
Asti 25 91 Isernia 4 14 Reggio Emil. 137 133
Avellino 20 50 La Spezia 32 47 Rieti 15 21
Bari 131 83 L’Aquila 52 6 Rimini 193 717
Barletta 14 31 Latina 59 168 Rome 6171 2429
Belluno 24 5 Lecce 45 67 Rovigo 44 39
Benevento 24 53 Lecco 27 38 Salerno 120 167
Bergamo 175 138 Livorno 106 133 Sassari 136 95
Biella 17 31 Lodi 26 14 Savona 85 71
Bologna 700 200 Lucca 124 106 Siena 48 22
Bolzano 106 60 Macerata 33 30 Siracusa 49 68

Sustainability 2022, 14, 13818 29 of 35
City acei reei City acei reei City acei reei
Brescia 486 286 Mantova 48 43 Sondrio 10 24
Brindisi 17 25 Massa 28 212 Taranto 22 40
Cagliari 97 208 Matera 21 30 Teramo 23 36
Caltanissetta 10 34 Messina 47 109 Terni 41 105
Campobasso 16 46 Milan 3828 2098 Turin 941 634
Carbonia 3 3 Modena 266 396 Trani 10 19
Caserta 45 65 Monza 144 39 Trapani 7 37
Catania 118 271 Naples 890 797 Trento 371 59
Catanzaro 14 81 Novara 64 67 Treviso 105 132
Cesena 146 100 Nuoro 16 6 Trieste 185 59
Chieti 19 14 Oristano 33 34 Udine 84 338
Como 52 48 Padua 209 108 Urbino 38 4
Cosenza 26 24 Palermo 370 302 Varese 66 44
Cremona 104 81 Parma 179 166 Venice 368 203
Crotone 50 27 Pavia 56 40 Verbania 10 16
Cuneo 21 82 Perugia 133 177 Vercelli 21 48
Enna 15 9 Pesaro 83 105 Verona 887 394
Fermo 27 128 Pescara 93 177 Vibo Valentia 1 20
Ferrara 67 171 Piacenza 224 120 Vicenza 93 290
Florence 722 356 Pisa 53 185 Viterbo 32 94
Table A3. Retail variables.
City reti City reti City reti
Agrigento 1795 Foggia 1756 Pistoia 2802
Alessandria 2257 Forlì 5345 Pordenone 1639
Ancona 2854 Frosinone 2289 Potenza 2207
Andria 1976 Genoa 18762 Prato 9081
Aosta 1537 Gorizia 940 Ragusa 2638
Arezzo 4333 Grosseto 3620 Ravenna 3970
Ascoli Piceno 3114 Imperia 1344 Reggio Cal. 4923
Asti 2377 Isernia 693 Reggio Emil. 9646
Avellino 1927 La Spezia 2812 Rieti 1349
Bari 4128 L’Aquila 1985 Rimini 5328
Barletta 1230 Latina 4556 Rome 83495
Belluno 1490 Lecce 1604 Rovigo 1362
Benevento 2028 Lecco 1535 Salerno 4411
Bergamo 6795 Livorno 4573 Sassari 4354
Biella 1880 Lodi 1056 Savona 2355
Bologna 11243 Lucca 2642 Siena 2200
Bolzano 4162 Macerata 1432 Siracusa 2922
Brescia 7324 Mantova 1556 Sondrio 781
Brindisi 902 Massa 2310 Taranto 2105
Cagliari 6496 Matera 1751 Teramo 1477
Caltanissetta 2117 Messina 5859 Terni 4633
Campobasso 1450 Milan 108407 Turin 26629
Carbonia 991 Modena 10935 Trani 692
Caserta 2350 Monza 4136 Trapani 2394

Sustainability 2022, 14, 13818 30 of 35
City reti City reti City reti
Catania 9689 Naples 28621 Trento 5300
Catanzaro 3492 Novara 3400 Treviso 2594
Cesena 3101 Nuoro 1246 Trieste 5640
Chieti 1892 Oristano 1491 Udine 2403
Como 2722 Padua 9394 Urbino 493
Cosenza 2490 Palermo 18327 Varese 2962
Cremona 1727 Parma 5244 Venice 19250
Crotone 1839 Pavia 2125 Verbania 827
Cuneo 1931 Perugia 5829 Vercelli 1376
Enna 703 Pesaro 2693 Verona 5971
Fermo 945 Pescara 4581 Vibo Valentia 1319
Ferrara 3066 Piacenza 3009 Vicenza 3409
Florence 22876 Pisa 2716 Viterbo 2685
Table A4. Accessibility variables.
City hsri d
AFi d
AIi Ai t
toti d
toti
Agrigento 0 0.1427 0.647 6.708 41.677 32.300
Alessandria 1 0.1886 1.427 18.272 15.121 14.085
Ancona 14 0.3576 5.358 14.340 15.074 13.718
Andria 0 0.2494 1.723 11.692 22.299 21.204
Aosta 0 0.1428 0.883 11.150 19.320 18.168
Arezzo 3 0.4531 1.280 16.152 13.941 12.918
Ascoli Piceno 3 0.4863 1.202 13.213 16.519 15.260
Asti 1 0.1756 1.593 16.798 16.155 15.028
Avellino 0 0.3699 1.801 14.703 20.295 19.665
Bari 15 0.2234 20.000 13.844 22.939 22.390
Barletta 13 0.2544 1.564 12.409 21.674 20.991
Belluno 0 0.1706 0.956 13.211 16.668 14.828
Benevento 5 0.3992 1.469 13.550 20.139 19.042
Bergamo 2 0.1718 1.173 22.416 14.631 13.539
Biella 0 0.1580 1.403 14.612 17.169 15.912
Bologna 85 0.2668 20.000 22.396 12.630 11.659
Bolzano 5 0.1567 20.000 13.157 16.623 15.474
Brescia 27 0.1877 0.776 20.299 13.885 12.728
Brindisi 9 0.1780 0.834 10.062 26.496 25.671
Cagliari 0 0.1918 13.451 9.764 25.663 28.142
Caltanissetta 0 0.1423 0.926 7.349 40.014 32.191
Campobasso 10 0.3997 0.826 11.816 19.761 18.410
Carbonia 0 0.1846 1.860 7.506 28.080 29.056
Caserta 5 0.4516 3.081 16.666 19.237 18.703
Catania 0 0.1280 20.000 10.496 36.828 33.881
Catanzaro 4 0.1661 2.787 8.220 29.568 28.463
Cesena 0 0.3054 1.140 16.872 13.733 12.251
Chieti 0 0.4436 7.642 14.318 17.023 15.978
Como 0 0.1626 2.354 20.684 15.540 14.424
Cosenza 5 0.1849 1.451 8.913 27.196 26.664
Cremona 0 0.1978 0.786 19.034 13.687 12.463

Sustainability 2022, 14, 13818 31 of 35
City hsri dAFi dAIi Ai ttoti dtoti
Crotone 0 0.1637 0.971 7.685 30.021 28.434
Cuneo 0 0.1679 1.030 12.930 17.985 16.793
Enna 0 0.1405 1.170 7.175 39.317 32.371
Fermo 0 0.3849 1.308 13.518 15.813 14.489
Ferrara 2 0.2355 1.984 18.313 13.515 11.989
Florence 58 0.3563 1.186 21.471 13.104 12.202
Foggia 14 0.2935 0.788 12.493 20.396 19.405
Forlì 4 0.2959 1.479 18.046 13.426 12.084
Frosinone 0 0.9188 0.919 16.021 17.225 16.635
Gorizia 0 0.1616 5.181 11.902 18.068 16.676
Grosseto 6 0.6416 0.650 13.271 15.454 13.933
Imperia 0 0.1724 0.850 11.239 18.547 17.356
Isernia 0 0.4931 0.972 12.639 19.066 17.847
La Spezia 7 0.2659 1.257 15.732 14.571 13.095
L’Aquila 0 0.6504 1.047 14.021 17.005 16.061
Latina 0 1.2230 1.223 15.507 18.161 16.821
Lecce 9 0.1676 0.644 9.263 27.828 26.747
Lecco 0 0.1633 1.416 19.155 15.513 14.295
Livorno 6 0.3515 3.998 15.790 14.848 13.227
Lodi 0 0.1840 1.290 21.598 14.150 13.040
Lucca 0 0.3093 3.296 16.914 14.203 12.755
Macerata 1 0.4147 1.851 13.550 15.876 14.092
Mantova 1 0.2138 0.979 19.896 13.096 12.058
Massa 6 0.2858 1.836 15.874 14.556 13.024
Matera 1 0.2249 1.653 11.165 23.997 22.640
Messina 0 0.1445 0.962 9.051 33.849 31.025
Milan 94 0.1744 2.301 30.949 14.458 13.510
Modena 11 0.2478 2.573 21.135 12.801 11.719
Monza 0 0.1708 2.103 28.530 14.595 13.702
Naples 48 0.4210 20.000 21.502 19.725 19.165
Novara 0 0.1694 3.475 19.735 15.535 14.451
Nuoro 0 0.2672 0.608 6.287 31.754 25.070
Oristano 0 0.2281 1.113 7.106 29.003 26.603
Padova 45 0.2040 2.421 20.855 13.952 12.635
Palermo 0 0.1745 0.480 10.517 40.653 28.651
Parma 10 0.2199 1.093 19.569 13.262 12.054
Pavia 2 0.1783 1.471 21.159 14.562 13.460
Perugia 1 0.5343 20.000 14.443 15.402 13.556
Pesaro 16 0.3394 1.746 15.317 14.269 12.889
Pescara 12 0.4226 20.000 14.412 16.961 15.782
Piacenza 10 0.1973 0.891 20.347 13.544 12.586
Pisa 7 0.3269 20.000 16.567 14.499 13.038
Pistoia 0 0.3144 1.367 18.365 13.594 12.440
Pordenone 3 0.1780 1.360 14.892 16.481 14.687
Potenza 1 0.2602 0.810 11.389 22.269 21.404
Prato 0 0.3386 1.237 20.876 13.231 12.275
Ragusa 0 0.1237 1.105 7.069 40.111 35.135

Sustainability 2022, 14, 13818 32 of 35
City hsri dAFi dAIi Ai ttoti dtoti
Ravenna 3 0.2797 1.289 17.386 13.653 12.136
Reggio di Calabria 4 0.1431 0.756 8.144 32.242 31.266
Reggio nell’Emilia 30 0.2336 1.566 20.227 13.040 11.883
Rieti 0 0.9465 0.946 14.772 16.487 14.975
Messina 0 0.1445 0.962 9.051 33.849 31.025
Milan 94 0.1744 2.301 30.949 14.458 13.510
Modena 11 0.2478 2.573 21.135 12.801 11.719
Monza 0 0.1708 2.103 28.530 14.595 13.702
Naples 48 0.4210 20.000 21.502 19.725 19.165
Novara 0 0.1694 3.475 19.735 15.535 14.451
Nuoro 0 0.2672 0.608 6.287 31.754 25.070
Oristano 0 0.2281 1.113 7.106 29.003 26.603
Padova 45 0.2040 2.421 20.855 13.952 12.635
Palermo 0 0.1745 0.480 10.517 40.653 28.651
Parma 10 0.2199 1.093 19.569 13.262 12.054
Pavia 2 0.1783 1.471 21.159 14.562 13.460
Perugia 1 0.5343 20.000 14.443 15.402 13.556
Pesaro 16 0.3394 1.746 15.317 14.269 12.889
Pescara 12 0.4226 20.000 14.412 16.961 15.782
Piacenza 10 0.1973 0.891 20.347 13.544 12.586
Pisa 7 0.3269 20.000 16.567 14.499 13.038
Pistoia 0 0.3144 1.367 18.365 13.594 12.440
Pordenone 3 0.1780 1.360 14.892 16.481 14.687
Potenza 1 0.2602 0.810 11.389 22.269 21.404
Prato 0 0.3386 1.237 20.876 13.231 12.275
Ragusa 0 0.1237 1.105 7.069 40.111 35.135
Ravenna 3 0.2797 1.289 17.386 13.653 12.136
Reggio di Calabria 4 0.1431 0.756 8.144 32.242 31.266
Reggio nell’Emilia 30 0.2336 1.566 20.227 13.040 11.883
Rieti 0 0.9465 0.946 14.772 16.487 14.975
Rimini 17 0.3110 1.091 16.367 13.935 12.544
Rome 103 3.7307 20.000 30.293 16.718 15.565
Rovigo 2 0.2204 1.257 18.089 13.725 12.247
Salerno 15 0.3453 1.593 14.849 20.746 20.267
Sassari 0 0.2495 0.461 6.511 33.872 25.953
Savona 0 0.1947 1.948 14.117 16.574 15.275
Siena 0 0.4643 0.936 15.673 14.007 12.784
Siracusa 0 0.1199 1.783 7.822 38.608 35.587
Sondrio 0 0.1477 0.691 11.671 17.887 15.928
Taranto 2 0.1943 1.078 11.470 25.302 24.511
Teramo 1 0.4916 1.651 13.636 16.637 15.556
Terni 3 0.8848 1.084 15.232 15.880 14.487
Turin 31 0.1614 6.587 21.782 17.123 16.159
Trani 0 0.2465 2.140 12.459 22.123 21.297
Trapani 0 0.1527 0.352 6.555 43.488 31.255
Trento 5 0.1720 1.766 15.373 15.148 13.994
Treviso 3 0.1942 2.996 18.666 15.034 13.354
Trieste 4 0.1559 3.025 11.761 18.777 17.425

Sustainability 2022, 14, 13818 33 of 35
City hsri dAFi dAIi Ai ttoti dtoti
Udine 3 0.1655 2.691 12.953 17.726 16.089
Urbino 0 0.3730 1.186 13.751 14.980 13.064
Varese 0 0.1601 3.488 18.817 15.914 14.766
Venice 46 0.1981 20.000 18.758 14.705 13.402
Verbania 0 0.1516 1.650 19.053 17.101 16.007
Vercelli 0 0.1705 1.923 17.808 15.903 14.734
Verona 32 0.2016 0.851 20.637 13.589 12.286
Vibo Valentia 2 0.1660 2.493 8.097 29.479 28.393
Vicenza 24 0.1966 1.417 19.590 14.130 12.656
Viterbo 0 1.2315 1.231 14.566 15.962 14.480
Table A5. Importance variables.
City capi City capi City capi City capi
Agrigento 0 Como 0 Matera 0 Rome 1
Alessandria 0 Cosenza 0 Messina 0 Rovigo 0
Ancona 1 Cremona 0 Milan 1 Salerno 0
Andria 0 Crotone 0 Modena 0 Sassari 0
Aosta 1 Cuneo 0 Monza 0 Savona 0
Arezzo 0 Enna 0 Naples 1 Siena 0
Ascoli Piceno 0 Fermo 0 Novara 0 Siracusa 0
Asti 0 Ferrara 0 Nuoro 0 Sondrio 0
Avellino 0 Florence 1 Oristano 0 Taranto 0
Bari 1 Foggia 0 Padova 0 Teramo 0
Barletta 0 Forlì 0 Palermo 1 Terni 0
Belluno 0 Frosinone 0 Parma 0 Turin 1
Benevento 0 Genoa 1 Pavia 0 Trani 0
Bergamo 0 Gorizia 0 Perugia 1 Trapani 0
Biella 0 Grosseto 0 Pesaro 0 Trento 1
Bologna 1 Imperia 0 Pescara 0 Treviso 0
Bolzano 0 Isernia 0 Piacenza 0 Trieste 1
Brescia 0 La Spezia 0 Pisa 0 Udine 0
Brindisi 0 L’Aquila 1 Pistoia 0 Urbino 0
Cagliari 1 Latina 0 Pordenone 0 Varese 0
Caltanissetta 0 Lecce 0 Potenza 1 Venezia 1
Campobasso 1 Lecco 0 Prato 0 Verbania 0
Carbonia 0 Livorno 0 Ragusa 0 Vercelli 0
Caserta 0 Lodi 0 Ravenna 0 Verona 0
Catania 0 Lucca 0 Reggio Calabria 0 Vibo Valentia 0
Catanzaro 1 Macerata 0 Reggio Emilia 0 Vicenza 0
Cesena 0 Mantova 0 Rieti 0 Viterbo 0
Chieti 0 Massa 0 Rimini 0
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