Angela Stefania Bergantino, Vicente Royuela Mora, Maria Grazia Cito (p)

Mario Intini

This study assesses the impact of climate change on tourism demand across EU NUTS-2 regions, analysing projected trends through 2100 under different global warming scenarios. The analysis relies on an original dataset integrating multiple sources. The dependent variable, total nights spent by tourists, serves as a proxy for tourism demand, while the key independent variable, the Tourism Climate Index (TCI), quantifies climatic suitability for tourism activities. A categorical variable differentiates among six tourism typologies (urban, coastal, nature-based, rural, snowy mountain, and mixed tourism). The analysis employs a multi-way fixed effects log-log panel regression model to estimate the relationship between climate conditions and tourism demand, incorporating population, GDP per capita, and fixed effects as controls. Findings suggest that Mediterranean coastal regions, particularly in Greece and Italy, will experience reduced summer tourism demand due to declining TCI values. However, shoulder months may see an increase. Nature-based and rural tourism areas face declines in summer and autumn, while winter tourism suffers from reduced snowfall. Meanwhile, mixed-tourism regions may benefit, as tourists seek destinations offering diverse attractions. Overall, climate change is expected to reshape Europe’s tourism geography, driving seasonal and regional shifts in demand. The anticipated decline in Mediterranean coastal tourism highlights the need for adaptive strategies to mitigate economic losses. At the same time, increasing demand in less climate-vulnerable regions presents opportunities for tourism development.

Angela S. Bergantino (p), Mario Intini (p), Michele Vitagliano

Alessandro Buongiorno

The concept of quality of life has evolved significantly over time, moving beyond economic well-being and life
expectancy to encompass a broader range of factors. It is now defined as the degree of satisfaction individuals derive from
their social and physical environments (Mulligan, Carruthers, & Cahill, 2004). This comprehensive approach emphasizes
both individual and societal well-being, integrating quality of life indicators into public policy to assess the holistic effects
of decisions on daily life.
This paper is designed to furnish decision-makers with the pertinent information required to intervene effectively in
specific territories, promoting greater alignment across the area or its particular characteristics while addressing the
limitations identified in the existing literature. It seeks to explore the relationships between quality of life and a diverse,
more granular dataset, employing machine learning (ML) models to identify the key drivers contributing to increasing
territorial disparities and preventing the establishment of uniform quality standards. Unlike the previously discussed
literature, which tends to isolate territorial disparities based on a single feature, this paper collects a comprehensive set of
heterogeneous variables across a more detailed spatial dimension (NUTS-3 level, or Italian provinces). To achieve this,
a combination of data from certified institutional sources and web scraping techniques has been utilized. Furthermore,
this paper aims to develop a decision-making tool for policymakers through decision tree representations, ensuring
enhanced explainability. More specifically, the approach trains machine learning models to identify the key characteristics
relevant for distinguishing differences in quality of life. This is represented by a composite indicator known as the Life
Quality Index (LQI), which, by design, aggregates several macro-categories and serves as a benchmark to address
territorial disparities across various aspects of life. The proposed methodology is neutral, as it avoids any assumptions of
functional dependence between the collected objective characteristics and the target variable, LQI. The insights gained can guide decision-makers in developing strategies
to enhance the quality of life in similar settings, even if they are situated in different geographical areas.

Alessandro Buongiorno (p), Angela Stefania Bergantino, Mario Intini

Giulio Fusco

Over the past 30 years, international arrivals have more than tripled, reaching 1.5 billion tourists. The growth in tourism has been steady over time, despite adverse events that only temporarily reduced the number of travellers. After the Covid-19 pandemic crisis, international demand has demonstrated extraordinary resilience, fully recovering 2019 levels globally by 2024. Considering this significant increase in tourism demand, more and more destinations are experiencing congestion, often exacerbated by the media, which highlights the frustration expressed by residents due to the unregulated, uncontrolled growth of tourism. Commonly used metrics to monitor tourism pressure on destinations fail to anticipate discontent because they have numerous limitations and often are inadequate for accurately estimating the phenomenon. These shortcomings are particularly evident when it comes to addressing the widespread issue of excessive tourist concentration, both temporally (seasonality) and spatially (a few popular sites within much larger territories).
This study highlights the main limitations of commonly adopted indicators for monitoring tourist flows and presents a methodology that addresses the issue of tourist concentration using a more comprehensive approach to evaluate the phenomenon. We developed a new synthetic indicator that combines aspects related to the volume of tourist flows (pressure) with their temporal dynamics, simultaneously considering, for the first time, the degree of concentration of tourist flows. Concentration often emerges as the primary cause of the negative impacts of tourism on local territories and services, as well as the discomfort experienced by the local population, beyond the mere number of visitors.
This methodology was tested in a wide and diverse territorial context, namely the 107 Italian provinces, through an empirical analysis involving extensive data collection and analysis at the provincial and municipal levels across the country. The results of the analysis demonstrate that this methodology can be highly effective in monitoring the vulnerability of tourist destinations exposed to excessive tourism pressure. It goes beyond generic assessments of tourist volume, typical of mass tourism, by delving into other critical aspects, such as demographic dynamics linked to the tourism economy and demand concentration phenomena.
The informative value of the indicator, which can be easily applied to various territories, provides precise policy guidance for administrators, industry professionals, scholars, and local social actors interested in fostering harmonious and balanced tourism development. It supports a governance and regulatory approach in a sector that still holds considerable potential for economic and social diversification and development.

Angela Stefania Bergantino, Giulio Fusco (p), Mario Intini

Maria Grazia Cito

The relationship between public transport investments and economic and territorial development has been a subject of discussion among researchers and policymakers for several years (Mikessel et al., 2015). However, it remains a widely debated and underexplored topic, particularly in light of continuous technological innovations that have transformed both the modes and capacities of transport systems (Acheampong et al., 2022).
While extensive literature has demonstrated that increasing transport infrastructure and investments in the transport sector can enhance productivity and stimulate regional growth (Istadt et al., 2012; Chen, 2019; Bergantino et al., 2025), the actual effects of these interventions remain complex and context dependent.
Among various transport initiatives, the deployment of High-Speed Rail (HSR) services in recent decades has arguably been the most significant innovation in intercity travel worldwide. HSR systems are designed to reduce travel times and improve regional accessibility, aligning with previous literature that suggests enhanced connectivity fosters regional development and boosts productive activities. However, the magnitude and direction of these anticipated effects remain contested (Chen & Vickerman, 2017). One key concern is that HSR may primarily benefit already prosperous and economically dynamic cities, potentially exacerbating regional inequalities by diverting economic activity away from smaller or less connected areas.
In this context, the objective of this study is twofold. First, we assess the impact of HSR on business performance using municipal-level data. Second, we analyze the spatial distribution of these effects to investigate the presence of a "siphoning effect" (Niu et al., 2020), which could disadvantage poorer or more remote municipalities located farther from HSR stations.
To address these research questions, we employ a counterfactual approach to estimate the causal impact of HSR introduction on firm performance in Italy. Furthermore, we conduct spatial analysis to examine how these effects propagate across different municipalities. Our findings will contribute to the ongoing debate on whether HSR serves as a catalyst for widespread economic growth or primarily benefits already thriving urban centers at the expense of peripheral regions.