The Economic Impact of High-Speed Rail on Rural and Inner Urban Areas Economy: The Case Study of HSR Naples-Bari in South of Italy

3.1. The Case Study In Italy, high-speed rail was planned in the early 2000s and has undergone numerous interventions and developments [13], but remains mainly present in the large metropolitan areas, and on the Tyrrhenian coastal area, mainly connecting northern Italy, while in the south, the only connection currently present is Naples and Rome. Further stations have been added, but these are not true high-speed stations (“Ferrovia dello Stato Italiano” (RFI)’ “Great Stations” project, known in Italy as “Grandi stazioni”), but rather medium-sized stations at which the two transport operators (RFI and Italo—“Nuovo Trasporti Viaggiatori” (NTV)) provide passenger transport services. High speed has increased in Italy in the past few years [28]. There is a link between this growth and the real estate market, as evidenced by the 2010 opening of the Italo “NTV” railway line and the RFI project of “Major Stations” [15]. Many countries have examined the effects of high-speed rail on their territory, but little research has been done on the relationship between HSR and inland/rural areas, except for China and Japan [30] infrastructure also commonly contributes to social instability and inequality [31]. Projects also modify accessibility, upsetting multimodality. Railway lines can therefore have complicated positive and negative effects that are related to the most recent concerns in the literature, and one must be aware of this. In the Italian case (Figure 1), HSR could favour transits between the three regions of Apulia, Campania and Lazio, connecting three important poles: the city of Bari, which gathers the southern basin, the city of Naples as the main town of the Campania region, and the city of Rome, the regional capital of the Lazio region and the capital of Italy. At present, the existing connections are between Rome and Naples (service provided by “RFI” Rete Ferrovia Italiana and Italo “NTV” Viaggiatori), with a frequency of every 20 min in the main bands and one train per hour. After the 2014, with the increasing of the HSR network, Bari is connected to Turin (in Northern Italy) via Naples and Rome, with a service provided by NTV Italo, but with few runs and making the journey complicated due to the long time. The connection between Naples and the inland cities of Avellino and Benevento, impacted by the Naples-Bari high-speed rail project, is crucial. Avellino, home to the University of Salerno, and Benevento, with the University of Sannio, are both tourist and industrial hubs. They offer attractions like historical sites, trekking, and local food and wine, but are mostly accessible by car, complicating commutes and travel to Naples or Campania’s airport. Currently, high-speed rail connects Rome and Naples frequently, with limited and complex service to Bari. Improved rail connections could enhance access between Apulia, Campania, and Lazio, reducing road traffic and boosting travel efficiency. High-speed trains can also encourage tourist movements to inland areas by providing convenient access to the stations along the future high-speed rail line that will connect urban centers with rural areas as Figure 1 and Figure 2. Italy’s rural areas are still complicated to analyze for several reasons. As pointed out by [10], there is a possibility that the spatial development of rural areas took place in an unorganized manner due to the possibility that the population in these areas had a lower level of knowledge regarding the conservation and protection of the environment, resulting in ecologically significant ecosystems on the outskirts of cities becoming fragmented and destroyed [29]. The lack of transport and waste treatment facilities in remote locations has made the regulation and communal disposal of pollutants problematic [30]. The expansion of residential areas often happens before the establishment of such infrastructure. The Naples-Bari HSR project is a major infrastructure development in Italy, integrating high-speed rail with existing lines through the new Naples “Afragola” station. Key upgrades include one new stop, three additional stations, a new computerized central control in Naples, and improvements to the Naples-Cancello line. The project will add 121 km of double-track, 25 new routes, 11 tunnels, and 14 new stations. Currently operational are the Vitulano-Benevento-Apice, Cervaro-Bovino, and Foggia Bretella sections. Some sections are funded but not yet under construction. Initiated in 2004 and revived in 2012 under the “Sblocca Italia” act, this HSR line is a national priority under the TEN-T network, aiming to increase capacity, reduce travel times, and improve regional accessibility (Figure 2 and Figure 3). The cities involved on the Naples-Bari ¹ route are those on these sections:

• Itinerary NA-BA, 1st section: Variation to the Naples-Cancello line;
• Route NA-BA, Track Doubling Cancello-Benevento, 1st functional lot Cancello-Frasso Telesino;
• Track-doubling Frasso Telesino—Vitulano;
• Apice—Orsara double-doubling;
• Bovino-Orsara;
• Cervaro—Bovino;
• Rehabilitation of freight route Naples—Bari (in Foggia city);
• Foggia Cervaro station;
• Speeding-up Adriatica: technological enhancement Foggia-Bari-Brindisi.

The Naples-Bari HSR project will enhance the “Scandinavia—Mediterranean” Core Corridor by improving the railway connection between Naples and Bari. The journey time will be reduced from four hours to two hours, and the Rome-Bari connection will be cut to three hours. The project aims to improve accessibility, reduce CO₂ emissions, and shift freight traffic from road to rail, supporting EU goals for 2030 and 2050. It will also remove level crossings, minimize network interference, and facilitate the redevelopment of railway areas. Environmental monitoring will be conducted throughout the project, with results reported to the Ministry of the Environment. Key components include doubling and upgrading various line sections, with significant tunneling and the construction of new stations. The anticipated goals and benefits include: (i) cutting travel times from 3 h and 54 min to approximately 3 h; (ii) increasing capacity from 4 to 10 trains/h on routes subject to doubling; (iii) improving accessibility at the Irpinia interchange hub; (iv) modifying performance to allow freight trains up to 750 m in length and capable of transporting high-cube containers and semi-trailers (combined traffic code P/C80) without weight restrictions for axis. The study area covers the region between Naples, Avellino, and Bari, focusing on local emissions and potential reductions from the HSR line. The project costs between $6 and $100 million, with $4 million financed, $500 million pending approval, and $3.5 million already operational. By 2019, the Naples-Bari section will be fully tendered, with completion expected by 2026, reducing travel times to two hours between Bari and Naples and three hours to Rome. The “Hirpinia-Orsara” project, costing $1.3 billion, includes a 27-km tunnel through the Appennino, requiring 2900 labour days over eight years. This tunnel will enable travel at speeds up to 250 km/h, connecting the Avellino city and a new Hirpinia station in 35 to 40 min. A massive technological undertaking (one of the world’s ten longest railway tunnels) for which a straightforward addition will bring the project’s completion date to 2030 ². 3.2. The Methodology 3.2.1. The Methodological Approach Analyzing the correlation between spatial variables can lead to problems and limitations of the model to robust results. On this aspect, many scholars are concerned about the causal relationship between the construction of transport infrastructures and regional economic growth. Data characteristics often cause problems during analyzes caused by endogenous problems in causal inference, and various methods emerge in the literature to improve the rigor of empirical analysis, such as the simultaneous equation method [55], the instrumental variables method [71,72], difference-in-differences (DID) model, or propensity score matching with the difference-in-differences method (PSM-DID) based on exogenous policies [57,58,59,60,61,62,63,64,65,66,67,68,69], hedonic pricing theory [15] or geometrically weighted prices. Due to the unavailability of a panel dataset of this study, utilizing data from 2020, we decided not to apply a Synthetic Difference-in-Differences (SDID) approach, raising concerns about potential bias and endogeneity. The primary objective of this research is to evaluate the current effects of the high-speed rail (HSR) line on the region while it is still under construction. Consequently, we employed variables that are not strongly spatial to assess the socio-territorial impact, particularly regarding accessibility between the station, urban center, and production-work systems. The construction phase already demands substantial energy, manpower, and resources, indicating that HSR can influence the territory even during its construction phase. Moreover, contrary to what is presented in the project, there is no extensive network under construction between the stations and the urban center or other key areas. Therefore, we believe that studying long-distance accessibility will enhance the railway system by reducing the reliance on cars to reach the station. This focus on station-city accessibility aims to understand the competition between train and car for both short and long distances. The overview of the methodology employed in this study is illustrated in Figure 4 and is divided into six steps. The first step covers a comprehensive literature review (see Section 2), comparing findings, models, and variables used in previous studies on the impact of high-speed rail (HSR) on regional economic development. The existing body of research highlights several key impacts:

1. Enhanced Accessibility and Connectivity: Studies by [26,54,64], Refs. [73,74] emphasize that HSR reduces travel times and increases connectivity between urban centers and peripheral regions, creating a space-time compression effect. This is seen as a catalyst for economic integration and development.
2. Economic Growth: Research by [34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75] suggests that HSR can stimulate local economies by attracting businesses, boosting tourism, and enhancing labour mobility. These studies show a positive correlation between HSR infrastructure and regional economic expansion.
3. Urban Development and Land Use: Refs. [76,77] explored the impacts of HSR on urban development, noting that it often leads to increased land values and urban regeneration around stations.
4. Environmental Benefits: Studies by [78] highlights the environmental advantages of HSR, such as reduced carbon emissions and lower energy consumption compared to other modes of transport like cars and planes.
5. Social Equity: Ref. [79] discuss the potential social equity issues related to HSR, such as the risk of displacement and gentrification around new HSR stations.

The second, third, and fourth steps are closely interconnected and are detailed in Section 3.2.1. In the second step (Section 3.2), we conduct an in-depth analysis of the dataset, examining the available variables to understand their characteristics and relationships. The focus is on identifying the dependent variables and covariates relevant to our study on the impact of High-Speed Rail (HSR) on regional economic development. The dataset used in this study includes variables related to economic performance, transportation infrastructure, and demographic characteristics. Our primary focus is on variables that influence the effects of HSR construction on regional economic development. We utilized the 2020 dataset for the following reasons: although construction on the line began in 2016, the primary executive phases were completed by 2020, making it a comprehensive reference point in terms of project acquisition, contract completion, and financing. Moreover, including a dataset spanning from 2016 to 2024 could yield inconsistent results due to disruptions caused by COVID-19 and related construction activities between 2020 and 2022. The main variables analyzed from the 2020 dataset are positively correlated and significant, providing a robust foundation for our analysis. In the third step, we concentrated on identifying the key variables necessary for analyzing the impact of HSR on regional economic development. The dependent variables selected were economic growth (measured as GDP per capita) and income disparity (measured using the Gini Coefficient), as they directly reflect economic performance and equity within the regions affected by HSR. To understand the influences on these outcomes, we identified several covariates: travel time reduction, which captures the direct benefits of HSR; employment rate, an indicator of labor market health; accessibility index, which measures how HSR enhances connectivity; population density, reflecting urbanization levels; investment in infrastructure, showing additional economic inputs; commuter statistics, providing insight into mobility patterns; and environmental impact, indicating changes in sustainability metrics. This comprehensive identification of variables enables us to control for various factors affecting economic growth and income disparity, ensuring a thorough analysis of HSR’s effects. The fourth step described the statistics descriptive in Table 1. Also, we conducted some main important test. The first is to test the normality of the sample data that is essential for several reasons. First, many statistical methods, including regression analysis, assume that the variables involved follow a normal distribution. This assumption underlies the validity of parametric tests, influencing the accuracy of hypothesis testing and confidence intervals. If the data deviates significantly from normality, the results of these tests may be unreliable, potentially leading to incorrect conclusions about the relationships between variables. By testing for normality, we can determine if transformations or non-parametric methods are needed to ensure robust and accurate analysis. Secondly, the correlation between variables is crucial for understanding the relationships and potential dependencies among them. Correlation measures the strength and direction of a linear relationship between two variables. By examining these correlations, we can identify which variables are significantly related and how they might influence each other. For instance, a strong positive correlation between employment rate and economic growth would suggest that regions with higher employment levels tend to experience greater economic expansion. Understanding these relationships helps in constructing more accurate regression models, as it allows us to account for multicollinearity and better interpret the influence of each covariate on the dependent variables. Additionally, it aids in the identification of key drivers of economic outcomes and in the formulation of targeted policy interventions to maximize the benefits of HSR. Additionally, we performed tests on the significance of the variables (the Spearman’s Rank and the Shapiro-Wilk test). The steps 5 and 6 of our analysis are detailed in the following Sections 3.3 and 4. To analyze the impact of high-speed rail (HSR) on regional economic development, we identified two econometric models related to the two main dependent variables. To investigate economic growth, the first model was developed to measure how the construction of HSR impacts local economic growth by improving connectivity, employment, and infrastructure. Specifically, we aimed to understand the influence of the presence of an HSR station on GDP per capita. For income growth, we developed the second model to measure how HSR infrastructure increases income per capita by enhancing accessibility and economic opportunities, and to understand the role of income inequality (Gini Coefficient) in this relationship. Based on the research questions and the characteristics of the variables, we employed two different models for different dependent variables. Finally, we tested the robustness of our results by analyzing McFadden’s pseudo-R², comparing our results with those found in the literature to evaluate differences in impact. We also performed residual analysis to check for normality and homoscedasticity, ensuring that residuals followed a normal distribution and had constant variance across levels of the independent variables ³. This approach provided confidence in the validity of our findings and their contribution to the existing body of research [80,81]. 3.2.2. Dataset and Variables The study analyzed the impact of the railway line on 25 cities, including Benevento, Foggia, Avellino, and Bari, using data from ISTAT (Italian National Statistics Institute). It focused on two aspects of rural development: mobility and economic productivity. Mobility analysis acts as an early indicator for future development, highlighting issues like increased traffic and pollution due to reliance on private vehicles. The study assessed how the high-speed rail network would affect accessibility and economic growth in metropolitan and inland areas. Accessibility impacts quality of life and business productivity, influencing political strategies and territorial planning. The general concept that connects all the different measurements is that a point/place is the most accessible and easiest to reach. This implies that the basic measure for calculating an accessibility index is the distance between origin and destination, which also measures the value of the cost of the trip. The accessibility and mobility indicators concern the entire national contest, the transport infrastructures considered are 258 railway stations with active passenger service where there is long-distance railway traffic; 2842 accesses to the motorway network; 35 airports for commercial services; 54 statistical ports with passenger traffic, distances are measured in terms of travel time on a TOM TOM road graph. The types of accessibility provided by the dataset [82,83,84,85,86] are:

• Cost to closest: Measures the cost required, expressed in terms of minimum travel time, for at least one destination among the selected ones to be reached from a given origin;
• Accessibility/Proximity Cluster: A municipality (generically an area) may be more or less accessible either because it is less or more difficult to reach an infrastructure or because there is no infrastructure nearby. The two situations might imply different policy interventions: investment in improving the road network to reach the infrastructure in the first case, investment in building the infrastructure in the second;
• Gravity model accessibility index: It introduces an element of infrastructure differentiation by considering the amount of services offered by the infrastructure itself. This model defines accessibility as the potential of opportunities, introducing into the analysis also a behavioural aspect of the choices made.

For the economic sector, we analyzed studies ranging from [60,61,62,63,64,65,66], focusing on the number of businesses situated around the infrastructure route and the Gini index ⁴ and with the socio-economic indicators of the cities (employment rate, income per capita). These studies provide crucial insights into the effects of infrastructure projects, such as high-speed rail (HSR), on local economies and income distribution. Our analysis involved assessing the density and distribution of businesses in proximity to the HSR line, which serves as an indicator of economic activity and development. Furthermore, we utilized the Gini index to evaluate income inequality within the affected regions. By correlating these economic variables with the development of HSR infrastructure, we aim to elucidate both the direct economic benefits and the broader socioeconomic impacts. This comprehensive approach ensures a detailed understanding of how HSR projects can influence local and regional economic landscapes, fostering growth and potentially mitigating income disparities. These findings underscore the multifaceted economic implications of high-speed rail, reinforcing its significance in promoting sustainable economic development. We used the 2020 dataset to capture the initial phases of the high-speed rail infrastructure project. The construction phase began in 2016, with completion expected between 2026 and 2028. Our focus at this stage is to monitor the pre-construction impacts of the railway line. 3.2.3. The Choice of Variables and Testing The dependent variables represent the primary outcomes we aim to measure in response to the construction of HSR. These variables are critical for testing our hypotheses regarding the economic and social impacts of HSR. In Table 1 the descriptive statistics of variable is reported. The socio-economic variables include the Gini index, employment rate, per capita income, and population. Accessibility is measured using several indicators: Time Access Road (which measures the time required to access the main road), and Accessibility 3 and 4 (which represent the time needed to reach airports and port areas, respectively). The variables Cluster Accessibility to Railway Network, Cluster Road Network, Cluster Airport, and Cluster Access Port Areas describe accessibility by cluster to capture local effects rather than just point-in-time measures. The final variables indicate accessibility indices for reaching port areas, airports, railway stations, and major roads. According to the economic sector we focused on the first variable “HSR station” and the second variable is “Income per capita”. The first variable aims to measure the effect of the HSR on the local economy, as this variable directly reflects the economic performance and growth of the regions impacted by HSR, making it a key indicator of local economic expansion. The Income per capita measures the degree of income within the region, assessing changes in income disparity helps us understand the impact of HSR on economic equity, particularly whether it helps bridge the income gap between urban and rural areas. Covariates are the independent variables that may influence the rural development according to research question. These factors are essential to control for in our analysis to isolate the effect of HSR on economic growth and income disparity. The variables that we have choose, according to the literature, are (a) travel time reduction to measure the reduction in travel time between key locations due to the HSR. The Travel time reduction is a direct consequence of HSR, enhancing connectivity and potentially stimulating economic activity and integration; (b) the employment rate is the percentage of the working-age population that is employed. Employment levels can significantly influence economic growth and income distribution, making it a crucial covariate; (c) the accessibility index is a measure of how accessible key urban centers are from surrounding areas. This variable represents the improved accessibility due to HSR can impact economic opportunities and regional cohesion; (d) population density is the number of people per square kilometer. Population density reflects urbanization levels, which can affect economic dynamics and the distribution of income; (e) investment in infrastructure is the amount of public and private investment in infrastructure, excluding HSR. This variable describe when the infrastructure investments drive economic growth, and controlling for this ensures that we isolate the impact of HSR; (f) the commuter statistics are the number of people commuting between different regions, including the mode of transport. The commuting patterns can influence local economies and labor markets, making it important to consider in our analysis; (g) the environmental impact measures changes in carbon emissions and other environmental indicators pre- and post-HSR. The environmental factors can indirectly affect economic growth and quality of life, which in turn can influence economic metrics. We carried out test to analyze normality of the dataset, and we found that the variables Employment and income per capita, they are normally distributed. The dependent variables are for model 1: HSR Rail (Stations) and Opening HSR work site before 2020. 3.3. The Econometric Modelling 3.3.1. The Choice of Hypothesis Research presents diverse perspectives on the impact of high-speed rail (HSR) on regional economic development. Most scholars agree that HSR enhances accessibility, creating a space-time compression effect that facilitates the flow of spatial elements and significantly impacts urban economic growth. Therefore, it is essential to determine how to reliably and scientifically assess the effect of HSR construction on regional economic development. Based on previous studies, this research proposes the following hypotheses (H1 and H2) to evaluate if “The development of HSR infrastructure is positively correlated with local economic expansion”. The rationale behind H1 and H2 is grounded in the following discussion: if the high-speed rail (HSR) positively impacts the region, we can measure it in this case study in several ways: (a) its effect on the territorial fabric, (b) its effect on workers, and (c) improvements in transportation from/to stations and urban centers. Promoting sustainable travel is a key requirement for both Italy and Europe. The Naples-Bari line, financed in part by Recovery plan “PNRR” funds, aims to reduce car dependency. Furthermore, as mentioned in Section 3.1, ensuring accessibility for workers along the railway in inland areas is crucial. The HSR systems significantly reduce travel times between regions, making it easier for individuals in rural or less accessible areas to reach urban centers. This improved connectivity allows residents from lower-income or rural regions to access a broader range of economic opportunities, including higher-paying jobs that are concentrated in urban areas. Over time, the economic benefits of HSR typically accumulate. These benefits can include increased property values, enhanced business opportunities, and improved regional connectivity. As the HSR network matures, its impact on regional economic growth and productivity becomes more pronounced. By linking economically disparate regions, HSR fosters greater economic integration. Individuals can commute longer distances for work, which can help reduce regional disparities by increasing the labor pool available to businesses in urban centers and providing rural residents with access to diverse employment options. Studies show that regions connected by HSR experience a reduction in travel times, which facilitates more frequent and efficient commuting and enhances overall mobility [34]. H1: The Construction of High-Speed Rail (HSR) Induces a Temporal Accumulation Effect. The construction of HSR significantly reduces travel times between the Naples and Bari poles and the surrounding municipalities. This temporal accumulation effect enhances connectivity, making it more feasible for workers to commute and for businesses to operate across greater distances. Consequently, this fosters economic integration and development in these regions. The improved accessibility facilitates the flow of goods and services and promotes regional cohesion by bridging the gap between urban and rural areas [87,88]. H2: The Construction of High-Speed Rail (HSR) Impacts the Income Gap. By enhancing accessibility and proximity, HSR infrastructure development facilitates easier and faster travel between regions, potentially leading to more equitable economic opportunities. Improved connectivity makes it feasible for individuals from lower-income rural areas to access higher-paying jobs in urban centers, thus reducing the income disparity. Additionally, increased accessibility can attract businesses to rural areas, fostering local economic growth and development. These factors combined contribute to narrowing the income gap and promoting regional economic integration [56,89]. 3.3.2. The Regression Models Evaluating the impacts of High-Speed Rail (HSR) on regional economic growth involves a theoretical framework that integrates concepts from transport economics, regional development theory, and urban planning. According to classical economic theory, transportation infrastructure enhances productivity and economic growth by reducing transportation costs and improving market access. HSR, by offering faster and more reliable transport, is expected to stimulate economic activities by linking regions more effectively. The improved accessibility brought by HSR often results in increased clustering of economic activities in connected regions, fostering regional economic development, the enhancing the ease with which people and goods can move between locations. To evaluate the accessibility and the spatial effects of HSR, we consider also the Spatial Interaction Models. These models, such as the Gravity Model, are used to estimate how changes in transportation infrastructure affect economic interactions between regions. HSR can alter the spatial distribution of economic activities by reducing travel times and expanding the effective catchment area of economic centers [90,91,92,93]. The models are designed based on the theory that improved transportation infrastructure, such as HSR, can lead to economic growth by enhancing connectivity, reducing travel times, and increasing accessibility to job markets and business opportunities. This, in turn, can impact local economic conditions, including income levels and regional disparities. The choice of variables is aligned with the objective to isolate the impact of HSR from other economic and socio-economic factors. By including control variables like employment rate, Gini index, and industrialization, the models aim to provide a comprehensive understanding of how HSR influences economic and social outcomes. The use of binary and continuous outcome variables, along with appropriate regression techniques, ensures that the analysis captures both the presence of HSR infrastructure and its quantitative effects on economic and social variables [56,75,87,88]. Based on the characteristics of the variables, we employed two different regression models to study H1 and H2 Model 1 to examines the relationship between HSR construction and local economic expansion; Model 2 to investigates the impact of HSR construction on income disparity and accessibility: Model 1 aims to estimate the direct effects of the HSR on the development of the local economy as follows: The model 2 is designed as the Equation (2): We treat the variable “HSR station” as a binary indicator (0 for no station open, 1 for station open) for the year 2020 across all 25 cities. This classification accounts for the fact that the Naples-Cancello and Telese sections of the HSR began construction in 2016. In Model 1, the dependent variable Y_it represents the binary status of whether a high-speed rail station is present in city i at time t. The covariates X_it include control factors such as the employment rate, the Gini index, industrialization levels in the area, and travel times between stations, as well as the time needed to reach airports and ports. Model 2 examines whether the presence of HSR affects per capita income. Here, the dependent variable Y_it is the income level in city i at time t, while the independent variables X_it include socio-economic factors such as the Gini index, population, accessibility, and economic clustering. Since Y_it conforms to a normal distribution, a linear model is appropriate for this analysis. Model 2 also assesses the potential impacts of HSR on accessibility to local work systems and transportation hubs (ports and airports), using the “Opening HSR work site” variable to indicate the presence of construction sites from 2016 to 2020. The evidence in the literature supports the idea that HSR construction has significant effects on local economic expansion, income disparity, and accessibility. Research demonstrates that HSR can stimulate economic growth, reduce income disparities by improving access to better job opportunities, and enhance regional connectivity. These findings provide a solid foundation for understanding the broader impacts of HSR infrastructure on regional development and socio-economic integration.