A Model for Estimating the Tourism Carrying Capacity (TCC) of a Serial Cultural Heritage: The Case of the Via Appia. Regina Viarum

Abstract

This study proposes a model for estimating the Tourism Carrying Capacity (TCC) of serial sites, a little-explored topic in the literature, developed for the UNESCO serial site Via Appia. Regina Viarum. The model is based on an extensive reinterpretation of Cifuentes’ Physical, Real, and Effective Carrying Capacity and on territorial indices used to modulate the carrying capacity of territories in relation to their infrastructural and ecological endowments. The estimate is conducted on 614 municipalities and 13 gravitational areas and includes the simulation of three evolutionary scenarios (2024–2034) of increased tourist pressure. The results for 2019 indicate an overall level of Effective Carrying Capacity at “low impact”, but with significant territorial variability, with some municipalities and areas tending towards increasing values of tourist load in future scenarios. Greater infrastructure provision does not automatically guarantee greater absorption capacity in the presence of ecological fragility, while municipalities with high ecological provision may show signs of stress due to a lack of services and infrastructure. The main contribution of the research is to extend the determination of TCC from the analysis of individual sites to the scale of the entire territory, providing a tool to support tourism planning and management.

1. Introduction

Tourism represents a strategic sector for local development, but it poses significant challenges in terms of sustainability and impact management [1,2,3,4,5,6]. These impacts underscore the necessity for complex governance processes involving institutional actors and stakeholders across multiple levels, highlighting the importance of increasing integration between tourism planning and territorial planning [5,7,8,9,10,11,12,13,14,15]

The literature has addressed the topic of Tourism Carrying Capacity (TCC) through diverse perspectives [16,17,18]. TCC methodologies have been developed, for instance, for urban tourism destinations [19], coastal areas [20], geosites [21], and protected areas [22,23]. Others have formulated cross-cutting TCC methodologies applicable to diverse destinations [24,25,26]. Additional studies focus on defining targeted indicators for measuring tourism sustainability [14,27,28,29,30] and for managing criticalities related to overtourism [4,31]. However, destinations characterized by heterogeneous attractors distributed over extensive geographic areas, require innovative methodological approaches, particularly considering the need to integrate various levels of territorial governance.

In this regard, the Italian context assumes a paradigmatic role, offering particularly favorable conditions for examining the interactions between spatial planning, tourism development, and environmental protection. Italy is among the State Parties to the Convention Concerning the Protection of the World Cultural and Natural Heritage, adopted by UNESCO in 1972 [32], and holds the highest number of World Heritage properties: 61 out of a total of 1248 sites inscribed across 170 countries worldwide [33]. In Italy, as also advocated at the European level [34], the safeguarding and enhancement of cultural heritage are understood—within an integrated development and landscape planning perspective—as positive drivers of local development. Tangible cultural heritage is commonly regarded as a key area of intervention with significant relevance for local development, and it exerts a measurable territorial impact [35].

The present study introduces an assessment of carrying capacity in tourism destinations, with particular reference to serial properties. From this perspective, the study develops a model to measure the TCC of a complex cultural destination based on the presence of heterogeneous attractors distributed over an extensive geographic area. The estimation model is applied to the Via Appia. Regina Viarum in Italy, a serial site inscribed in 2024 on UNESCO’s World Heritage List [33,36]. UNESCO serial sites comprise two or more elements, monuments, and geographically distinct places (located in one or more Member States) that, taken together, contribute to the Outstanding Universal Value of the inscribed site [37,38].

A substantial corpus of research has contributed over time to better define the concept of TCC and its applicative evolution. Over the years, various definitions of TCC have been proposed, including that of UNWTO [39], which describes it as “the maximum number of people that may visit a tourist destination at the same time, without causing destruction of the physical, economic and sociocultural environment and an unacceptable decrease in the quality of visitors’ satisfaction.” Despite advances in this field, although there are several studies focused on UNESCO sites, to our knowledge, those dedicated to estimating Tourism Carrying Capacity in relation to extensive serial sites remain limited.

Developed within the framework of the nomination process for Via Appia. Regina Viarum as a UNESCO World Heritage site, this research was conceived as part of a broader institutional initiative involving the direct participation of the Italian Ministry of Culture (MiC). The nomination process provided a unique opportunity to operationalize a methodology aimed at assessing the Tourism Carrying Capacity of a large-scale serial cultural property, in line with national strategies for cultural heritage and UNESCO guidelines. The designation of a site as a World Heritage property is undoubtedly a highly coveted recognition for States, with the associated benefits primarily linked to the prestige and branding potential of the UNESCO label. In the first case, the emphasis lies on international recognition and the shared commitment within the global community to preserve and protect the site. In the second case, the focus shifts toward enhancement and promotion, leveraging the UNESCO certification as a guarantee of the site’s uniqueness and quality to attract potential tourists [40].

While some studies confirm a positive relationship between World Heritage List (WHL) inscription and increased tourist flows, others report more nuanced results [41,42,43]. In general, however, both Italy and the local stakeholders involved in Via Appia. Regina Viarum perceive the UNESCO designation as a valuable opportunity to decentralize tourist flows, promote lesser-known areas, and foster sustainable and integrated development [36].

Via Appia. Regina Viarum represents a particularly significant case for the application of TCC estimation. The spatial complexity characterizing this serial cultural property, combined with its historical and archaeological significance and its high potential for international tourism appeal, poses substantial challenges for the sustainable management of visitor flows. In this context, the calculation of TCC responds to a request by the Italian Ministry of Culture (MiC), with the aim of preventing overtourism and safeguarding the integrity of the cultural and landscape heritage concerned.

The Italian Ministry of Culture (MiC) has identified the designated site in 22 components constituted by a multiplicity of attributes (tangible archaeological evidence), very different from each other in typology and conservation status [36]. The components of the serial site, core zones, are territorially distributed across four regions of central-southern Italy (Lazio, Campania, Basilicata, and Puglia); the sum of their perimeters covers an area of 9387.64 ha, while that of their respective protection zones, buffer zones, is 41,354.34 ha. In this study, we propose an estimation of Tourism Carrying Capacity based on an extensive interpretation of the ecosystem-based model developed by Cifuentes [44,45,46], adapting it to complex and functionally heterogeneous contexts. While preserving the original conceptual framework, the model is extended and recalibrated from a geographical–territorial perspective, with the aim of capturing the multiplicity of factors that influence the tourism absorption capacity of the territories crossed by Via Appia. Regina Viarum.

This approach entails a methodology for measuring Tourism Carrying Capacity grounded in an extensive reinterpretation of the capacity assessment model proposed by Cifuentes [45].

The geographical–territorial model provides for a recalibration of the route’s pertinence areas. To this end, based on a procedure for extending the radius of influence of Via Appia. Regina Viarum, the methodology integrates two scales of geographical analysis: one with municipal-level analysis units (614 municipalities), the other with territorial-level analysis units (13 gravitational areas). This choice was adopted to overcome the impracticability of a detailed TCC estimate for each component and, simultaneously, enabling an exploratory assessment of the carrying capacity of the affected territories.

The literature acknowledges the impossibility of applying a single methodological approach for estimating Tourism Carrying Capacity uniformly across all territories [26], and highlights the variety of techniques available to capture its social, economic, and environmental dimensions. However, to date, consolidated applications addressing complex serial contexts—characterized by marked spatial and functional heterogeneity—remain lacking. The methodology proposed here is not intended to be generalizable, but rather seeks to address this gap by offering a targeted response to the specificities of the case study.

The evidence produced by the model can serve as a basis for territorially oriented policy evaluations. First, it provides an empirical knowledge base to support the development of tourism planning strategies grounded in sustainability principles, territorial differentiation, and local absorption capacity, while acknowledging the multi-dimensional nature of tourism impacts. Second, in line with the existence of a geographical context characterized by a seasonal and spatially polarized tourism phenomenon—particularly concentrated along coastal areas and in major cities—the model’s outputs allow for a refinement of tools to monitor tourism pressure. This can guide decision-making toward a more balanced distribution of tourist flows and a more efficient and coherent use of territorial resources, especially in areas less exposed to tourism, including those located farther from major urban centers.

For these reasons, the model can support public policies in identifying areas that are most vulnerable to anthropogenic pressure as well as those with untapped tourism potential, facilitating selective interventions for the protection, management, and valorization of both the territory and its cultural heritage [14].

The document is organized into seven sections. After the introduction, Section 2 explores the theoretical framework and main analytical perspectives on the concept of tourism planning, analyzing the evolution of tourism as a strategic and plannable activity. Section 3 identifies the study area and describes the procedure that led to the recalibration of the geographical analysis scope. Subsequently, it presents the geographical–territorial model, illustrating its conceptual structure and analysis dimensions associated with corrective indicators. This approach is further deepened by the construction of scenarios aimed at testing the carrying capacities of individual municipalities and individual areas. Section 4 shows the results obtained from applying the model and scenarios, highlighting the temporal evolution of carrying capacities in the municipalities and areas previously derived. Section 5 offers a critical discussion of the proposed model, focusing on its main strengths, the results achieved, and the limitations encountered. Section 6 addresses methodological considerations and limitations. Finally, Section 7 provides the conclusions, summarizing the key contributions of the study and suggesting avenues for future research.

2. Theoretical Background

In the context of post-World War II economic growth, the expansion of international travel made mass tourism a global phenomenon, laying the foundations for new challenges in tourism destination management. During this historical phase, mass or conventional tourism activity emerged as a direct application of the Fordist approach to the travel industry and destination management, aimed at responding to growing demand from an increasingly broad public [47,48,49,50,51]. Mass tourism has traditionally been associated with intensive resource use, the adoption of unsustainable production models, and increased tourism intensity in many areas, which were in turn reshaped according to strategies markedly oriented by the economic growth paradigm [10,11,52].

From a scientific perspective, particularly since the 1970s, researchers have repeatedly questioned the effects of intensifying tourism growth and its expansion [53,54,55,56,57,58]. During this years, tourism planning, initially applied only within the tourism industry framework [59] and in the physical/economic evaluation of specific facilities or individual areas [60], began to emerge as a field of study and concept of public interest. This evolution was configured as a response to rapid sector changes and the diseconomies produced by tourism growth, in an attempt to manage its associated effects from environmental, socio-anthropological, and spatial perspectives [10,53].

In his work, Getz [61] identified four distinct traditions in tourism planning, each representative of a specific vision of tourism development: boosterism; industry-oriented; physical/spatial; community-oriented. These traditions continue to represent an essential reference framework, widely used but also expanded by subsequent literature in analyses of different planning contexts; the traditions do not necessarily exclude each other, nor do they develop in linear sequence. While approaches guided by tourism growth objectives and demand stimulation emerged from the 1960s, reflecting a currently dominant interpretation that tourism would automatically bring positive results in the absence of a critical vision—the so-called “boosterism”—more structured attempts at rational planning emerged from the 1970s, aimed at mitigating the diseconomies associated with unregulated or unplanned tourism growth [8]. From this perspective, physical/spatial practices constituted a formulation sensitive to spatial organization, land use planning and associated infrastructure, the search for spatial patterns coherent with the development of specific tourism areas, as well as the ecological absorption capacity of tourism’s environmental impacts [58,62,63]. At the same time, such practices were judged less sensitive to socio-cultural aspects. The ecological logic of these models proved coherent with the emergence, beginning in the 1960s, of a growing scientific debate on Tourism Carrying Capacity, aimed at preventing and monitoring the effects of tourism pressure in spaces designated for recreational uses and tourism places [64]. The TCC concept, although traceable to Sumner [65] in the mid-1930s [66], began to gain consistency later, during the 1970s. These elaborations subsequently gave rise to a broad corpus of empirical research and theoretical reflections, while finding isolated previous contributions [67,68,69], as well as in other seminal works [70,71]. Reflections on TCC remain more central than ever on the conceptual level today, as demonstrated by the overtourism debate and concerns regarding the management of sensitive places [4,18,72,73,74]. On the operational level, numerous management techniques have been developed, both quantitative and qualitative [75,76,77], although critical contributions toward attempts to define carrying capacity in purely numerical terms have not been lacking [78].

In the 1980s, in light of problems that emerged in previous decades, attention to the multi-dimensional and multi-spatial impacts of tourism and carrying capacity gave impetus to a conception of tourism planning as a complex and integrated system. Growing tourism intensification prompted researchers and policymakers to reflect on the impact this expansion had on natural resources and local communities, making tourism planning more attentive to sustainability necessary. The diffusion of the sustainable development concept, promoted by the Our Common Future report [79], found application in tourism beginning in the 1990s, giving rise to an extensive debate on the definition of sustainable tourism [80]. This debate, while having generated relevant contributions, is still marked by persistent conceptual ambiguity, which feeds the gap between theory and practice. Progressively, reflection on tourism sustainability has expanded, including different geographical scales of analysis of implications (for example, as evidenced by studies on tourism water footprint [81]) and interweaving with the development of more sustainable forms of tourism [35,82,83].

Community-oriented planning frameworks, pioneered in Murphy [60], have proven more sensitive to the diversity of local contexts and coherent with sustainable development principles. In theory, such models support bottom-up participatory practices in line with pre-existing socio-territorial conditions and with a concept of development endogenously conceived on a local basis. This translates into pathways of local community involvement in creating authentic tourism experiences and discovering intangible cultural capital; at the same time, community-oriented planning frameworks aim to improve local living standards and monitor social/perceptual carrying capacity. Hall [84] introduces the concept of “sustainable tourism planning” as an extension of Getz’s work [61]. This concept refers to a tourism planning approach that integrates elements of pre-existing traditions and emphasizes holistic practices, management cooperation, and attention to the environmental and social components of development, with a focus on strategic planning.

Murphy [60] argues that “planning is concerned with anticipating and regulating change in a system, to promote orderly development so as to increase the social, economic, and environmental benefits of the development process.” Williams [57] adds that planning requires “an ordered sequence of operations and actions that are designed to realize one single goal or a set of interrelated goals”. In this sense, tourism planning can be seen as a process through which to channel broader public interest objectives [61]. Williams [57] identifies some specific objectives of tourism planning, such as the integration of tourism with other economic sectors; the direction and control of physical development patterns; and the creation of harmonious social and cultural relationships between tourists and local populations. In theory, according to the author, tourism-related planning allows for structuring the spatial allocation of tourism infrastructure and services among different geographical areas; organizing or reorganizing a territorial offering capable of anticipating tourism market dynamics; and promoting balanced and monitored tourism development, balancing costs and positively directing spillover effects among different territories [57]. Furthermore, as Inskeep [85] emphasizes, it enables the conservation of critical tourism resources, such as natural and cultural ones, preserving them and, when possible, improving them to ensure their future use. As reported by Hall [84], tourism planning contributes to mitigating tourism’s negative externalities—environmental, economic, and social. Additionally, it helps produce economic benefits and improve perceptions regarding the relationships between tourists and the local community.

Tourism planning is not necessarily a linear or simple process [84,86]. Planning activity can comprise different key areas of tourism intervention, operate at different geographical scales, involve multiple planning bodies (public or private) and at various levels (starting from the local level), and adopt distinct temporal horizons for different development, implementation, and evaluation phases [7]. Conversely, its absence, operational complexity, and the adoption of short-sighted visions can result in negative impacts with different types of implications for territorial resources and host communities [57,62,84]. In a broader vision, it is possible to consider that it aims to achieve, considering different scales, a balance between tourism growth, territorial development, and community [29,62,63]. Tourism planning is a process that highlights the need to consider tourism as a multi-sector activity, integrating tourism resource management and taking into account both physical and institutional aspects [85]. Previously, the importance of this integration had been emphasized especially by [87] and Getz [61], who proposed an interconnected and systematic perspective of tourism planning [88]. In this sense, it is a dynamic process [59], future-oriented, continuous, and participatory [8,57,61,84], and is, above all, constantly informed according to different implementation contexts, which are themselves complex.

Although studies on tourism growth limits and impact management have offered significant contributions, their operational application has often remained limited [73,89,90,91]. This gap between theory and practice is reflected in problems generated by tourism which, in many contexts, has continued—and continues today—to be configured as an unplanned activity [48,63,88]. However, the perpetuation of extreme situations, such as overtourism, has reinforced the urgency of integrating the tourism phenomenon with other functions and levels of planning. It is increasingly clearly recognized that tourism cannot be treated as an uncontrolled economic force, but must be coordinated with territorial dynamics. The heterogeneous nature of tourism, both from spatial and functional perspectives, has indeed intensified interest in its territorial impacts, processes of diffusion or redistribution of effects, and its role in local development [4].

Reading socio-economic and tourism dynamics at the territorial scale is complex and requires being addressed through in-depth analyses, integrated multi-level planning processes, management models, and specific evaluation techniques. Such considerations have contributed to strengthening the role of strategic planning, emphasizing the need to integrate tourism activity within multiple planning frameworks and operate at greater systemic and temporal scales [92]. In the tourism context, strategic planning, borrowed from management studies [93], is seen as a process that “requires some estimated perception of the future” [62] and is oriented toward favoring the proactivity of destination places in a systematic way [92].

Strategic planning assumes a higher and guiding role suitable for supporting a more territorially balanced and oriented vision of development [7,60]. Hall [84] emphasizes that the application of sustainability requires a balance between long-term temporal horizons and short-term operational constraints, which can be effectively addressed through strategic planning. Ladeiras et al. [94] affirm that a destination must develop a clear vision and undertake a participatory strategic planning process at multiple levels (national, regional, and local) to ensure sustainable tourism development. The pursuit of sustainability objectives in tourism is indeed linked to a structured planning process that adopts a circular causality perspective [93,95,96]. This goal- and context-oriented process also considers endogenous factors that influence strategy generation, requiring robust coordination mechanisms to support the participation of multiple stakeholders and multi-sectoral and multi-actor coordination [92,96,97]. This approach responds to community needs and integrates conventional planning as part of a continuous and dynamic process [92].

3. Materials and Methods

3.1. Area of Study and Territory Calibration

The analytical process first involved a recalibration of the area of relevance, with an extension of the Via Appia’s radius of influence beyond the initial boundaries, in order to include a wide variety of surrounding territories. This approach allowed for the integration of areas not directly connected to the main route but nevertheless relevant in relation to morphological and physical specificities, providing a broader and more representative understanding of the considerable territorial complexity of the study area.

It is plausible to conceive the Via Appia. Regina Viarum route as a complex set of tourism destinations, characterized by undefined entry and exit points, where tourists and residents share resources and structures. Inevitably, this set of tourism destinations does not exclusively concern municipalities directly crossed by the route, but extends to a broader territorial dimension. A procedure was executed to extend the radius of influence of the serial site that finds support in the concept of range offered by Christaller’s model [98,99]. The 22 sections (components) constitute “central goods,” from which a hypothetical maximum range extending in a radius reaching 25 km distance was delineated. At the same time, the “central goods” are reinterpreted in the geographical–territorial model as “gravitational centers” around which areas equipped with a complex of infrastructures and services connected to them by functional relationships tend to gravitate. The polygonal approach of the Thiessen method was used to identify gravitational areas; therefore, through the digital geometric tool, space was distributed into areas of relevance, attributing to each section the area closest to it. The association of municipalities belonging to each of the 22 areas of relevance was then optimized according to territorial morphology and the local road system. In some cases, the existence of contiguous sections resulted in an aggregation of areas.

The result achieves a spatialization of the range defined in 13 new gravitational areas comprising 614 municipalities, compared to the original 74 comprising the nominated site and each buffer zone [36] (

Table 1. Connection between the 22 sections to be enhanced and 13 gravitational areas. Source: Authors’ elaboration from MiC direct provision and ISTAT data [100].

Areas	Routes	Denomination of the 22 Routes		Population	N° Municipalities	Surface (ha)
Area 1	1	L’Appia a Roma dal I al XIII miglio		3,288,724	21	1903
Area 2	2	L’Appia ai Colli Albani		587,028	28	1312
	3	L’Appia dal XIX al XXIV miglio, con diramazione per Lanuvium
Area 3	4	L’Appia nella Pianura Pontina, con diramazione per Norba		314,356	23	1466
Area 4	5	Tarracina e il superamento del passo di Lautulae		235,384	28	1406
	6	L’Appia a Fundi
	7	L’Appia al valico di Itri
	8	L’Appia dal miglio LXXXIII a Formiae
Area 5	9	Minturnae e il superamento del Garigliano		197,736	33	1289
	10	L’Appia da Sinuessa al pagus Sarclanuse
Area 6	11	L’antica Capua		3,144,352	138	2059
Area 7	12	Beneventum e l’Arco di Traiano		574,310	175	4340
	13	L’Appia Traiana da Beneventum a Aequum Tuticum
	19	L’Appia sul percorso da Beneventum ad Aeclanum
Area 8	14	L’Appia nell’alta Valle del Bradano		141,825	36	2957
Area 9	15	L’Appia sul percorso del tratturo tarantino		332,006	15	2947
Area 10	16	Tarentum		688,744	39	3007
	17	L’Appia da Mesochorum a Scamnum
Area 11	18	Brundisium		594,585	29	2260
	22	L’Appia Traiana lungo la costa adriatica, passando per Egnathia
Area 12	20	L’Appia Traiana da Aecae a Herdonia		256,035	22	2425
Area 13	21	L’Appia Traiana a Canusium e il percorso dell’Ofanto		1,106,147	27	3263
		Total		11,461,232	614	30,634

; Figure 1). The range area reflects both elements of heterogeneity in the spatial distribution of population, infrastructure, and activities, as well as the different degree of tourism exposure of the territories. Overall, it covers an area of 30,634 km² and has a population of 11,461,232 residents, 72.4% of the total residents in the 4 regions [100]. Of the 614 municipalities, 64 are cities or large urban areas with high population intensity, equal to 55% of residents in the entire reference area.

The demographic dynamics within the system follow the developments of a space highly conditioned by the existence of different factor endowments. More than half of the municipalities (382 municipalities) included in the system belong to mountain and inland hill areas: these represent 21% of the population considered.

According to ISTAT (Istituto Nazionale di Statistica) [100] parameterized scheme on the degree of urbanization, the range area identifies the territory in three types of municipalities: (1) 64 “Cities” or “Densely populated zones”, (2) 227 “Small towns and suburbs” or “zones with intermediate population density”, (3) 323 “Rural zones” or “Sparsely populated zones”. Respectively, the municipalities thus classified represent 54.6%, 30.5%, and 14.9% of the resident population. The cities of Rome and Naples alone (32.5% of total residents) condition the population distribution to such an extent that they bring their respective areas (area 1; area 6) to account for 56.1% of the entire range. Such highly polarized demographics has repeatedly influenced the construction of the analysis model.

3.2. TCC Estimation Model

TCC is a holistic and multi-objective concept for which no universally recognized measure or univocal methodological approach exists. Both in theoretical studies and in applied contexts, TCC has followed the evolution of sustainability analysis dimensions, thus incorporating various carrying capacity components: ecological [89,101], economic [102,103], socio-anthropic [53,104,105], and cultural and behavioral [106].

In theory, TCC estimation should result from the combination of both quantitative descriptive elements and qualitative elements oriented toward achieving a balance between tourism growth, territory, and local community [26,107]. Simultaneously, quantitative elements are based on the construction of complex indicator sets capable of operating, in time and space, within a systemic framework that encompasses, for a specific context, tourism aspects and territorial dimensions [25]. Authors such as Wagar [67], Lime [108], Mathieson, Wall [53], and Getz [109] have led to consideration of the existence of different carrying capacities for different subsystems of a destination. For example, Getz [109] identifies thresholds for physical, economic, ecological, socio-cultural, political/administrative, and perceptual carrying capacity.

TCC is intrinsically linked to the notion of sustainability, representing a model that informs about a territory’s/tourist site’s capacity to sustain a certain level of use over time. A tourism destination can be considered sustainable only when it operates within the limits of its carrying capacity [110,111]. Furthermore, it is not limited to defining a static objective but configures itself as a continuous and adaptive management process capable of responding flexibly to emerging challenges and ensuring constant monitoring of the balance between tourist flows and the territorial system’s capacity to absorb and sustain them [8,16].

Over the years, numerous TCC estimation techniques have been proposed involving one or more analysis components. In order to develop a model consistent with the geographical context of reference, existing methodologies for estimating TCC were examined, integrating the methodological frameworks commonly used to monitor tourism pressure and its sustainability across different territorial components. Studies on the subject reveal not only the impossibility of adopting a one-size-fits-all approach for all destinations, but also—consistent with the components of sustainability [24]—the need to account for the multi-dimensionality of tourism impacts, which involve both the physical environment and the social and economic spheres. These components provide a foundation for evaluating and analyzing TCC as dimensions that, while analytically distinct, are deeply interrelated [107].

Some of the methods considered are presented in the following

Table 2. Examples of TCC estimation methods and observed sustainable tourism frameworks.

References	Geographical Area of Application	Case Study	Methodology	Key Strengths
Costa e Van der Borg [103]; Canestrelli e Costa [112]	Urban historic centers	Venice (Italy)	Mathematical model for calculating economic-type TCC.	Offers a robust economic calculation method for the quantitative determination of TCC.
Cifuentes [45]	Protected areas	Monumento Nacional Guayabo (Costa Rica)	Quantitative model with three main analytical levels for measuring TCC at the physical/infrastructural level.	Systemic approach; provides a stepwise framework to calculate CCT, applicable also in contexts different from the case study.
UNEP [113]	Costal areas	Vis (Croatia); Archipelago Brijuni (Croatia); Island of Rhodes (Greece)	Provides general guidelines for the estimation of TCC, integrated with considerations of tourism planning.	Interpretation of CCT structured in procedural steps; provides guidelines for carrying capacity assessment based on locational, spatial, ecological, social, cultural, and market criteria useful for analysis and the selection of optimal solutions.
Mansfeld e Jonas [114]	Rural areas experiencing emerging tourism	Kibbutz Yiron (Northern Israel)	Qualitative model for estimating the level of satisfaction of resident citizens.	Overcoming the traditional view of TCC as a fixed numerical limit; integration of the perspective of resident communities.
Navarro [20]	Coastal area	Eastern Costa del Sol (Spain)	Quantitative and spatial analysis.	Holistic perspective; flexible method adaptable to other territorial contexts; integration of quantitative, qualitative, and spatial data.
European Commission [115]	European tourist destinations of varying scale (local or regional)	Example: Clare (Ireland) [116]	Indicator system to monitor and measure the impacts of tourism.	Systemic and multi-dimensional approach; flexibility and adaptability to different types of destinations and territorial contexts; active involvement of stakeholders.
Peeters et al. [117]	Overtourism case studies in the EU	-	Development of six overtourism indicators.	Addresses the complex phenomenon of overtourism in the European context; recommends policies on measures and collaboration between stakeholders and policymakers; applicable to various types of destinations; suggests the most relevant indicators for overtourism.
ESPON [26]; Zekan et al. [25]	European tourist destinations	Pilot areas	A mix of quantitative and qualitative tools, including spatial assessments.	Structured and participatory methodology; adaptability to larger scales; integrates tourism exposure indicators and territorial sensitivity indicators; simultaneously addresses regional sustainability and tourism development; applicable to a variety of tourist destinations.
Fondazione Dolomiti Dolomiten Dolomites Dolomitis UNESCO [118]	Serial sites	Dolomites (Italy)	Method for assessing the specific hresholds of tolerance within which recreational activities and their related transformations remain sustainable.	Overcoming the numerical logic of carrying capacity; contributes to outlining various adaptive management frameworks for regulating and monitoring the impacts of tourism.
Villarán et al. [24]	Inhabited tourist destinations	Basque Country (Spain)	Multi-dimensional qualitative–quantitative analysis.	Holistic and multi-dimensional approach; model adaptability; allows for assessing carrying capacity at a given moment.

, which also outlines their key features.

The estimation of the Tourism Carrying Capacity (TCC) of the serial site is inspired by and adapts the ecosystem-based model developed by Cifuentes [45,46,119], originally conceived for defining carrying capacity in parks and protected areas [46,101,120,121,122]. This model has subsequently been tested in more complex contexts, thus broadening its traditional scope of application [24,51]. The spatial flexibility of this model, combined with its consideration of multiple components of Tourism Carrying Capacity—excluding aspects related to social and psychological perception—justifies its use in non-protected areas characterized by high functional heterogeneity.

The choice of Cifuentes’ ecosystem-based model is motivated by its capacity to adapt to complex and articulated contexts such as serial sites, integrating environmental, infrastructural, and managerial dimensions. However, an adaptation of the original model is proposed here from a geographical–territorial perspective, capable of more precisely capturing the complexity of the case study. This adaptation substantially retains the theoretical/conceptual framework of Cifuentes’ method, while extending its analytical scope.

In this regard, the model is recalibrated to capture the plurality of factors that, within a broad and highly heterogeneous context, affect the TCC, which is calculated at two scales: municipal and areal. Tourism Carrying Capacity is thus addressed by focusing on the 614 municipalities and 13 areas through an integrated reading of anthropic-settlement, natural, infrastructural, and cultural components. The decision to calculate TCC also at the areal level, alongside the geospatial considerations discussed in Section 3, enables the generation of results grounded in the developed methodology, offering a preliminary exploratory framework for investigating the different areas.

In particular, this approach is useful for highlighting the varying tourism absorption capacities among the identified areas and for calibrating, in subsequent studies, specific models for measuring the carrying capacity of each territory. The outcome is a hybrid model based on the definitions of Physical Carrying Capacity (PCC), Real Carrying Capacity (RCC), and Effective or Allowable Carrying Capacity (ECC) proposed by Cifuentes. These are constructed in relation to specific correction factors that capture both the tourism exposure of the municipalities involved in the Via Appia and their territorial characteristics, as recommended by ESPON researchers [26]. Like the original approach, our model is articulated according to a procedural sequence, in which each successive level of carrying capacity is determined by adjusting the previous level. In the adapted model, the PCC is greater than or equal to the RCC, which in turn is greater than or equal to the ECC (also referred to as TCC). The geographical–territorial model is illustrated in Figure 2, which summarizes the procedural sequence.

The following subsections detail the model, describing the specific phases leading to the estimation of ECC. Intermediate territorial findings are presented, based on the Ecological endowment composite index (EEI) and the Infrastructure Endowment composite index (IEI), which constitute the correction parameters for the three different levels of carrying capacity. EEI and IEI are considered, in accordance with the geographical scale of reference, as territorial threshold values beyond which tourism pressure results in overcapacity, likely triggering diseconomies. After illustrating the ECC estimation method, the scenario-building method is described, based on the application of specific variation coefficients.

3.2.1. Physical Carrying Capacity (PCC)

Physical Carrying Capacity (PCC) is defined as “the maximum number of visitors who can attend physically in a given place and time” [45] (pp. 10). For the case under examination, it is interpreted in terms of receptive capacity; specifically, its determination is based on the Gross Occupancy rate (GOr) of tourist accommodation establishments, calculated both at the municipal scale and across gravitational area. The specific calculation is shown in the Formula (1).

GOr_t = (eNS_t/BPa_t)·100

(1)

where GOr is the Gross Occupancy rate in hotels and extra hotels; eNS constitutes the estimated number of nights spent in hotels and extra-hotels; BPa is the potential number of the bed places (available) on offer by day·365; and t is the reference year.

The Physical Carrying Capacity (PCC) is expressed by Formula (2):

PCC = f (GOr)

(2)

The index provides, at specific times and expressed as a percentage, the level of pressure exerted by fluctuations in tourist arrivals on available tourism supply. Therefore,

• PCC < 20% Very low impact;
• 21% ≤ PCC < 40% Low impact;
• 41% ≤ PCC < 60% Moderate impact;
• 61% ≤ PCC < 80% High impact;
• 81% ≤ PCC < 100 Very high impact;
• PCC ≥ 100 Congestion and overuse.

This procedure was developed to calculate the PCC of tourist accommodation (both hotels and extra-hotels).

3.2.2. Real Carrying Capacity (RCC)

Real Carrying Capacity (RCC) is defined as “the maximum limit of visits determined from the PCC of a site, adjusted by correction factors reflecting the specific characteristics of the site” [45] (pp. 12). For this study, the RCC is derived by applying to the PCC a series of ecological correction factors described through indicators of environmental and anthropic pressure.

Ecological correction factors (Ecf) were normalized and weighted, with their sum constituting the Ecological Endowment composite index (EEI), as shown in Formula (3):

$$\mathrm{E}\mathrm{E}\mathrm{I}=\sum _{\mathrm{i}=1}^5{\mathrm{E}\mathrm{c}\mathrm{f}}_{\mathrm{i}}$$

(3)

where Ecf₁ is the population density; Ecf₂ is the ecological efficiency; Ecf₃ is the ecological pressure; Ecf₄ is the environmental endowment; and Ecf₅ is the unconsumed land. As a result, the Real Carrying Capacity (RCC) is expressed by Formula (4):

RCC = f (PCC, EEI)

(4)

3.2.3. Effective Carrying Capacity (ECC)

Effective Carrying Capacity (ECC) corresponds to “the maximum number of visits that can be allowed, given the capacity to manage and distribute them […] the ECC is determined by comparing the RCC with the management capacity of the protected area administration” [45] (pp. 19).

For the calculation of the ECC, additional correction factors related to physical and cultural infrastructure systems were applied to the RCC Formula (4). The Infrastructure correction factors (Icf) were normalized and weighted, with their sum constituting the Infrastructure Endowment composite index (IEI), as shown in Formula (5):

$$\mathrm{I}\mathrm{E}\mathrm{I}=\sum _{\mathrm{i}=1}^5{\mathrm{I}\mathrm{c}\mathrm{f}}_{\mathrm{i}}$$

(5)

where Icf₁ is the prevailing tourism supply; Icf₂ is the tourism accommodation offer intensity; Icf₃ is the material cultural heritage endowment; Icf₄ is the station intensity for passenger transport; and Icf₅ is the local public transport offer. For this purpose, the Effective Carrying Capacity (ECC) is given by the Formula (6):

ECC = f (RCC, IEI)

(6)

In the geographical–territorial model, the ECC is, therefore, the result of a serial site’s capacity to accommodate tourist demand, considering both the environmental and infrastructural territorial characteristics. Its estimation represents the actual and weighted measure of the Tourism Carrying Capacity (TCC), obtained through a gradual process of geographical–territorial adaptation of the model proposed by Cifuentes [45]. In line with the existing literature, this study treats the terms ECC and TCC as interchangeable.

3.2.4. The Correction System Based on EEI and IEI: Indicators, Weighting, and Spatial Representation

The use of the EEI and the IEI enables the adjustment of the various levels of carrying capacity, ultimately deriving the ECC for each municipality and each area.

The estimates were conducted based on the construction of a database developed using statistical information available from official secondary sources (

Table 3. Composite indices: set of indicators.

Correction Factors	Indicators	Description	Variables	Reference	Source	Year
Ecological Endowment Composite Index (EEI)
Ecf1	Population density	Ratio of administrative municipality’s land area to resident population [number of inhabitants per sq. km]	Resident	[123]	ISTAT	2021
			Land area (sq. km.)	[123]	ISTAT	2021
Ecf2	Ecological efficiency	Sorted municipal waste fraction detected in the administrative municipality [%]	Separate waste [t]	[124]	ISPRA	2020
Ecf3	Ecological pressure	Municipal waste generated by municipal inhabitants [kg]	Municipal solid waste (MSW) [t]	[124]	ISPRA	2020
Ecf4	Environmental endowment	Presence of protected areas (EUAP2010) [%]	Protected areas (EUAP2010) [h]	[125]	ISTAT	2010
Ecf5	Unconsumed land	Share of unsealed land [%]	Unconsumed land [h]	[124]	ISPRA	2019
Infrastructure Endowment Composite Index (IEI)
Icf1	Prevailing tourism supply	Relationship between hotel receptivity with non-hotel receptivity	Bed-places in hotel and non-hotel accommodations	[123]	ISTAT	2019
Icf2	Tourism accommodation offer intensity	Ratio of total beds in accommodations to municipal population	Bed-places in hotel and non-hotel accommodations	[123]	ISTAT	2019
Icf3	Material cultural heritage endowment	Number of archaeological assets and number of architectural assets	Number of archaeological assets and number of architectural assets	[126]	MIC	2017
Icf4	Station intensity for passenger transport	Ratio of the number of railway stations and metro stations to the territorial area of the administrative municipality [per 100 sq. km]	Number of stations belonging to the categories Platinum, Gold, Silver, and Bronze	[127]	ISTAT	2015
				[128]	RFI	2022
Icf5	Local public transport offer	Ratio of the number of vehicles for local public transport (LPT) to residents	Number of bus and trolleybus cars intended for passenger transport	[127]	ISTAT	2015

).

It is important to note that the analytical model requires the search and selection of a significant volume of data, which was not always fully reflected in the available sources. The collected data exhibit adequate coverage and territorial representativeness at the municipal level. Furthermore, to enable the implementation of the spatial analysis approach, the data were precisely georeferenced across the territory.

The selection of the indicators used to construct the correction factors took into account, in the context of complex territorial systems characterized by varying degrees of tourism exposure, the multi-dimensionality of tourism impacts [25,26,27,29,30,83,107,129,130,131,132]. The indicators provide raw data on environmental, anthropic, and infrastructural components, elaborated at the municipal scale. Adopting a coherent approach in relation to the phenomenon being measured, the polarity of each elementary indicator was defined.

The EEI and IEI are calculated as the sum of normalized indicators on a scale from 0 to 1 and recalibrated through the assignment of weights.

The spatial representation of the two indices (Figure 3 and Figure 4) provides an informative framework that enables the development of preliminary observations on the territorial and tourism-related aspects of the area influenced by the “Via Appia, Regina Viarum”. In both cases, the different shades of color used in the maps reflect variations in endowment levels: darker colors indicate areas with higher endowment, while lighter colors represent zones with lower endowment. This representation facilitates the visual analysis of areas with greater potential to support the tourism development of the Via Appia, while also serving as a useful tool for interpreting the spatial distribution of ecological and infrastructural resources.

3.3. Dynamic Estimation of Tourism Carrying Capacity

The methodology has been further developed to account for the dynamic nature of the TCC tool.

The aim of this research is to test the tourism absorption capacities of the areas that compose a serial site. In particular, it is assumed that tourist pressure on territorial endowments (both infrastructural and ecological) will progressively increase over time.

Therefore, through the application of specific variation coefficients in the growth of tourist arrivals and bed places, modifications are induced in the PCC values of the municipalities. In contrast, the respective ecological and infrastructural correction factors, described by the EEI and IEI, are kept constant over time, acting as parameters to determine the threshold values. Building on these assumptions, the procedural framework of the three levels of carrying capacity—physical (PCC), real (RCC), and effective (ECC)—facilitates the evaluation of the territories’ ability to absorb potential increases in tourism over time, under various scenario conditions.

Thus, the scenario assumptions enable the estimation of the different reactions in the ECC of the areas comprising the serial site observed in the following years. This analysis identifies the decade 2024–2034 as the forecast period for estimating the effects of UNESCO recognition.

The ECC, as the PCC, is expressed in percentage terms, values exceeding 100% represent situations of congestion and overuse:

• ECC < 20% Very low impact;
• 21% ≤ ECC < 40% Low impact;
• 41% ≤ ECC < 60% Moderate impact;
• 61% ≤ ECC < 80% High impact;
• 81% ≤ ECC < 100 Very high impact;
• ECC ≥ 100 Congestion and overuse

3.3.1. Scenario 1—Ordinary Dynamics

Scenario 1—Ordinary Dynamics considers the evolution of the ECC in the absence of specific policies aimed at fostering tourism growth. This Scenario projects the trends in tourism demand and accommodation supply observed during the 2014–2019 period onto the forecasting period. This choice reflects the researchers’ intent to present a measure as neutral as possible regarding the consequences of the SARS-CoV-2 pandemic. The assumption is that during the forecast period, the system will continue to exhibit five-year growth trends of 7.3% in bed capacity and 7.9% in tourist arrivals for the hotel sector, and 30.2% and 30.7%, respectively, for the accommodation sector (including both hotel and extra hotel facilities). Scenario 1 is represented by Formulas (7) and (8):

ECC₂₀₂₉ = f (eNS₂₀₂₄, eBP₂₀₂₄, Trend_2014–2019, Cf)

(7)

ECC₂₀₃₄ = f (eNS₂₀₂₉, eBP₂₀₂₉, Trend_2014–2019, Cf)

(8)

where eNS is estimated nights spent at tourist accommodation establishments; eBP is estimated bed places in tourist accommodation establishment; Trend represents the trend of nights spent and bed places based on ISTAT data 2014–2019 [123]; and Cf is the correction factors used for the calculation of the EEI and IEI.

In order to assess the impact of UNESCO recognition on the evolution of ECC, adjustments were made to the trends of Scenario 1, and two additional scenarios were proposed, as described below.

3.3.2. Scenario 2—UNESCO Effect

Scenario 2—UNESCO effect examines the evolution of the ECC following the potential increase in pressure resulting from the site’s designation as a World Heritage Site. Scenario 2 assumes that, with UNESCO recognition, the ECC will grow at a more pronounced rate compared to Scenario 1. The development of this Scenario was based on tourism trends observed in similar serial sites. This led to the formulation of a five-year growth coefficient of 18.75%, defined within the study as the UNESCO Coefficient (C_UNESCO). Scenario 2 is expressed by Formulas (9) and (10):

ECC₂₀₂₉ = f (eNS₂₀₂₄, eBP₂₀₂₄, Trend_2019–2024, Cf, C_UNESCO)

(9)

ECC₂₀₃₄ = f (eNS₂₀₂₉, eBP₂₀₂₉, Trend_2019–2024, Cf, C_UNESCO)

(10)

where eNS is estimated nights spent at tourist accommodation establishments; eBP is estimated bed places in tourist accommodation establishment; Trend represents the trend of nights spent and bed places based on ISTAT data 2014–2019; Cf is the correction factors used for the calculation of the EEI and IEI; and C_UNESCO represents a five-year growth coefficient of 18.75 percent.

3.3.3. Scenario 3—Overflowing/Stress Test

Scenario 3—Overflowing/Stress Test explores the hypothesis that the ECC, in the presence of UNESCO recognition, grows to such an extent that it pushes the territorial tourism hospitality systems towards a situation of widespread overflowing. In order to analyze Scenario 3, a five-year growth coefficient of 50% was applied, referred to in this study as the Overflowing Coefficient (C_OVERFLOWING). Scenario 3 is expressed by Formulas (11) and (12):

ECC₂₀₂₉ = f (eNS₂₀₂₄, eBP₂₀₂₄, Trend_2019–2024, Cf, C_OVERFLOWING)

(11)

ECC₂₀₃₄ = f (eNS₂₀₂₉, eBP₂₀₂₉, Trend_2019–2024, Cf, C_OVERFLOWING)

(12)

where eNS is estimated nights spent at tourist accommodation establishments; eBP is estimated bed places in tourist accommodation establishment; Trend represents the trend of nights spent and bed places based on ISTAT data 2014–2019 [123]; Cf is the correction factors used for the calculation of the EEI and IEI; and C_OVERFLOWING represents a five-year growth coefficient of 50 percent.

4. Results

Table 4. Effective Carrying Capacity (ECC) in percentage values based on the Physical Carrying Capacity (PCC) calculated for the hotel and extra hotel (accommodation establishment). Source: Authors’ elaboration.

Effective Carrying Capacity (ECC)
Accommodation Establishments Hotel and Extra Hotel (Value %)					Ordinary ECC Scenario 1			UNESCO ECC Scenario 2			Overflowing ECC Scenario 3
Area	Nights Spent Trend	Bed Places Trend	PCC		f (PCC, trend, EEI, IEI)			f (PCC, trend, EEI, IEI, Coeff. UNESCO)			f (PCC, trend, EEI, IEI, Coeff. Overflow.)
			ISTAT Data		Estimated Value			Estimated Value			Estimate Value
	2014–2019	2014–2019	2014–2019	2019–2024	2019–2024	2024–2029	2029–2034	2019–2024	2024–2029	2029–2034	2019–2024	2024–2029	2029–2034
Via Appia. Regina Viarum as one statistical unit	30.7	30.2	26.4	26.5	26.6	26.7	26.8	26.6	30.5	35.1	26.6	36.9	51.3
Area 1	29.8	46.5	38.2	33.9	30.0	26.6	23.6	30.0	30.4	30.9	30.0	36.8	45.2
Area 2	59.5	3.8	5.3	8.1	8.3	12.7	19.6	8.3	19.3	30.1	8.3	19.8	31.4
Area 3	0.0	0.0	19.0	10.5	10.5	10.5	10.5	10.5	10.6	10.7	10.5	10.7	10.9
Area 4	1.6	13.0	15.9	14.3	12.7	11.4	10.3	12.7	12.2	11.7	12.7	13.4	14.0
Area 5	100.0	22.5	5.9	12.8	11.6	19.0	31.0	11.6	34.4	56.8	11.6	35.0	58.8
Area 6	38.8	5.8	31.2	41.0	44.7	58.6	76.9	44.7	74.3	102.7	44.7	80.6	120.9
Area 7	89.3	6.4	4.6	8.2	10.5	18.7	33.3	10.5	27.0	50.1	10.5	28.8	56.7
Area 8	0.0	0.0	13.0	11.8	11.8	11.8	11.8	11.8	12.9	14.0	11.8	14.6	17.9
Area 9	55.8	22.3	15.1	19.2	18.7	23.8	30.3	18.7	32.4	42.9	18.7	34.5	48.8
Area 10	10.9	31.2	16.3	13.8	10.7	9.0	7.6	10.7	10.1	8.7	10.7	10.4	9.3
Area 11	42.9	26.2	16.6	18.8	16.7	18.9	21.4	16.7	25.0	29.3	16.7	26.4	32.8
Area 12	29.9	24.8	16.0	16.6	13.3	13.9	14.4	13.3	18.0	18.8	13.3	18.0	18.8
Area 13	39.8	32.9	22.1	23.2	19.5	20.5	21.6	19.5	26.7	29.2	19.5	28.4	33.1
		High Impact							Scenario 1. Ordinary ECC
		Very high impact							Scenario 2. UNESCO ECC
		Overflowing							Scenario 3. Overflowing ECC
		To limit model bias, the negative trend was treated as a null trend.

and

Table 5. Effective Carrying Capacity (ECC) in percentage values based on the Physical Carrying Capacity (PCC) calculated for the hotel sector. Source: Authors’ elaboration.

Effective Carrying Capacity (ECC)
Hotel Sector (Value %)					Ordinary ECC Scenario 1			UNESCO ECC Scenario 2			Overflowing ECC Scenario 3
Area	Nights Spent Trend	Bed Places Trend	PCC		f (PCC, Trend, EEI, IEI)			f (PCC, Trend, EEI, IEI, Coeff. UNESCO)			f (PCC, Trend, EEI, IEI, Coeff. Overflow.)
			ISTAT Data		Estimated Value			Estimated Value			Estimated Value
	2014–2019	2014–2019	2014–2019	2019–2024	2019–2024	2024–2029	2029–2034	2019–2024	2024–2029	2029–2034	2019–2024	2024–2029	2029–2034
Via Appia. Regina Viarum as one statistical unit	7.9	7.3	38.7	38.9	39.1	39.4	39.6	39.1	46.2	54.5	39.1	57.6	84.8
Area 1	4.6	8.9	49.4	47.4	45.6	43.8	42.1	45.6	51.6	58.5	45.6	64.7	91.9
Area 2	0.0	0.0	19.1	16.2	16.2	16.2	16.2	16.2	16.5	16.9	16.2	17.1	18.0
Area 3	0.0	0.0	32.3	19.7	19.7	19.7	19.7	19.7	19.8	19.9	19.7	20.0	20.4
Area 4	0.0	0.8	26.4	23.5	23.4	23.2	23.0	23.4	24.6	25.9	23.4	26.9	31.1
Area 5	93.1	0.0	9.4	20.7	22.8	44.1	85.2	39.9	77.9	152.2	39.9	79.4	157.8
Area 6	16.3	4.0	42.1	47.1	48.2	53.9	60.3	52.7	62.7	74.5	52.7	68.9	90.2
Area 7	40.0	0.0	5.9	8.6	10.0	14.1	19.7	12.1	17.9	26.4	12.1	19.4	31.1
Area 8	0.0	0.0	13.4	10.2	10.2	10.2	10.2	10.2	11.1	12.0	10.2	12.5	15.4
Area 9	64.3	0.0	19.9	33.0	40.2	66.1	108.7	54.2	92.6	158.0	54.2	98.3	178.4
Area 10	1.3	20.8	21.5	18.1	15.0	12.6	10.5	15.1	13.0	11.2	15.1	13.5	12.1
Area 11	28.6	12.3	23.1	26.4	25.4	29.1	33.3	30.3	36.1	43.0	30.3	38.4	48.6
Area 12	18.3	7.1	20.9	23.1	21.5	23.8	26.3	25.5	28.1	31.1	25.5	28.1	31.1
Area 13	18.9	13.8	27.3	28.5	26.5	27.7	28.9	29.8	32.6	35.7	29.8	35.1	41.2
		High Impact							Scenario 1. Ordinary ECC
		Very high impact							Scenario 2. UNESCO ECC
		Overflowing							Scenario 3. Overflowing ECC
		To limit model bias, the negative trend was treated as a null trend.

show the values obtained from applying the TCC calculation method to each of the 13 areas and to the entire area of influence of the Via Appia. The first two columns report the trends in tourist arrivals and bed availability for the period 2014–2019; the following columns show the PCC values calculated for the years 2014 and 2019. The last three columns present the ECC values obtained for each of the three scenarios considered in time intervals 2019–2024, 2024–2029, and 2029–2034.

The first step of the analysis involved calculating the PCC as the initial step in determining the ECC. The PCC was calculated for (i) the total set of accommodation facilities; (ii) the hotel sector; and (iii) the non-hotel sector. The calculation was performed separately for each category. However, the discussion focuses on the first two categories, and the cartographic representation of the Tourism Carrying Capacity is based on the PCC calculated for the hotel sector. This is due to the greater structural rigidity of the hotel sector in responding to fluctuations in tourist demand, which generally requires longer planning and adaptation times. The lower resilience of the hotel supply is clearly visible when comparing the trends reported in the first two columns of Table 4 and Table 5. The latter also provides the specific ECC values calculated based on the physical carrying capacity of the hotel sector.

For the year 2019 only, the PCC corresponds to the ECC. The estimated Tourism Carrying Capacity is 26.5% (Table 4). It is important to note that in the same year, tourist arrivals across the entire accommodation system exceeded 48 million, with the Rome area (Area 1) alone accounting for 68%. Overall, tourist arrivals in the influence area increased by +30% compared to 2014 (36.7 million) (

Table A1. Nights spent in total accommodation establishments. Source: Authors’ elaboration.

Nights spent in Accommodation Establishments
Hotel and Extra Hotel (val. Absolute)
Area	ISTAT Data		Estimated nights Spent Ordinary Dynamics—Scenario 1			Estimated Nights Spent Unesco Effect—Scenario 2			Estimated Nights Spent Overflowing—Scenario 3
	2014	2019	2024	2029	2034	2024	2029	2034	2024	2029	2034
Via Appia. Regina Viarum as one statistical unit	36,764,517	48,057,386	62,819,059	82,115,039	107,338,120	62,819,059	93,893,613	140,339,742	62,819,059	113,524,568	205,157,923
Area 1	24,448,335	31,741,825	41,211,128	53,505,337	69,467,186	41,211,128	61,232,423	90,980,516	41,211,128	74,110,901	133,275,305
Area 2	474,698	757,176	1,207,748	1,926,441	3,072,806	1,207,748	1,950,961	3,151,526	1,207,748	1,991,828	3,284,938
Area 3	660,790	364,543	364,543	364,543	364,543	364,543	367,028	369,530	364,543	371,169	377,916
Area 4	1,681,729	1,709,054	1,736,823	1,765,043	1,793,722	1,736,823	1,871,139	2,015,842	1,736,823	2,047,965	2,414,847
Area 5	275,230	725,499	1,450,998	2,901,996	5,803,992	1,450,998	2,932,819	5,927,940	1,450,998	2,984,191	6,137,430
Area 6	3,475,430	4,824,556	6,697,399	9,297,260	12,906,360	6,697,399	9,794,739	14,324,504	6,697,399	10,623,871	16,852,310
Area 7	179,444	339,677	642,989	1,217,140	2,303,975	642,989	1,265,922	2,492,359	642,989	1,347,226	2,822,783
Area 8	132,494	112,530	112,530	112,530	112,530	112,530	122,219	132,742	112,530	138,367	170,136
Area 9	828,770	1,291,022	2,011,098	3,132,802	4,880,143	2,011,098	3,261,574	5,289,581	2,011,098	3,476,195	6,008,625
Area 10	1,048,726	1,162,705	1,289,072	1,429,172	1,584,499	1,289,072	1,460,723	1,655,231	1,289,072	1,513,307	1,776,549
Area 11	2,247,306	3,210,559	4,586,687	6,552,659	9,361,296	4,586,687	6,785,677	10,038,927	4,586,687	7174,041	11,220,925
Area 12	153,141	198,936	258,425	335,705	436,093	258,425	335,705	436,093	258,425	335,705	436,093
Area 13	1,158,424	1,619,304	2,263,546	3,164,099	4,422,939	2,263,546	3290,542	4783,498	2,263,546	3,501,280	5,415,823
	Scenario 1. Ordinary ECC. Via Appia. Regina Viarum as one statistical unit
	Scenario 2. UNESCO ECC, Via Appia. Regina Viarum as one statistical unit
	Scenario 3. Overflowing ECC, Via Appia. Regina Viarum as one statistical unit
	To limit model bias, the negative trend was treated as a null trend.

). Figure 5 shows the spatial distribution of Tourism Carrying Capacity values in the territory in 2019, highlighting the differences between the 13 areas and among individual municipalities. As previously mentioned, the cartographic evidence illustrates the results obtained starting from the hotel PCC. The color scale uses increasingly intense shades of orange to represent higher levels of Tourism Carrying Capacity. These figures highlight a highly heterogeneous territorial configuration of tourist demand, with a discontinuous tourism space [133], in which areas and municipalities show widely varying current levels of tourism exposure. Data on actual and estimated tourist arrivals, in absolute values, are provided in the Appendix A section (Table A1 and

Table A2. Night spent in hotels. Source: Authors’ elaboration.

Nights Spent in Sector Hotel
(val. Absolute)
Area	ISTAT Data		Estimated Nights Spent Ordinary Dynamics—Scenario 1			Estimated Nights Spent Unesco Effect—Scenario 2			Estimated Nights Spent Overflowing—Scenario 3
	2014	2019	2024	2029	2034	2024	2029	2034	2024	2029	2034
Via Appia. Regina Viarum as one statistical unit	3,073,8770	33,159,978	35,771,898	41,104,508	47,252,184	35,771,898	46,251,914	59,194,296	35,771,898	54,830,923	82,978,874
Area 1	21,468,871	22,460,767	23,498,490	24,584,158	25,719,985	23,498,490	28,990,125	35,765,163	23,498,490	36,333,403	56,178,765
Area 2	413,825	344,762	344,762	344,762	344,762	344,762	351,761	358,903	344,762	363,427	383,103
Area 3	430,994	262,422	262,422	262,422	262,422	262,422	264,211	266,012	262,422	267,192	272,049
Area 4	633,072	570,094	570,094	570,094	570,094	570,094	604,919	641,871	570,094	662,960	770,954
Area 5	199,919	386,142	745,830	1,440,565	2,782,441	745,830	1,456,409	2,843,981	745,830	1,482,815	2,948,043
Area 6	3,510,121	4,081,032	4,744,800	5,516,528	6,413,775	4,744,800	5,868,969	7,259,484	4,744,800	6,456,371	8,785,351
Area 7	121,757	170,420	238,532	333,867	467,305	238,532	351,964	519,338	238,532	382,126	612,161
Area 8	99,298	64,675	64,675	64,675	64,675	64,675	70,244	76,291	64,675	79,524	97,783
Area 9	470,477	773,025	1,270,131	2,086,910	3,428,933	1,270,131	2,168,238	3,701,394	1,270,131	2,303,785	4,178,641
Area 10	777,461	787,741	798,157	808,711	819,404	798,157	828,246	859,469	798,157	860,805	928,370
Area 11	1,571,449	2,021,548	2,600,566	3,345,427	4,303,634	2,600,566	3,477,544	4,650,263	2,600,566	3,697,740	5,257,809
Area 12	118,000	139,555	165,047	195,197	230,853	165,047	195,197	230,853	165,047	195,197	230,853
Area 13	923,526	1,097,795	1,304,948	1,551,192	1,843,901	1,304,948	1,624,087	2,021,274	1,304,948	1,745,579	2,334,992
	Scenario 1. Ordinary ECC. Via Appia. Regina Viarum as one statistical unit
	Scenario 2. UNESCO ECC, Via Appia. Regina Viarum as one statistical unit
	Scenario 3. Overflowing ECC, Via Appia. Regina Viarum as one statistical unit
	To limit model bias, the negative trend was treated as a null trend.

).

Starting from this baseline, what changes in Tourism Carrying Capacity levels could be observed at area and municipal scales under different scenario assumptions? Figure 6 illustrates the spatial distribution of the ECC estimated under the Ordinary Dynamics scenario in 2034, developed on the basis of the hotel PCC. In this case, tourist arrivals and bed availability were increased based on trends recorded in previous years. Tourist arrivals are forecast to reach approximately 107.3 million by 2034 (Table A1). As before, the territorial distribution is shown for the 13 areas and the municipalities within the scope.

The increase in tourist arrivals within the system results in moderate variations in territorial Tourism Carrying Capacity. Indeed, during the periods 2019–2024, 2024–2029, and 2029–2034, the carrying capacity remains between 26.6% and 26.8% when calculated using the PCC of all accommodation facilities (Table 4); and ranges between 39.1% and 39.6% when using the PCC of the hotel sector alone (Table 5). In both cases, the carrying capacity remains within low impact levels.

At the area level the increase in Tourism Carrying Capacity is more pronounced in the Capua Area (Area 6), where the ECC would rise from a moderate impact in 2019 (41.0%) to a high impact in 2034 (76.9%) (Table 4). When limiting the analysis to the initial PCC values of the hotel sector, the scenario immediately appears more critical in some areas. In this case, tourist arrivals in 2034 would reach about 47.2 million (Table A2). The Domitian Area (Area 5) would see the Tourism Carrying Capacity rise to 85.2%, while Area 9 would reach 108.7% (Table 5; Figure 6). In Area 5, increased tourist flows would compound an already high population density, further raising the territorial Tourism Carrying Capacity. Conversely, Area 9, which includes the Matera region, is more likely to face congestion and overuse risks due to limited initial infrastructure. However, such risks would only materialize if the tourist growth trend initiated by the Matera European Capital of Culture experience continues.

In all other areas, the Tourism Carrying Capacity on the territory remains at less significant levels. This is also reflected at the municipal level. In some cases, tourism movements—combined with low estimated flows and the territorial characteristics described by the EEI and IEI—suggest a higher absorption capacity of the areas.

Figure 7 shows the spatial distribution of ECC values estimated under the UNESCO Effect scenario, with projections for 2034 based on the hotel PCC. The application of the variation coefficient associated with the UNESCO effect results in an incremental estimate of tourist arrivals during the forecast periods, reaching approximately 140.3 million in the Appia’s influence area (Table A1). Even in this case, considering the area as a whole and using different calculation bases, the increase in Tourism Carrying Capacity remains compatible with existing ecological and infrastructural capacities. Over the projected periods, territorial ECC increases from 26.6% to 35.1% (Table 4), and from 39.1% to 54.5% (Table 5). This latter value indicates a moderate impact on the territorial system.

The area-level detail would show differentiated trends, with some areas experiencing a sharper increase in Tourism Carrying Capacity. On the one hand, areas already found to be more sensitive under Scenario 1 would naturally see a worsening of their carrying capacity conditions by 2034. On the other hand, the territorial absorption capacity would vary, with a rapid increase in carrying capacity in Areas 5, 7 and 9, leading to moderate impact levels, while Area 6 reaches an overflowing condition when considering the PCC of the overall accommodation system (Table 4).

The 2034 projection, based on hotel PCC, results in two areas reaching higher levels of congestion and overuse of territorial resources (Areas 5 and 9) (Figure 7; Table 5). The UNESCO coefficient leads to an estimate of tourist arrivals around 59.1 million (Table A2). These greater flows would increase the number of municipalities experiencing very high impact conditions or even surpassing 100% of ECC, beyond those previously identified, signaling stronger tensions between tourism growth and the absorption capacity of local resources (Figure 7).

Finally, Figure 8 highlights ECC levels estimated under the extreme Overflowing/Stress Test scenario, assuming the hotel PCC as the reference baseline for calculations. This scenario serves to test the resilience of the territorial system in the face of exceptional tourist pressures. Here, the variation coefficient brings tourist arrivals up to approximately 205 million in 2034 (Table A1). In that year, territorial carrying capacity would reach 51.3% when using the PCC of all accommodation facilities—still within a moderate impact range (Table 4); but would climb to 84.8% when based on hotel PCC, indicating a significant risk of congestion and overuse of local resources (Table 5). At the same time, the uneven distribution of ecological/infrastructural endowments would increase the number of areas—and particularly municipalities—exceeding critical thresholds of Tourism Carrying Capacity.

5. Discussion

The results show that in 2019 the overall Tourism Carrying Capacity of the serial site Via Appia. Regina Viarum, recalibrated to encompass a total area of 30,634 km² and over 11 million inhabitants, was at low impact levels, indicating a tourism load acting on the territory that was substantially contained. In reality, the 2019 data provide the concrete capacity of the observed territory to sustain the tourist flows actually recorded in that year in relation to the ecological and infrastructural endowments expressed by the territorial context indicators used for constructing the multi-dimensional composite indices, the EEI and the IEI. Based on these parameters, we estimate for 2019 an ECC equal to 26.5% and 38.9%, when considering as the basis for calculation the PCC of total accommodation facilities (Table 4) or hotels (Table 5), respectively. However, this load acts on the territory in a differentiated manner both among the 13 areas and among individual municipalities. Starting from this initial snapshot, we then simulate three evolutionary scenarios to explore how municipalities and areas along the serial site Via Appia. Regina Viarum could react to an increase in tourist arrivals, according to three evolutionary hypotheses: (i) Ordinary Dynamics (Scenario 1); (ii) UNESCO Effect (Scenario 2); and (iii) Overflowing/Stress Test (Scenario 3). Each scenario represents a hypothetical trajectory of evolution for municipalities and areas with respect to incremental tourism pressure.

The model results highlight how the territorial variability of ECC is strongly influenced by the complex and fragmented urban geography of the catchment area of the serial site. This is certainly valid regardless of the calculation basis assumed in the model for estimating quantitative ECC, although over time the calculation based on hotel PCC returns a more marked trend of tourism pressure for areas 1, 5, 6, and 9 (Figure 8).

This geography includes large cities such as Rome and Naples with part of their respective metropolitan areas, medium-sized cities, as well as numerous small urban centers—especially non-coastal ones—characterized by conditions of geographical and functional marginality or peripherality [134]. Similarly, the territorial variability of ECC is influenced by the presence of well-established polarities in the tourism space. These polarities tend to coincide with the main cities, where greater infrastructural endowments are concentrated but also higher residential density, the latter factor negatively affecting load dynamics. The infrastructural concentration is represented by the spatialized IEI (Figure 4, data by municipality) which shows higher values along the coasts and in correspondence with the main cities. Conversely, the spatialized EEI (Figure 3, data by municipality) highlights higher values in municipalities internal to the catchment area and peripheral to the main urban poles. The IEI and EEI delineate two distinct conditions: (i) in the first case, municipalities showing greater infrastructural endowment—understood as higher accommodation capacity, the presence of high-performance railway nodes, and a more articulated local transport offer—would give them greater capacity to absorb incoming tourist flows; (ii) in the second case, municipalities should present greater absorption capacity linked mainly to their ecological endowments, thanks mostly to lower anthropogenic pressure. However, territorial evidence based on the developed methodology reveals how some municipalities and areas experience conditions of stress even when in the presence of higher infrastructural endowments, suggesting a possible approach to critical thresholds of tourism load that would be accentuated in scenario years with the growth of hypothesized tourist flows.

A significant example is represented by the temporal evolution of ECC values for both calculation bases, detected in Areas 5 and 6 and in their constituent municipalities, characterized by a high degree of urbanization [135].

In Area 5, the tendency over time toward growing tourism pressure and threshold exceedance emerges only when the ECC is calculated on the basis of hotel facility PCC; in Area 6, instead, this tendency is evident with both calculation bases. This empirical finding confirms the assumption that greater infrastructural endowment does not automatically guarantee greater tourism absorption capacity. On the contrary, approaching limits is the result of ecological criticalities captured by the EEI. In particular, the approach to limits of the most infrastructurally equipped municipalities often appears connected to the cumulative effect between tourist flows and residential density, confirming what is highlighted in the literature about the need to integrate TCC assessments with other metrics capable of capturing criticalities linked to the relationship between tourism and urban governance [24,104]. The intersection between tourist flows and densely populated urban structures, if not adequately managed, can in fact produce diseconomies in the management and provision of common services, such as waste collection and disposal or water management, with effects of loss of ecosystem functionality [30]. Furthermore, it can exert pressures on territorial organization processes, favoring increasing levels of soil consumption, with changes to the urban landscape that are sometimes irreversible [136,137,138,139]. Similarly, the model results return dynamics of stress in tourism load even in municipalities with greater ecological endowment. These are mostly municipalities marginal from a demographic point of view, geographically distant from the main urban centers. In this case, the signs of stress shown by areas and municipalities are related to the lower availability of tourism services and tourism-related infrastructure. An example is offered by the temporal trend of tourism load in Areas 9 and 7, calculated on the basis of hotel PCC. Although in Area 7 no signs of stress are detected over time with respect to tourism load, the situation changes if observed at the municipal scale. In this area, in fact, the territorial evidence reported in Figure 6, Figure 7 and Figure 8 shows for the Municipality of Benevento an increasing trend in ECC values. These criticalities are attributable to a territorial tourism offer initially penalized by a scarce presence of accommodation services which, in contexts already marked by lower infrastructural capillarity, despite the presence of cultural heritage sites, limit the territory’s absorption capacity and contribute to increasing tourism pressure.

In Benevento, as in other municipalities covered by the model, more distant from the coasts, tourist flows are low if not even null. Cases of absence of flow registration, in addition to detecting non-tourist municipalities, can also be associated with an ISTAT policy that does not always provide data on tourist arrivals at the municipal level. The results show that even in municipalities characterized by a contained number of tourist arrivals over time, pressure on the territory can still manifest as a consequence of infrastructural insufficiency and other minimum conditions of local supply.

6. Methodological Considerations and Limitations

The developed methodology is not exempt from operational limitations. These difficulties substantiate weaknesses to which we believe correspond margins for improvement nonetheless. Our intent was to provide a quantitative estimate supported by territorial evidence of the TCC of the areas and municipalities that compose the UNESCO serial site. Due to the complexity of the UNESCO serial site, the settlement complexity of the study territory and the multi-dimensional and articulated nature of the tourism phenomenon, it was not possible to use integrally a model already consolidated by the literature. For this reason, among the numerous models advanced by the literature, we identified in the method proposed by Cifuentes [45] the one most capable of capturing the impacts of tourism on the different components of territory and sustainability. The method, currently applied mostly to estimate the TCC of protected areas and parks, therefore in clearly defined areas, proved more sensitive to operational reinterpretation allowing its application extension also to conduct TCC technique for serial sites not clearly delimited and present in extended and complex territories. We believe this aspect represents a substantial element of value of our study, since, although modeled on the serial site Via Appia. Regina Viarum, it provides useful elements for applying TCC technique addressed to serial sites, still little explored by the literature. Furthermore, an additional element of value consists in having placed at the center of the investigation the Tourism Carrying Capacity of designated territories, extended beyond the strictly understood territorial domain of the serial site to respond to more coherent logics of territorial organization and functional interaction. These geographic parameters are suitable for representing the articulated anthropization of places directly and indirectly connected to the serial site. Our approach is not based on direct measures of tourism impact, but on the use of territorial context composite indices capable of highlighting the components that currently favor or hinder tourism overload phenomena and that, in the absence of targeted policies, would tend to accentuate them especially where the formal recognition of the serial site would lead to growing tourism exposure of catchment municipalities. In our model, the tendency to overload is not, therefore, implied by demand and tourist behavior, an aspect addressed instead by more explanatory models of this dimension such as LAC, but rather by the structural, infrastructural, and organizational characteristics of the territories involved, which determine a greater or lesser capacity to absorb growing tourist flows. These observations align with the thinking of Mathieson and Wall [53], according to whom the tolerance levels of a destination are also influenced by local factors understood as territory and community. In this sense, the propensity to overload is read as the outcome of pre-existing and persistent territorial conditions, which act independently of current demand, but which can significantly amplify the effects of future tourism pressure induced by UNESCO recognition. The adoption of this perspective allows orienting the analysis toward the territory’s capacity to sustain tourism in a preventive and proactive key. This approach reinforces the utility of an exploration conducted on an areal scale, toward which most of the presented analysis was oriented. The 13 areas are, in fact, explanatory of the complex mosaic of differentiated Tourism Carrying Capacities expressed by constituent municipalities, thus serving as a guide for future punctual analyses, supported by more specific indicators capable of taking charge of the specificities of the most vulnerable municipalities. This reflects the widely shared position in the literature regarding the adoption of differentiated approaches, recognizing that there is no single standardized formula for determining carrying capacity and that its determination needs to be adapted to the context requiring, in addition, the involvement of local stakeholders [89]. As emerged in other studies, tourism impacts are not uniform among places just as their causes are not. The intensity of tourism loads can vary among different dimensions depending on the type of locality or destination, supporting the need for targeted and context-oriented management [91,117]. In line with other methodologies, united by the objective of providing a numerical datum of TCC [26], a weak point of our model lies in the complex articulation of the procedure necessary to arrive at the result to be estimated. Despite the complexity, this process is not capable of offering a sufficient framework of carrying capacity in the territories of the serial site with the quantitative data used alone. Consistently with what was found by various studies in analogous attempts to determine TCC, the process of indicator selection and data collection had to deal with numerous criticalities, linked in particular to availability, quality, and accessibility of necessary information. The difficulty of connecting information returned according to heterogeneous methodological approaches by different managers made complex the integration of primary source data in the model, leading to the exclusion of specific indicators, such as that relating to water treatment. In other cases, our requests did not obtain response, actually reducing the possibility of obtaining statistically more informed results from the model. This confirms the need to integrate the model, in particular, and in general TCC techniques, with the participation of public and private stakeholders operating at local and supra-local levels in order to obtain more significant and explanatory results of local sensitivities and vulnerabilities. Our model presents a strong orientation toward indicators, which unites it with the obstacles and therefore with the limits discussed by the literature regarding the operational complexity of TCC techniques exclusively based on statistical data. Working on such an extended territory entailed the need to process a huge quantity of secondary source data, with consequent challenges linked to the management and integration of statistical information, not always available, not always updated and with differentiated territorial coverage. The absence of availability of data on tourist arrivals for all municipalities, for example, entailed an underestimation of real flows, just as we could not detect with local scale detail excursionists and data on seasonalized tourist arrivals. On the updating level, for example, not being able to count on the availability of primary data, the data on metros offered by ISTAT are the most recent at the time of analysis elaboration. On the territorial coverage level, the municipal level is often not detected by ISTAT or the data are returned obscured, as often observed in smaller municipalities for hotel and extra-hotel accommodation. We believe, however, that such limits can be overcome through the expansion and enrichment of the information base. The integration of new indicators and alternative statistical sources, such as big data, combined with qualitative investigations that provide, for example, direct involvement of local stakeholders and collection of perceptions and feedback both from territory users and from the resident population, could contribute to a more complete and in-depth assessment of carrying capacity in the territories of Via Appia. Regina Viarum. Such an approach would allow increasing the depth and multi-dimensionality of territorial endowment indices, while improving the precision of estimates. Furthermore, it would allow applying the model with greater spatial detail, both at levels below the municipal scale, and considering differentiated territorial aggregations.

7. Conclusions

TCC is a concept that was developed in the second half of in the second half of the twentieth century [89]. Its evolution reflects discussions on sustainable development, in harmony with an approach to development that fundamentally seeks to achieve social, economic, and ecological objectives in equal measure. According to various approaches, TCC aims to answer the question “How many is too many?; for other authors, it should inquire “on the social and biophysical conditions desired or appropriate at a destination” [78]. Despite extensive literature on the concept and the development of differentiated methodologies to arrive at its definition across various dimensions, TCC currently remains an elusive notion that is complex to apply operationally. This study has presented a model for the numerical estimation of TCC applied to serial cultural sites, attempting to fill a gap in carrying capacity methodologies. By extensively interpreting the approach proposed by Cifuentes to the complex reality of the UNESCO serial site Via Appia. Regina Viarum, the study empirically demonstrates how TCC assessment can be scaled from the evaluation of specifically delimited areas to extended territorial systems, characterized by functional heterogeneity and spatial discontinuity. The numerical results and territorial evidence allow an initial exploration of the TCC of the territories within the scope of the serial site, providing an orientative and multi-dimensional framework of tourist absorption capacity at the territorial level. The scenarios of ordinary dynamics, UNESCO effect, and stress test provide a mapping of areas and municipalities where specific territorial conditions captured by the model prove favorable or critical with respect to localized overload phenomena. The load dynamics returned by the model are context-sensitive. While in some highly urbanized areas, despite infrastructure, signs of overload are observed due to the cumulative effect of tourist flows and population density, in peripheral zones the lack of structured local supply amplifies local pressure. These dynamics, in the absence of interventions, would be accentuated by incremental tourist flows attracted by the UNESCO brand. In this sense, the integration between areal and municipal scale proposed supports an articulated geographical reading of local vulnerabilities that, prospectively, require further information. The study’s focus on preventive management aligns with emerging paradigms on tourism sustainability that favor proactive rather than reactive interventions. Through the case study Via Appia. Regina Viarum and the discussed limitations, we demonstrate how Tourism Carrying Capacity management, particularly in the context of application to serial sites, requires analytical tools capable of capturing the territorial complexity of location territories. The main contribution offered by the research is the transferability of the methodological logic that shifts the focus of TCC determination from the individual site to the territory. Despite the model providing insights in this direction, several limitations must be considered. The methodology is biased toward strictly quantitative indicators; likewise, it is dependent on static secondary data that, while ensuring adequate territorial coverage, risk underestimating local dynamics and offering a partial framework. Future research should integrate primary data collection, including consultations with stakeholders and local communities, and visitor experience assessments. Consideration of such information would extend the definition of additional load components [52,104,106], improving the model’s explanatory function. Generally, we acknowledge that the TCC technique applied to serial sites represents a methodological challenge that remains open and needs further investigation. The implications of this research extend beyond the Italian case to inform global discussions on TCC and its connection with tourism planning. Through this research, we also want to support the political/academic debate on the importance of TCC by advocating its function as an essential operational mechanism for translating the principle of sustainability into feasible and manageable metrics in the tourism field. While TCC contributes to regulating tourist pressure on sites and destinations, it is founded on a holistic interpretation of impact management, supported by the activation of feedback circuits for continuous monitoring, functional to adaptive management of preventive and tourism development strategies. The developed methodology derives useful data for decision-making in tourism planning, supporting the definition of more informed and territorially contextualized strategies. Strategic planning, supported by a systemic and long-term vision, articulated through a coherent sequence of objectives and actions, is configured as a key activity to guide the orderly transformation of territories and their tourism development, balancing opportunities with the mitigation of negative impacts.