The Integrated Analysis of Territorial Transformations in Inland Areas of Italy: The Link between Natural, Social, and Economic Capitals Using the Ecosystem Service Approach

Abstract

This paper examines how spatial dynamics have impacted natural capital and the provision of ecosystem services. The units outlined by the National Strategy for Inland Areas (SNAI) have been used as the territorial units for this study. The SNAI is a public policy focused on enhancing the quality of services to citizens (such as transportation, healthcare, and education). It proposes the economic revitalization of inland areas undergoing processes of marginalization. Our focus on inland areas stems from two primary reasons: first, no previous studies in Italy have analyzed the changes in ecosystem services in SNAI areas; and second, SNAI areas are well-suited to providing ecosystem services that are in demand by urban areas. Although this study does not cover all aspects inherent to the topic, it represents a starting point aimed at understanding the links between environmental and socio-economic dynamics and ecosystem service changes. This is essential for both current and future generations. By analyzing the processes of permanence and transformation, modifications in the supply–demand balance have hereby been studied, as well as the economic variations in ecosystem services. The period considered runs from 1990 to 2018. These findings could help governmental institutions in developing sustainable governance models, in line with spatial policies and strategies.

1. Introduction

1.1. Background

The National Strategy for Inland Areas (SNAI) in Italy is a territorial policy supported by European funds (ERDF, ESF, and EAFRD) and national programs. It aims to enhance the quality of services provided to citizens (such as transportation, health, and education) and fostering economic development in inland areas at risk of marginalization. In the document titled the “National Strategy for Inland Areas”, produced by the Ministry for Territorial Cohesion and the South [1], inland areas are defined in terms of their distance from major urban centers and their communication infrastructures. The SNAI was initially introduced in the National Reform Program (NRP) in 2014 and further defined in the 2014–2020 Partnership Agreement. The SNAI classifies Italian municipalities into four clusters: belt areas, intermediate areas, peripheral areas, and outermost areas. Based on these classifications, the strategy identifies inland areas as places that, although distant from services, are rich in environmental and cultural resources as a result of long processes of human influence on the environment. These territories are characterized by a high biodiversity and heterogeneous ecosystems and are able to provide significant ecosystem services (ESs) to the community, such as drinking water supply, food supply, and carbon sequestration.

1.2. Territorial Socio-Economic Transformations and Ecosystem Service Balance

Rural abandonment and rapid urbanization, associated with agricultural intensification and climate change, have been key drivers in transforming terrestrial landscapes. This has had a significant impact on the quality of the environment and the ESs provided for human well-being [2]. It is clear that socio-economic factors have influenced and will continue to influence ES provision [3]. Pressure from human activities has altered the landscape, leading to a decreased supply and increased demand for ESs in urban areas [4]. The demand for ESs is increasing due to various factors such as population growth and urban expansion [5]. This increased demand can lead to ecosystem degradation by impacting the supply of goods and services, harming current and future generations [6]. It is important to analyze the demand for ESs at different scales [7]. For example, the demand for services like food supply and air purification is higher in urban areas than in rural areas due to human and industrial activities [4,8,9]. Conversely, regulatory services such as protection from erosion and hydrogeological disruption are analyzed at a local scale [10]. In recent decades, the gap between supply and social demand for ESs has increased [11]. Anthropogenic factors have caused an increase in demand, resulting in changes in land use and land cover, impacting natural capital and ES provision [12]. In recent decades, inland areas of Italy have experienced a significant migration of people to urban areas. This movement is attributed to several factors, including agricultural mechanization, which has reduced the need for agricultural labor, economic crises in local communities, the declining quality of public services, and environmental hazards [13]. The depopulation of rural and mountainous areas has led to changes in land use, altering the landscape. The abandonment of cultivation practices is one of the main causes of land use changes in Europe, particularly affecting mountainous areas [14]. The increase in forested areas has occurred at the expense of land intended for agriculture and grazing, resulting in a change in the landscape structure and changes in the ES supply [15]. These changes have led to a reduction in the supply of provisioning ESs and an increase in regulation ESs. Furthermore, land use and land cover changes have influenced both social and economic dynamics [16]. According to McPhearson [17], the complex interplay of socio-ecological systems (SESs) makes it challenging to identify long-term drivers. Changes in land use lead to variations in ESs on spatial and temporal scales [18,19,20,21,22,23,24] and also affect relationships among ESs [25]. The impacts of changes in land use and cover have highlighted the importance of identifying tools to monitor natural capital and ESs. Mendoza and colleagues [26] highlight the importance of quantifying these changes to understand their impacts on natural capital and human activities. One essential analysis tool used is the mapping of ESs, which helps identify the areas which need intervention for ecosystem maintenance and restoration. Mapping is crucial for assessing and visualizing ESs [27]. It allows us to identify where ES are available and where ES demand and supply are high [28]. The “European Commission’s Mapping and Assessment of Ecosystems and their Services” initiative aims to improve the understanding of ecosystems and their services to support the implementation of Objective 2 of the Biodiversity Strategy by 2020. Various methods are currently used to map ESs, including models, proxy indicators, remote sensing data, and expert-based matrices. To effectively manage ESs, it is crucial to assess them from both a biophysical and economic perspective [29,30]. Biophysical evaluation helps one understand the qualitative and quantitative variations in the natural capital, while economic assessment allows one to assign an economic value to the benefits provided by ESs. The benefit transfer method is often used to estimate the benefits of ESs by transferring information (especially values) from studies conducted in another location and/or context [31]. In recent times, databases such as the Ecosystem Service Valuation Database (ESDV) [32] have been developed and made available online. These databases are useful for organizing economic information on ESs at different levels of detail, such as biomes.

1.3. The Research Objective

This paper aims to understand how demographic and economic dynamics have influenced changes in land use and land cover, thus affecting ES provision in SNAI areas. To achieve this, we analyzed the changes in soil and land cover in SNAI areas from 1990 to 2018, building upon a previous study by Marino et al. (2023) [31]. Using the ecosystem service matrix developed by Burkhard (2014) [28], we qualitatively evaluated the supply, demand, and balance of ESs. Inland areas are the focus of this study, as they are environmentally and socio-economically fragile and vulnerable territories. These areas require policies and interventions to support investment in innovation, protect the environment, and promote the unique features of the territory. This paper provides an initial assessment of the qualitative changes in the potential balance of ESs in these areas.

2. Materials and Methods

The research follows a methodological framework composed of 4 stages (Figure 1). Starting from a previous work [31], we represented the processes of transformations and permanence at the level of SNAI areas (step 1) and subsequently calculated the variation in the qualitative balance between the demand and supply of ESs due to these processes (step 2). To understand the impacts of these processes, economic changes in ES (step 3) and some social and economic transformations (step 4) were analyzed.

2.1. Study Area

The National Strategy for Inland Areas (SNAI) identifies 59 per cent (18 million ha) of Italy’s territory as inland areas and defines the rest of the territory as central areas. Underlying this territorial subdivision is a criterion of distance from essential services (hospitals, railway stations, educational systems, etc.) measured in minutes of travel time by car.

Each Italian municipality (Figure 2) has been labeled by the SNAI as a hub (A), intermediate hub (B), or belt (C) municipality in the case of central areas or as intermediate (D), peripheral (E), or ultraperipheral (F) in the case of inland areas. In terms of the extent of these classes, intermediate municipalities (belonging to inland areas) are the most extensive (87 thousand km²), followed by belt municipalities (84 thousand km²).

Regarding land use,

Table 1. Land composition (absolute values in km² and percentage) of Italian SNAI areas for the year 2018 (source: Corine Land Cover). For each SNAI area, the percentage identifies the weight of each surface type. The intensity of the light-blue color increases as the percentage increases.

SNAI Description	SNAI Code	Urban Areas		Agricultural Areas		Wooded and Semi-Natural Areas		Wetlands and Water Bodies		Total
		km2	%	km2	%	km2	%	km2	%	km2
Hubs	A	4424	15%	18,706	65%	5135	18%	592	2%	28,857
Intermediate hubs	B	661	8%	5782	66%	2062	24%	242	3%	8746
Belt	C	7060	8%	53,602	63%	22,905	27%	1037	1%	84,605
Intermediate	D	2801	3%	44,168	50%	39,448	45%	1288	1%	87,705
Peripheral	E	1219	2%	29,230	40%	41,340	57%	608	1%	72,397
Ultraperipheral	F	279	1%	5825	30%	13,079	68%	112	1%	19,296
Total		16,444	5%	157,313	52%	123,969	41%	3879	1%	301,605

shows the relative percentage composition of each SNAI area in 2018 [CLC 2018 data]. Hub areas (A) are the areas with the highest relative concentration of urban areas (15%), while intermediate hubs (B) are those with the highest concentration of agricultural areas (66%), and ultraperipheral areas (F) are those whose area is predominantly occupied by forests (68%). Note that, in general, the core areas (A, B, C) are those whose relative composition is predominantly agricultural; in contrast, the interior areas E and F have a relative composition of predominantly forested areas. Again, in relative terms, the highest percentages of urbanization are found in the central areas (A, B, and C).

2.2. Analysis of the LULC Changes in SNAI Areas from 1990 to 2018 through Permanence and Transition Categories (Step 1)

The land use and cover change that occurred in Italian SNAI areas between 1990 and 2018 were analyzed in a GIS environment using the two respective shapefiles derived from the Corine Land Cover (CLC) project [33]. The shapefiles were then associated with each administrative unit, which, in turn, were associated, by ISTAT [34], with one of six SNAI categories. Next, the intersection function between the two land use shapefiles of 1990 and 2018 was used to obtain a third shapefile containing the polygons of the LULC changes that occurred during the period considered. Then, through the joint function, the types of LULC changes that occurred were named through the categories of transition and permanence already used in a previous work [31] (see Figure A1 in Appendix A). Finally, the shapefile attribute table was exported to an Excel file, and the surfaces for each transition and permanence type and SNAI area were summed. This elaboration provided information on the amount of area (absolute and relative) affected by each type of transition and permanence within each SNAI category.

2.3. Evaluation of ES Balance Variation by LULC Changes in Italian SNAI Areas (Step 2)

One of the most widely used approaches in the field of ES mapping is the one devised by Burkhard et al. [28,35,36], commonly known as the ‘matrix approach’ [37]. Specifically, this approach involves the association of qualitative values (where 0 identifies the minimum and 5 the maximum) of supply, demand, and balance (i.e., supply minus demand) of ESs to each CLC class at the third level of detail.

In our work, we obtained ES balance values for each CLC class using the ES potential supply and demand matrix contained in the work of Burkhard et al., from 2014 [28]. Specifically, for each CLC class, we subtracted the demand values from the potential supply values of ESs. The ES balance values obtained identified, for each CLC class, the net capacity to provide (when balance values > 0) or demand (when balance values < 0) ESs. The ES balance values obtained thus far were associated with third-level Corine land cover classes. We grouped the third-level classes and their associated ES balance values into macro classes (mostly aggregated according to the second Corine level) to allow for comparison (see also Section 2.3 below) with the results of our previous work, which was based on their aggregation into macro classes [31]. In particular, a weighted average for the area of the classes (III CLC level) contained in the macro class (II CLC level) was used to relate the ES balance values from the classes to the macro classes (

Table 2. ES balance values associated with the LULC macro classes used in this work. The values are elaborated from Burkhard et al. (2014) [28]. The negative values (gradation of red) indicate that the demand for the ES of a specific macro class is higher than the relative supply. The positive values (gradations of green), on the contrary, indicate that the supply is greater than the demand.

Macroclass Description	Macroclass Code	Global Climate Regulation	Air Quality Regulation	Water Flow Regulation	Water Purification	Erosion Regulation	Natural Hazard Regulation	Crops	Fodder	Timber	Wild Food and Resources
Urban areas	100	−3.3	−4.7	−4.4	−4.1	0.1	−4.2	−3.0	−1.6	−3.2	−3.8
Arable lands	210	−1.1	0.0	−0.1	−0.2	−3.0	−1.0	5.0	4.9	0.0	1.0
Permanent crops	220	0.2	0.2	0.2	−1.7	−0.8	−2.6	4.0	0.0	1.1	0.1
Pastures	230	−1.0	−1.0	0.0	−2.0	0.0	−1.0	0.0	0.0	−1.0	2.0
Heterogeneous agricultural areas	240	0.5	0.5	0.5	−1.1	0.6	−0.6	3.5	2.1	0.5	1.5
Forests	310	5.0	5.0	3.0	5.0	5.0	4.0	0.0	1.0	5.0	5.0
Shrub and/or herbaceous vegetation	320	3.2	0.5	1.1	1.9	2.6	1.1	0.0	1.4	0.8	2.6
Non and sparsely vegetated areas	330	0.1	0.0	0.7	0.9	1.3	1.3	0.0	0.0	0.0	0.4
Wetland and water bodies	500	0.9	0.0	3.9	2.0	0.2	3.3	0.0	0.4	0.0	3.5

).

Using the qualitative coefficients calculated above, we then calculated the ES balance change that occurred from 1990 to 2018 in Italian SNAI areas. For this purpose, we calculated the ES unit balance variation values produced by each variation dynamic between the LULC macro classes (e.g., 210 to 100; see previous paragraph) and multiplied these by the surface area affected by the specific LULC variation dynamic. Finally, for each ES, we normalized the ES balance variation value (see equation below).

ΔBDG_ESy = Ʃ (BDG_ESyMx2018 − BDG_ESyMz1990) × (A_dinzx × 100/A_tot)

where

ΔBDG_ESy = change in ES_y balance between 1990 and 2018;

BDG_ESyMx2018 = value of the balance of ES_y by macro class M_x at 2018;

BDG_ESyMz1990 = value of the balance of ES_y by macro class M_z at 1990;

A_dinzx = area affected by land use change dynamics (i.e., an area that was a macro class z in 1990 and changed to a macro class x in 2018);

A_tot = total area of the study area.

The results obtained were aggregated in terms of both LULC transition categories and SNAI areas.

2.4. Economic Evaluation of the ES Supply in SNAI Areas (Step 3)

To achieve the economic evaluation of ES supply variations in SNAI areas, we used the benefit transfer approach [38,39,40] and, in particular, the unitary economic coefficients (€/ha) collected in our previous work. We then associated, in a GIS environment, the unitary economic coefficients to the LULC macro class and aggregated them for each SNAI zone in 1990 and 2018. We obtained the economic value of the ES supply for each SNAI area for the years 1990 and 2018 and the relative difference.

2.5. Analysis of Territorial Socio-Economic Transformations (Step 4)

We examined the changes in territories based on socio-economic indicators. Specifically, we calculated (i) the population variation in municipalities from 1990 to 2018 [41] as a social indicator and (ii) employees and companies number variation from 2012 to 2018 [41]. Both changes were represented at SNAI level. While we understand that more indicators are needed for a comprehensive analysis of territorial changes, we chose these indicators because representative basic data were available at the municipal level. For this reason, the economic data had a different reference period.

3. Results

3.1. Main LULC Changes in SNAI Areas

The analysis of the land use changes in SNAI areas from 1990 to 2018 (

Table 3. Changes in the LULC of SNAI areas between 1990 and 2018 summarized by transition categories [15]. Note: For each SNAI area (columns), we report the area of the transitions expressed as a percentage of the total area of the relevant SNAI area. Gradations of blue indicate increasing percentage values (light to dark).

LULC Permanence and Transition Categories	A	B	C	D	E	F
Agr. Extensification	0.3%	0.5%	0.3%	0.3%	0.2%	0.1%
Transformation to complex system	2.5%	2.2%	2.5%	2.6%	2.1%	1.5%
Agr. Intensification	4.3%	5.3%	4.5%	4.9%	4.9%	4.4%
Forest extension	0.9%	1.1%	0.7%	1.2%	1.6%	2.9%
Urbanization	2.8%	1.6%	2.0%	0.8%	0.4%	0.4%
Other	0.6%	0.4%	0.5%	0.3%	0.2%	0.3%
Permanence	88.7%	88.9%	89.5%	89.8%	90.6%	90.4%

) reveals trends that are common to all SNAI areas, as well as ones which are specific to each area type. In particular, by relating the extent of individual land use transitions to the total area of each SNAI zone, several key dynamics emerged (Table 3,

Table A1. Land use changes aggregated at the SNAI classes level (surface area and percentage coverage).

A	28,857		B	8746		C	84,605
	km2	%		km2	%		km2	%
Perm (210210)	9838	34	Perm (210210)	3114	36	Perm (210210)	29,016	34
Perm (240240)	4275	15	Perm (310310)	1599	18	Perm (310310)	15,622	18
Perm (100100)	3615	13	Perm (240240)	1183	14	Perm (240240)	11,662	14
Perm (310310)	3164	11	Perm (220220)	718	8	Perm (220220)	5954	7
Perm (220220)	2250	8	Perm (100100)	518	6	Perm (100100)	5340	6
Perm (320320)	1212	4	Perm (500500)	227	3	Perm (320320)	4064	5
Perm (500500)	552	2	Perm (320320)	226	3	Etcs (210240)	1200	1
Int (240210)	532	2	Urb (240210)	223	3	Int (240210)	1165	1
Etcs (210240)	438	2	Etcs (210240)	89	1	Perm (330330)	1099	1
Urb (210100)	408	1	Int (240220)	86	1	Perm (500500)	956	1
Urb (240100)	272	1	Etcs (220240)	76	1	Urb (210100)	886	1
Etcs (220240)	232	1	Urb (210100)	74	1	Int (240220)	810	1
Int (240220)	211	1	Int (210220)	68	1	Etcs (220240)	725	1
Perm (320310)	153	1	Perm (330330)	54	1	Perm (230230)	709	1
			Urb (240100)	46	1	Urb (240100)	580	1
						Perm (320310)	506	1
						Int (210220)	492	1
D	87,705		E	72,397		F	19,296
	km2	%		km2	%		km2	%
Perm (310310)	25,010	29	Perm (310310)	23,292	32	Perm (310310)	5325	28
Perm (210210)	19,592	22	Perm (210210)	12,468	17	Perm (320320)	4761	25
Perm (240240)	11,121	13	Perm (320320)	10,317	14	Perm (210210)	2230	12
Perm (320320)	7854	9	Perm (240240)	7448	10	Perm (240240)	1797	9
Perm (220220)	5555	6	Perm (330330)	3361	5	Perm (330330)	1249	6
Perm (330330)	2605	3	Perm (220220)	2990	4	Perm (320310)	508	3
Perm (100100)	2099	2	Perm (320310)	1340	2	Perm (220220)	468	2
Perm (500500)	1239	1	Perm (230230)	1031	1	Perm (310320)	297	2
Int (240210)	1203	1	Int (240210)	917	1	For Ext (240320)	284	1
Perm (320310)	1145	1	Perm (100100)	895	1	Perm (330320)	231	1
Etcs (210240)	993	1	Etcs (320240)	818	1	Etcs (320240)	216	1
Etcs (220240)	988	1	Perm (330320)	758	1	Int (240210)	204	1
Perm (230230)	892	1	Perm (310320)	755	1	Perm (100100)	198	1
Perm (330320)	803	1	Etcs (210240)	583	1	Int (320210)	163	1
Int (240220)	669	1	Perm (500500)	564	1	Etcs (210240)	128	1
Perm (310320)	651	1	Etcs (220240)	557	1	Perm (230230)	123	1
Etcs (320240)	628	1	Int (320210)	488	1	Etcs (220240)	116	1
Int (210220)	554	1	For Ext (240320)	443	1	Perm (500500)	98	1

). In all SNAI areas, permanence, representing approximately 90 percent by weight, is predominant over transitions. In central areas, permanence is primarily related to the continuation of agricultural land, particularly arable land. In inland areas, the primary permanence relates to the continuation of forested land. Conversely, agricultural intensification processes represent the most significant transition in all SNAI areas, with a range of 4.3% in hub areas to 5.3% in inter-municipal hubs. The process of renaturation is particularly prominent in the outermost areas, and, in general, it is greater in inland areas than in central areas. Urbanization has opposite dynamics: it has greater weight in central areas (with a maximum of 2.8 percent in hub areas) than in interior areas (with a minimum of 0.4 percent in peripheral and outermost areas).

3.2. Qualitative ES Balance Potential Variation

First, ES balance variation was analyzed for each type of land use change dynamic, regardless of the surface area covered by each dynamic (

Table A2. LULC change dynamics and consequences on the potential balance sheet of each ES.

Transition and Permanence	Change	Global Climate Regulation	Air Quality Regulation	Water Flow Regulation	Water Purifcation	Erosion Regulation	Natural Hazard Regulation	Crops	Fodder	Timber	Wild Food and Resources	Sum (0–5)
Urb	210100	−2	−5	−4	−4	3	−3	−8	−6	−3	−5	−3
Int	210220	1	0	0	−2	2	−2	−1	−5	1	−1	0
Perm	210230	0	−1	0	−2	3	0	−5	−5	−1	1	−1
Etcs	210240	2	0	1	−1	4	0	−2	−3	1	1	0
For Ext	210310	6	5	3	5	8	5	−5	−4	5	4	2
For Ext	210320	4	1	1	2	6	2	−5	−3	1	2	1
For Ext	210330	1	0	1	1	4	2	−5	−5	0	−1	0
Urb	220100	−4	−5	−5	−2	1	−2	−7	−2	−4	−4	−2
Agr Ext	220210	−1	0	0	2	−2	2	1	5	−1	1	0
Agr Ext	220230	−1	−1	0	0	1	2	−4	0	−2	2	0
Etcs	220240	0	0	0	1	1	2	0	2	−1	1	1
For Ext	220310	5	5	3	7	6	7	−4	1	4	5	3
For Ext	220320	3	0	1	4	3	4	−4	1	0	3	1
For Ext	220330	0	0	1	3	2	4	−4	0	−1	0	0
Urb	230100	−2	−4	−4	−2	0	−3	−3	−2	−2	−6	−2
Int	230210	0	1	0	2	−3	0	5	5	1	−1	1
Int	230220	1	1	0	0	−1	−2	4	0	2	−2	0
Etcs	230240	1	1	0	1	1	0	3	2	2	−1	1
For Ext	230310	6	6	3	7	5	5	0	1	6	3	3
For Ext	230320	4	2	1	4	3	2	0	1	2	1	1
For Ext	230330	1	1	1	3	1	2	0	0	1	−2	1
Urb	240100	−4	−5	−5	−3	0	−4	−6	−4	−4	−5	−3
Int	240210	−2	0	−1	1	−4	0	2	3	−1	−1	0
Int	240220	0	0	0	−1	−1	−2	0	−2	1	−1	−1
Int	240230	−1	−1	0	−1	−1	0	−3	−2	−2	1	−1
For Ext	240310	5	5	3	6	4	5	−3	−1	4	4	2
For Ext	240320	3	0	1	3	2	2	−3	−1	0	1	1
For Ext	240330	0	0	0	2	1	2	−3	−2	−1	−1	0
Urb	310100	−8	−10	−7	−9	−5	−8	−3	−3	−8	−9	−5
Int	310210	−6	−5	−3	−5	−8	−5	5	4	−5	−4	−2
Int	310220	−5	−5	−3	−7	−6	−7	4	−1	−4	−5	−3
Int	310230	−6	−6	−3	−7	−5	−5	0	−1	−6	−3	−3
Etcs	310240	−5	−5	−3	−6	−4	−5	3	1	−4	−4	−2
Perm	310320	−2	−4	−2	−3	−2	−3	0	0	−4	−2	−2
Perm	310330	−5	−5	−2	−4	−4	−3	0	−1	−5	−5	−2
Urb	320100	−7	−5	−5	−6	−3	−5	−3	−3	−4	−6	−3
Int	320210	−4	−1	−1	−2	−6	−2	5	3	−1	−2	−1
Int	320220	−3	0	−1	−4	−3	−4	4	−1	0	−3	−1
Int	320230	−4	−2	−1	−4	−3	−2	0	−1	−2	−1	−1
Etcs	320240	−3	0	−1	−3	−2	−2	3	1	0	−1	−1
Perm	320310	2	4	2	3	2	3	0	0	4	2	2
Perm	320330	−3	−1	0	−1	−1	0	0	−1	−1	−2	−1
Urb	330100	−3	−5	−5	−5	−1	−5	−3	−2	−3	−4	−3
Int	330210	−1	0	−1	−1	−4	−2	5	5	0	1	0
Int	330220	0	0	−1	−3	−2	−4	4	0	1	0	0
Int	330230	−1	−1	−1	−3	−1	−2	0	0	−1	2	−1
Etcs	330240	0	0	0	−2	−1	−2	3	2	1	1	0
Perm	330310	5	5	2	4	4	3	0	1	5	5	2
Perm	330320	3	1	0	1	1	0	0	1	1	2	1
Perm Macro	−	0	0	0	0	0	0	0	0	0	0	0
Other	−	1	1	1	1	0	1	1	1	1	1	0
	tot	−3	−4	−5	−4	0	−4	−4	−3	−2	−5	−2

). As can be observed in Table A2, urbanization is the process of land use change that can produce the most significant decreases in the balance of ESs: this is due to a substantial increase in demand and, conversely, a notable decline in the supply of all ESs. In this regard, the soil sealing of forested lands represents the most detrimental dynamic. When forests undergo such a conversion, the air purification ES is most adversely affected (−10 points). In general, the loss of forests due to urbanization and agricultural intensification processes is among the most deleterious changes.

Then, to evaluate the ES balance variation considering the specific area affected by each dynamic of land use change, the qualitative values commented on in the previous paragraph were weighted (see Section 2). The radar chart below (Figure 3) presents the overall weighted ES balance qualitative value variation determined by each LULC dynamic from (i) arable land, (ii), permanent crops, (iii) pasture, (iv) heterogeneous agricultural areas, (v) forests, (vi) shrub and herbaceous vegetation, and (vii) open spaces with little or no vegetation.

Negative radar peaks (Figure 3) identify the LULC dynamics of change that produce the greatest detrimental impacts on ES balance variation. Specifically, the urbanization of arable land (210100) produces −21 points, while the homogenization of heterogeneous agricultural zones (240100) produces −18 points. The internal trends that define forest classes and, namely, the replacement of woodland with shrubland (310320) also produce a high overall decrease in ESs (−17 points).

These negative changes in the ES balance between 1990 and 2018 are weakly offset by the positive changes in forest extension. In particular, land use changes from shrubland (320) and heterogeneous agricultural areas (240) to forests (310) generated, respectively, +28 and +8 points.

By delving deeper into the details of the effects of LULC changes on balance variation for each ES (

Table A3. Changes in the area-weighted balance of each LULC change dynamic.

Transition and Permanence	Change	km2	Global Climate Regulation	Air Quality Regulation	Water Flow Regulation	Water Purification	Erosion Regulation	Natural Hazard Regulation	Crops	Fodder	Timber	Wild Food and Resources	Sum
Urb	210100	1725	−1.3	−2.7	−2.5	−2.2	1.8	−1.8	−4.6	−3.7	−1.8	−2.7	−21.5
Int	210220	1496	0.6	0.1	0.2	−0.8	1.1	−0.8	−0.5	−2.4	0.5	−0.5	−2.4
Perm	210230	299	0.0	−0.1	0.0	−0.2	0.3	0.0	−0.5	−0.5	−0.1	0.1	−0.9
Etcs	210240	3430	1.8	0.5	0.7	−1.0	4.0	0.5	−1.7	−3.1	0.6	0.6	2.8
For Ext	210310	173	0.3	0.3	0.2	0.3	0.5	0.3	−0.3	−0.2	0.3	0.2	1.9
For Ext	210320	398	0.6	0.1	0.2	0.3	0.7	0.3	−0.7	−0.5	0.1	0.2	1.3
For Ext	210330	46	0.0	−	0.0	0.0	0.1	0.0	−0.1	−0.1	−	−0.0	−0.0
Urb	220100	294	−0.3	−0.5	−0.5	−0.2	0.1	−0.2	−0.7	−0.2	−0.4	−0.4	−3.2
Agr Ext	220210	695	−0.3	−0.1	−0.1	0.4	−0.5	0.4	0.2	1.1	−0.2	0.2	1.1
Agr Ext	220230	20	−0.0	−0.0	−0.0	−0.0	0.0	0.0	−0.0	−0.0	−0.0	0.0	−0.0
Etcs	220240	2694	0.2	0.2	0.2	0.6	1.2	1.7	−0.4	1.9	−0.5	1.3	6.5
For Ext	220310	126	0.2	0.2	0.1	0.3	0.2	0.3	−0.2	0.0	0.2	0.2	1.6
For Ext	220320	174	0.2	0.0	0.0	0.2	0.2	0.2	−0.2	0.1	−0.0	0.1	0.8
For Ext	220330	33	−0.0	−0.0	0.0	0.0	0.0	0.0	−0.0	−0.0	−0.0	0.0	0.0
Urb	230100	89	−0.1	−0.1	−0.1	−0.1	0.0	−0.1	−0.1	−0.0	−0.1	−0.2	−0.8
Int	230210	357	−0.0	0.1	−0.0	0.2	−0.4	−0.0	0.6	0.6	0.1	−0.1	1.1
Int	230220	47	0.0	0.0	0.0	0.0	−0.0	−0.0	0.1	0.0	0.0	−0.0	0.1
Etcs	230240	463	0.2	0.2	0.1	0.1	0.1	0.1	0.5	0.3	0.2	−0.1	1.8
For Ext	230310	67	0.1	0.1	0.1	0.2	0.1	0.1	−	0.0	0.1	0.1	0.9
For Ext	230320	587	0.8	0.3	0.2	0.8	0.5	0.4	−	0.3	0.4	0.1	3.8
For Ext	230330	10	0.0	0.0	0.0	0.0	0.0	0.0	−	−	0.0	−0.0	0.0
Urb	240100	1357	−1.7	−2.3	−2.2	−1.4	−0.2	−1.6	−2.9	−1.7	−1.7	−2.4	−18.0
Int	240210	4245	−2.2	−0.7	−0.9	1.2	−5.0	−0.6	2.1	3.9	−0.8	−0.7	−3.5
Int	240220	2158	−0.2	−0.2	−0.2	−0.5	−1.0	−1.4	0.3	−1.5	0.4	−1.0	−5.2
Int	240230	259	−0.1	−0.1	−0.0	−0.1	−0.0	−0.0	−0.3	−0.2	−0.1	0.0	−1.0
For Ext	240310	819	1.2	1.2	0.7	1.6	1.2	1.3	−0.9	−0.3	1.2	1.0	8.2
For Ext	240320	1320	1.2	0.0	0.3	1.3	0.9	0.7	−1.5	−0.3	0.1	0.5	3.2
For Ext	240330	75	−0.0	−0.0	0.0	0.0	0.0	0.0	−0.1	−0.1	−0.0	−0.0	−0.1
Urb	310100	137	−0.4	−0.4	−0.3	−0.4	−0.2	−0.4	−0.1	−0.1	−0.4	−0.4	−3.2
Int	310210	315	−0.6	−0.5	−0.3	−0.5	−0.8	−0.5	0.5	0.4	−0.5	−0.4	−3.4
Int	310220	415	−0.7	−0.7	−0.4	−0.9	−0.8	−0.9	0.5	−0.1	−0.5	−0.7	−5.1
Int	310230	59	−0.1	−0.1	−0.1	−0.1	−0.1	−0.1	−	−0.0	−0.1	−0.1	−0.8
Etcs	310240	928	−1.4	−1.4	−0.8	−1.9	−1.4	−1.4	1.1	0.3	−1.4	−1.1	−9.3
Perm	310320	2219	−1.3	−3.3	−1.4	−2.3	−1.7	−2.2	−	0.3	−3.1	−1.7	−16.7
Perm	310330	177	−0.3	−0.3	−0.1	−0.2	−0.2	−0.2	−	−0.1	−0.3	−0.3	−1.9
Urb	320100	136	−0.3	−0.2	−0.2	−0.3	−0.1	−0.2	−0.1	−0.1	−0.2	−0.3	−2.1
Int	320210	1190	−1.7	−0.2	−0.5	−0.8	−2.2	−0.8	2.0	1.4	−0.3	−0.7	−3.9
Int	320220	243	−0.2	−0.0	−0.1	−0.3	−0.3	−0.3	0.3	−0.1	0.0	−0.2	−1.2
Int	320230	543	−0.8	−0.3	−0.2	−0.7	−0.5	−0.4	−	−0.3	−0.3	−0.1	−3.5
Etcs	320240	2037	−1.9	−0.1	−0.4	−2.0	−1.4	−1.1	2.4	0.5	−0.2	−0.8	−5.0
Perm	320310	3692	2.2	5.4	2.4	3.8	2.9	3.6	−	−0.5	5.1	2.9	27.8
Perm	320330	646	−0.7	−0.1	−0.1	−0.2	−0.3	0.0	−	−0.3	−0.2	−0.5	−2.3
Urb	330100	18	−0.0	−0.0	−0.0	−0.0	−0.0	−0.0	−0.0	−0.0	−0.0	−0.0	−0.2
Int	330210	60	−0.0	−	−0.0	−0.0	−0.1	−0.0	0.1	0.1	−	0.0	0.0
Int	330220	17	0.0	0.0	−0.0	−0.0	−0.0	−0.0	0.0	0.0	0.0	−0.0	−0.0
Int	330230	8	−0.0	−0.0	−0.0	−0.0	−0.0	−0.0	−	−	−0.0	0.0	−0.0
Etcs	330240	99	0.0	0.0	−0.0	−0.1	−0.0	−0.1	0.1	0.1	0.0	0.0	0.1
Perm	330310	108	0.2	0.2	0.1	0.1	0.1	0.1	−	0.0	0.2	0.2	1.2
Perm	330320	2200	2.3	0.4	0.3	0.7	1.0	−0.1	−	1.0	0.6	1.6	7.7
Perm Macro	-	261,823	−	−	−	−	−	−	−	−	−	−	−0.0
Other	-	1080	0.9	1.2	1.2	0.9	−0.0	0.9	1.3	0.8	0.8	1.2	9.0
	tot	301,605	−3.5	−3.7	−4.5	−4.1	−0.2	−4.3	−3.8	−3.2	−2.2	−4.7	−34.1

), it is possible to comment that the greatest negative and positive variations were found, on one hand, in (i) the erosion regulation ES as a consequence of the agricultural intensification processes of heterogeneous agricultural areas being transformed into arable zones (−5.0) and, on the other hand, in (ii) the air quality regulation ES, as a response to forest extension at the expense of shrublands (+5.4).

The effective changes in the ES balance generated by the territorial LULC dynamics (Table A3) were then grouped by LULC transition categories and SNAI area types (see

Table 4. Total and mean ES qualitative balance variation at the SNAI area level. The colour gradations are proportional to the intensity of the change (negative in red, positive in green) in the ES balance.

SNAI Code	Global Climate Regulation	Air Quality Regulation	Water Flow Regulation	Water Purification	Erosion Regulation	Natural Hazard Regulation	Crops	Fodder	Timber	Wild Food and Resources	TOT	MEAN
A	−0.8	−1.2	−1.1	−0.9	0.2	−0.8	−1.5	−1.1	−0.8	−1.2	−9.3	−0.9
B	−0.1	−0.2	−0.2	−0.1	−0.1	−0.1	−0.2	−0.1	−0.1	−0.2	−1.4	−0.1
C	−1.6	−2.7	−2.4	−2.5	0.3	−2.4	−2.9	−2.7	−1.9	−2.9	−21.7	−2.2
D	−0.3	−0.2	−0.7	−0.6	0	−0.8	0	0	0	−0.5	−3.1	−0.3
E	−0.7	0.4	−0.2	−0.3	−0.7	−0.3	0.9	0.7	0.3	0	0.1	0
F	0.1	0.2	0.1	0.2	0.1	0.2	−0.1	0.1	0.2	0.2	1.3	0.1
TOT	−3.5	−3.7	−4.5	−4.1	−0.2	−4.3	−3.8	−3.2	−2.2	−4.7	−34.1	−3.4

below).

The results reveal that the belt municipalities (C) experienced the most pronounced negative variations in the total ES balance (−2.7) among all SNAI areas. These changes were attributable to a drastic decline especially in the provision of crops (−2.9), wild food and resources (−2.7), and forage (−2.9).

In belt areas, the primary driver of this change was the urbanization of arable land (from 211 to 100) and heterogeneous agricultural areas (240 to 100), which, respectively, resulted in a loss of ES balance of −11.0 and −7.7 points. Urbanization, in fact, generated an increase in ES demand and a drastic decline in ES supply. Further negative variations, smaller in magnitude than the preceding ones (between −3 and −2.5), could be observed in relation to (i) internal transformation within forest classes (from forests 310 to shrublands 320) and (ii) processes of agricultural intensification, specifically from forests (310) to permanent crops (220) and heterogeneous agricultural areas (240). Conversely, in terms of positive ES balance variations, we observed a +3.8 in changes within the forestry classes, i.e., from 320 to 310.

The results of this analysis allow for the identification of two distinct patterns, which exert a differential influence on central and internal SNAI areas. In particular, in central areas, the process that most negatively affects ES balance is the urbanization of agricultural areas (especially arable land). In contrast, in internal areas, the worst process in terms of variation in the ES balance has been identified to include LULC transitions within forest classes, and, in particular the transitions from forests to shrublands (310320). Furthermore, the order of magnitude of negative fluctuations in central areas is significantly higher than in interior ones. Concerning positive ES balance variations, the most influential LULC change dynamic in both interior and central SNAI areas is the conversion of shrublands into woodlands (320310). This dynamic is responsible for an increase in the overall ES supply.

The land use changes that took place between 1990 and 2018 resulted in a total average variation in the ES balance that was markedly negative (see

Table 5. Qualitative ES average balance variation by LULC transitions and permanence categories. The colour gradations are proportional to the intensity of the change (negative in red, positive in green) in the ES balance.

Transitions and Permanence	A	B	C	D	E	F	TOT by Transitions and Permanence
Agr. Extensification	0.01	0.01	0.03	0.04	0.02	0	0.11
Transformation to complex system	0.07	0.02	0.23	0.2	0.06	0.01	0.59
Agr. Intensification	−0.26	−0.08	−1	−1.02	−0.94	−0.25	−3.55
Forest extension	0.13	0.06	0.4	0.63	0.61	0.25	2.08
Urbanization	−1.05	−0.19	−2.22	−0.93	−0.43	−0.11	−4.94
Other	0.16	0.02	0.36	0.24	0.13	0.04	0.96
Permanence	0.01	0.01	0.03	0.54	0.56	0.19	1.34
TOT by SNAI areas	−0.93	−0.14	−2.17	−0.31	0.01	0.13	−3.41

below), with a final value of −3.41, out of a possible range ranging from −5 to +5. Going into more detail, the land use changes that occurred between 1990 and 2018 resulted, on average, in a fairly marked decrease in the overall balance of ESs in belt (−2.17) and hub (−0.93) areas. Less marked but still negative were the changes that occurred in the intermediate (−0.31) and inter-municipal hubs (−0.14). Slight increases, on the other hand, were recorded for the more inland areas such as the peripheral (+0.01) and ultraperipheral (+0.13) areas. These changes in the ES balance were driven by land use and land cover transitions that occurred in each SNAI area. Almost 90% of the urbanization that occurred at the Italian national level occurred, in fact, in the belt (46%), hub (21%), and intermediate (19%) areas, causing, especially in the case of the belt and hub areas, the greatest relative decrease in SEs (−2.22 and −1.05, respectively). Also, agricultural intensification led to decreases in the ES balance, especially in intermediate (−1.02), belt (−1.00), and peripheral (−0.94) areas. Regarding the positive changes in the ES balance, reforestation was the process that, on average, led to the most marked increase in service (+2.08), followed by the processes of complex system evolution (+0.59) and agricultural extensification (+0.11). As it is possible to note, permanencies and the other categories also led to positive increases. In particular, the permanence category also included internal forest transitions (320310) that led to ES balance growth. In general, reforestation processes resulted in point increases in all SNAI areas. However, in the case of peripheral and ultraperipheral areas, these processes were not counterbalanced by the processes of urbanization and agricultural intensification, leading to a final negative balance variation.

3.3. Economic Evaluation of the ES Supply

The results of the economic analysis of the supply of ESs are presented in

Table 6. Economic valuation of ES supply in SNAI areas and its change from 1990 to 2018. The colour gradations are proportional to the intensity of the change (negative in red, positive in green) in the ES supply economic value.

SNAI Code	km2	1990		2018		VAR 1990−2018
		MLN €	EUR/ha	MLN EUR	EUR/ha	Var EUR/ha	Var MLN EUR
A	28,857	9702	3362	9383	3252	−110	−319
B	8746	3125	3573	3102	3546	−27	−24
C	84,605	29,582	3496	28,889	3415	−82	−692
D	87,705	28,418	3240	28,331	3230	−10	−87
E	72,397	22,162	3061	22,300	3080	19	137
F	19,296	5518	2860	5576	2890	30	58
TOT	301,605	98,508	3266	97,581	3235	−30	−927

below. The results obtained are the total and average (per hectare) economic value of the ES supply in each SNAI zone for the years 1990 and 2018, as well as the differences in the supply between the two periods. In terms of the average change per hectare, all central areas experienced a significant decline, including the intermediate (D) zone. This was particularly evident in hubs and belts. Conversely, peripheral (E) and ultraperipheral (F) areas experienced a slight increase. With regard to the total economic value, the belt (C) appeared to be the zone most affected by land use changes. The total variation amounted to −692 million EUR (74% of the total variation at the national level). As was also the case for the SNAI central areas, specifically hubs, decreases were the results of the loss of agricultural areas (in particular arable land). Notably, these results in terms of economic changes in the ES supply were in line with the findings of our qualitative balance evaluation (Table 4). Also concerning the qualitative balance, SNAI areas A, B, C, and D exhibited a reduction over time, while areas E and F demonstrated slight increases.

3.4. Analysis of Socio-Economic Transformations and Comparison with the Variation in the ES

Table 7. Comparison of variations in territorial socio-economic indicators, qualitative balance, and economic value of ESs. (Note: The changes in the socio-economic indicators are assessed in the period of 2012–2018, while the changes in the ES values are for the period of 1990–2018.).

SNAI Code	Population (var %)	Number of Companies (var %)	Number of Employees (var %)	ES Average Qualitative Balance Variation (−5, +5)	ES Mean Economic Value (var EUR/ha)	ES Total Economic Value (var MLN EUR)	ES Economic Value per Inhabitant (var EUR/Inhabitant)
A	−0.9	0.9	6.9	−0.9	−110.5	−318.8	−10.7
B	3.6	−1.3	2.2	−0.1	−27.2	−23.8	−45.5
C	16.4	−3.0	1.1	−2.2	−81.8	−692.5	−248.1
D	6.2	−3.9	−1.0	−0.3	−9.9	−86.9	−213.9
E	−5.7	−3.7	−0.9	0.01	19.0	137.4	379.2
F	−9.4	−3.2	0.5	0.1	30.1	58.0	827.9

below provides a summary of the indicators of social and economic dynamics in relation to the economic and qualitative variation in ESs. The areas exhibiting the most pronounced decline in population between 1990 and 2018 are the peripheral (−5.7%) and ultraperipheral ones (F) (−9.4%). This trend confirms the long-term depopulation dynamics in the most marginal and isolated municipalities. This phenomenon contributed to the activation of the National Strategy for Internal Areas during the same period, when municipalities in the belt category experienced an increase of 16.4%. In terms of economic dynamics, indicators related to the number of employees and companies for the period 2012–2018 describe a negative trend in the number of companies, particularly in the intermediate (−3.9%), peripheral (−3.7%), and ultraperipheral (−3.2%) municipalities.

These data indicate weaker job creation in peripheral municipalities, consistent with the decrease in the number of employees in the intermediate and peripheral municipalities. Comparing the economic and social data with the estimate of ESs shows a negative economic value variation in the more central municipalities, but a positive variation in the peripheral and ultraperipheral municipalities. Importantly, data on the economic variation in ESs confirm that inland areas play a central role in the delivery of benefits. The economic value per hectare and inhabitant are higher in the peripheral and outermost areas, where depopulation and economic decline have been more marked. In the central areas, in fact, there is a greater demand for goods and services than in the inland areas, as shown by the qualitative values of the balance.

4. Discussion

Today, there is considerable interest in inland areas in institutional and scientific circles due to awareness of their great potential and the possible new settlement scenarios that could emerge in these areas [42]. The SNAI strategy aims to enhance the economic value of territories and improve the provision of essential public services [43].

The innovative contribution of our work was to provide a study of the dynamics of land use change and ESs provision in the territories within SNAI areas.

In Italy, scientific research on SNAI areas has been carried out by several authors [42,44,45], with only a few of them focusing on ESs [46]. Our investigation further examines the impact of socio-economic transformations on land use changes and ES provision. Some authors have demonstrated how spatial transformation processes linked to socio-economic factors, have impacted land use change and the provision of ESS (see, for example, [12,47]). The spatial heterogeneity of ES and their functional relationships are indeed influenced by natural and socio-economic factors [48,49].

In this paper, we highlight the significant impact of spatial dynamics on land use and the provision of ESS. Depopulation and economic decline in mountainous and inland areas have led to land abandonment in marginal areas, resulting in natural reforestation. Conversely, agricultural intensification has increased in easily accessible areas such as hubs and intermediate hubs. Another crucial spatial dynamic is urbanization, which has significantly impacted the belt areas over the period under investigation (1990–2018). The aforementioned spatial dynamics have significantly impacted the balance between supply and demand. There is an increased demand for goods and services, especially in urbanization and intensification processes, while reforestation has resulted in a surplus of supply. However, reforestation has also resulted in an increase in regulation ESs (e.g., CO₂ absorption) at the expense of provisioning ESs.

To fully understand the various paths of territorial development, it is essential to analyze the characteristics and dynamics of the socio-economic structure of the areas in question. This analysis provides insights into the interaction of natural, social, and economic capital within socio-ecological systems (SESs) [50]. When natural capital contributes to ESs, its interaction with social and economic capital affects the overall economic well-being of the community [18]. The interactions between natural, social, and economic capital are influenced by various complex mechanisms and depend on several factors. Natural capital comprises ecosystems that form without human intervention and provide the resources that support economic activities [18,51]. Economic capital consists of human-produced capital, including consumer goods, intermediate goods used for production, and financial capital. Social capital, including human capital, encompasses social networks, norms, culture, and institutions that facilitate cooperation [52,53]. The combination of these capitals generates various human welfare benefits. Analyzing these interactions can offer insights into achieving environmental, economic, and social sustainability goals. The use of indicators to monitor the achievement of sustainability goals is a valuable tool for such analyses. In the present study, we selected some indicators that are most representative of the phenomenon under investigation. We chose three indicators to analyze the potential relationships between the three forms of capital: the economic value of ESs (natural capital), the resident population (social capital), and the number of employees (economic capital).

The indicators were selected on the basis of the availability of data for the specified reference period. Although they could be improved, they provide a framework for interpreting the phenomena that have occurred in the country’s inland areas in recent decades.

The results demonstrate that the territorial changes in the SNAI areas resulted in a positive increase in the economic capital in the hub areas (+1.00), while, at the same time, there was a decrease in the natural capital (−0.46) in the same areas. Additionally, social capital experienced significant growth, particularly in the belt areas (+1.00), which appeared to be the final destination for the population moving from the hubs and inner areas (Figure 4).

It is worth noting that the social and economic factors affecting these areas have not caused a significant increase in the supply of ESs. Natural capital shows weakly positive values in peripheral and ultraperipheral areas. The provision of ESs depends, in fact, on the interaction of various forms of capital and the proper maintenance of the territory. This, in turn, affects the provision of essential goods and services for society, such as food and fodder supply [18,50].

The combination of different capital forms is indispensable in the integration of social, economic, and ecosystem perspectives and the fusion of these concepts to better understand and manage ESs [54]. It is therefore crucial to consider strategies that balance economic development with the conservation of natural capital, for example, by identifying and preserving, at least, critical natural capital [55,56] in the most urbanized areas.

Further developments in our research will have to focus on overcoming the current limitations, refining the approach presented here, and applying it to other contexts. Future investigations could involve analyzing the relationships among different ESs through the concept of “bundles”. Bundles are defined by Raudsepp-Hearne et al. (2010) [57] as a “set of associated ES that repeatedly appear together in time or space”. In effect, bundle analysis allows for the evaluation of potential synergies and trade-offs, thereby supporting appropriate landscape planning and management [19]. Additionally, it would be undoubtedly beneficial to incorporate a quantitative assessment of the demand for ESs. This would enable a quantitative assessment of the relationship between supply and demand and, thus, also of the balance of the ESs over time. The advantages of economic evaluation are manifold and include the possibility of comparing variables and indicators commonly used in cost–benefit analyses that underlie, for example, the ex ante evaluation of public policies, possible public–private agreements, and the activation of potential payment for ecosystem service (PES) schemes. Another line of future research can be identified in the application of the ES balance approach in other specific territorial areas such as protected areas or metropolitan cities. This would facilitate the analysis of the effects of different forms of spatial planning on the dynamics of ES production and consumption over time.

5. Conclusions

Our research stands out from others conducted in Italy [58,59,60] within SNAI areas due to our focus on analyzing the changes in ESs in connection to social and economic factors from 1990 to 2018. This was achieved by combining transition and permanence matrices with a qualitative ES matrix and using coefficients to estimate economic changes. Although our approach is experimental, it has allowed us to demonstrate how spatial changes have impacted natural capital and the provision of ESs and identify which SNAI areas have been most affected by these changes. The LULC analysis revealed that agricultural intensification represents the most significant transition across all SNAI areas, while urbanization has a greater impact on central areas compared to inland areas. Urbanization processes have caused a considerable increase in demand and a decrease in the supply of ESs, bringing the ES balance into deficit. These spatial dynamics have influenced both the qualitative change in demand and supply and the economic value of ESs. On average, during the period studied, all central areas experienced significant decreases in terms of ES economic value per hectare. These results suggest the need for government agencies to develop sustainability governance models to support the implementation of territorial policies and strategies. Furthermore, the research results provide a tool for mapping the data, which allows for the identification of areas requiring prompt intervention for the purposes of improved planning and management. Additionally, the economic estimation of ESs highlights their areas of surplus and deficit and identifies the sustainable limits of natural resource consumption. This approach can also support the implementation of SEEA Ecosystem Accounting (SEEA EA) [61], which is designed to account for global and local governance. The findings of this study are pertinent to the implementation of Act No. 22, from 28 December 2015, which focuses on environmental measures designed to promote a green economy and reduce the overuse of natural resources. This law introduced specific obligations for municipalities to contribute to the conservation and protection of natural capital. It also requires municipalities to develop a preliminary strategy for accounting for natural capital and ESs, as well as implementing measures for their valorization, which may include payments for ESs. One of the key objectives of SNAI is to enhance productive land use and address demographic decline to strengthen the overall cohesion of the national territory. In this context, the ESs provided by these territories should be at the core of development policies that take into account their value in community well-being, as also indicated by the recent Kunming–Montreal Global Biodiversity Framework (COP 15).