Urban Green Spaces and Climate Changes: Assessing Ecosystem Services for the Municipality of Sassari (Italy)

Abstract

Urban green spaces (UGS) supply a wide range of ecosystem services (ESs), which are key to mitigation and adaptation to climate changes. In this study, we focus on two ESs, i.e., greenhouse gas sequestration by terrestrial ecosystems and mitigating the heat island effect through vegetation, as defined by the Common International Classification of Ecosystem Services. The purpose is to support municipalities with characteristics similar to those of the municipality investigated in this study with a rough assessment of ESs through freely available data. The ES delivery capacity assessment relies on the adoption of two indicators: (i) increased carbon storage in forests and (ii) the Heat Island Mitigation Index (HIMI). We applied the method to the UGS of the municipality of Sassari (Italy) and found that the potential amount of carbon storage is 42,052.7 t, while the value of HIMI provided by the green spaces in the homogeneous territorial areas is 67.73%. The methodological approach adopted in this study is potentially applicable in Italian as well as Mediterranean small to medium municipalities to integrate the quantitative assessment of ESs in local planning tools. The novelty of this study lies in the applied practical approach, which is implementable by public bodies lacking data and resources, to assessing prima facie the need for operational climate adaptation and mitigation strategies.

1. Introduction

Urbanized areas are increasingly hosting humans [1,2], who are addressing the effects of a changing climate [3,4,5,6,7]. Climate changes due to human (e.g., the emission of carbon dioxide produced by the combustion of fossil fuels, increasing livestock farming, etc.) and non-human (e.g., volcanic eruptions) activities are responsible for global warming, which often implies extreme weather events such as high temperatures and excessive rainfall [8,9] or potential positive effects on crop production (e.g., cotton yields and rice growth [10,11]).

Well-known effects exacerbated by global warming include flooding, landslides, and heat waves [12,13]. Heat waves (or heat extremes), which are characterizing summer seasons around the world [14], negatively affect people’s health (e.g., dehydration and heat stroke are recurrent cause of death [15]), and are responsible for increased mortality and morbidity. In urbanized areas, the heat waves intensify their effect due to the urban heat island (UHI) [13]. In this study, we define the UHI effect as the phenomenon where “cities are warmer than their surrounding hinterlands due to the complex topography and mass of buildings, replacement of pervious vegetated surfaces with impervious built surfaces and the emission of heat from anthropogenic activities” [15]; the UHI is a noticeable feature of urban climate [16].

According to several scholars, urban green spaces (UGS) deliver a variety of ecosystem services (ESs) [17,18,19,20,21,22], including cultural services (e.g., spiritual and recreation) and regulating services (e.g., microclimate regulation and carbon sequestration). After all, the benefits supplied by green spaces are well-known from decades [23,24,25,26] and publications regarding green spaces date back to the 1970s [27]. Scientific literature proposed many definitions of ESs. According to highly cited scholars, ESs “represent the benefits human populations derive, directly or indirectly, from ecosystem functions” [28,29]. The Millennium Ecosystem Assessment defines ESs as “the benefits people obtain from ecosystems” [30]. In this study, we define ESs as intended by Rodrigues Ferreira Barbosa et al., i.e., “the ecological characteristics, functions, or processes that directly or indirectly contribute to human well-being through various pathways” [20]. UGS also deliver ecosystem disservices (EDiss), which are “defined as the negative effects of nature on human wellbeing” [31] and include plants responsible for allergies and restoration of wetlands that can increase the incidence of diseases, such as malaria [32]. Some scholars argue that a comprehensive understanding of how nature affects human well-being requires an integrated assessment of ES and EDiss and this should also lead to better sustainability policies [33].

In this study, we define ‘green space’ as “any vegetated land within an urban area, which could be publicly or privately owned and may include but is not limited to parks, landscaped streets, or residential gardens” [34]; furthermore, we use ‘green cover’ or ‘green area’ as a synonym of ‘green space’. These are of paramount relevance also considering the European Restoration Regulation (2024) [35], the EU Biodiversity Strategy for 2030, which also aims to halt the loss of green ecosystems in urban centers and promote healthy ecosystems and green infrastructure, which should be thoroughly integrated into urban planning [36], and the Sustainable Energy and Climate Action Plan as solution that partially contributes to reducing the greenhouse gases in the atmosphere and mitigating the urban heat island effect [37]. Green spaces contribute to mitigating the heat island effect [15,38,39,40] and are important for reducing atmospheric carbon dioxide, because they act as natural carbon sinks [41,42,43].

In Italy, scholars have extensively addressed research on urban green spaces according to multifaceted perspectives [44,45,46,47,48]. De Meo et al. [44] investigated the citizens’ use of green areas before and during the two lockdowns that occurred in Italy due to the Coronavirus Disease 2019 and found that the pandemic intensified the positive citizens’ perception of UGS. Giannico et al. [45] focused on the link between green spaces in residential areas and death prevention, and found that green spaces contribute to reducing deaths. Sallustio et al. [46] quantitatively estimated the relevance of green spaces in built-up (GSB) areas in Italy and found that most of them are sealed (about 55%). The surface area covered by GSB (18%) mostly consisted of forest. Speak et al. [49] focused on the pros (e.g., carbon storage and sequestration) and cons (e.g., pollen production for allergy sufferers) of using different tree species for green spaces in three Italian town squares (piazzas) in Bolzano, northern Italy. The authors set nine scenarios for each town square, which were based on the public’s perspective gathered via workshops and data acquired through a smartphone app. Among other things, the findings suggested that scenarios with larger trees supplied most ESs although larger trees were also responsible for increased ecosystem disservices (EDiss), which can be reduced with wise species selection.

The above premise emphasizes that the planning and design of UGS are relevant to providing the citizens with the maximum benefits (ESs), while avoiding or reducing pitfalls (EDiss).

Speak et al. [49] point out a general lack of data for practitioners at the municipal level, and greening programs rarely consider indicators of ESs and EDiss. Thus, pragmatic decisions are often based on a limited set of indicators. In 2022, Vidal et al. [50] recalled a study performed by Dias et al. [51], who focused on a comparative analysis of the municipal master plan strategies on public UGS in Porto and Lisboa (Portugal). Dias et al. [51] found that the lack of data is one of the main reasons that prevents adequate intervention in public UGS. In their research, which aimed at identifying groups of ESs potential in public UGS in Porto, Vidal et al. [50] remarked on the lack of data as one of the limitations of the study. Finally, Lourdes et al. [52] listed the lack of data as one of the causes why some models, e.g., recreational services, may lack accuracy. Therefore, lack of data is commonly identified by scholars as one of the limitations of research on UGS.

In a context lacking adequate data and resources, practitioners may face a variety of difficulties when they have to assign quantitative values to ESs and EDiss. As some municipalities may address lack of data when defining operational climate adaptation and mitigation strategies based on UGS, the aim of this study is to contribute to filling this research gap, by providing municipalities with climate, institutional, geographical, and other characteristics (e.g., a certain lack of data for in-depth analyses) similar to those of the municipality investigated in this study, with a methodological approach to integrate the quantitative assessment of ESs in local planning tools (e.g., municipal master plan, green plan, or green program), by considering freely available data and software (i.e., data and software accessible to anyone with Internet). So, the research question is: can we suggest to municipalities a practical approach to quantify the ESs provided by green spaces, when available data are limited? The purpose is to provide the municipalities with support for a rough assessment of ESs through freely available data and methods rooted in scientific basis. We assume this method allows for a comparative approach by pointing out the best and worst performances of similar local administrations. This may be relevant to municipalities that ask for more financial resources (e.g., the worst performing municipalities could ask for more financial resources to reduce the gap with the best performing municipalities). In this study, we focus on two ESs as defined by the Common International Classification of Ecosystem Services (CICES; [53]): (i) greenhouse gas sequestration by terrestrial ecosystems, and (ii) mitigating the heat island effect through vegetation.

The article unfolds as follows. After the literature review in Section 2, in Section 3 we describe the method. In Section 4 and Section 5, we, respectively, summarize and critically comment on the findings. In Section 6, we answer the research question, briefly summarize the main findings, and highlight the limitations of this research.

2. Green Spaces for Climate Change Adaptation and Mitigation

The interest of scholars in green spaces in facing climate change has increased over time. As an example, the Scopus database indexed 851 articles (time span 1992–2024) with the search terms “green spaces” AND “heat island” and 228 articles (time span 2003–2024: no other journal articles indexed before 2003) with “green spaces” AND “carbon sequestration” (search within Article title, Abstract, Keywords; Figure A1 in Appendix A). We performed the same research on the Web of Science (by Topic) and found 698 articles for “green spaces” AND “heat island” (time span 2000–2024) and 155 articles for “green spaces” AND “carbon sequestration” (time span 2010–2024; Figure A2 in Appendix A). Research on green spaces and heat islands increased significantly after the mid-2010s, while studies on green spaces and carbon sequestration increased after 2020. Note that we did not consider other types of publications such as conference papers, book chapters, reviews, books, and notes, nor other search engines such as Google Scholar. A detailed review of the articles is beyond the scope of this work: recent relevant reviews can be found, for example, in Dong et al. [54] and Wu et al. [55]. As a representative study, Zhao et al. [42] performed a literature review with the aim of providing an overview on the research concerning UGS as carbon sequestration measure. They used search terms such as ‘public green space’ and ‘carbon sink’ in the Web of Science core database. After applying filters on the resulting publications (time span 2007–2022), 145 out of 2950 articles were analyzed using CiteSpace. They found that the role of urban public green spaces as a measure for emission reduction and carbon sequestration has gained increasing attention over the years. Below, we provide a summary of the main research we found relevant to the objectives of this study, by stressing the methodologies adopted and the main findings.

In Lisbon (Portugal), Reis and Lopes [39] estimated the cooling potential of green spaces. They used climatic data, mobile measurements, satellite images, Geographic Information System (GIS), and NDVI. The authors found that an amount of 50 m² of green cover (vegetation) reduces the air temperature by 1 °C. Reis and Lopes [39] argue that such a surface is the minimum area that has a certain relevance for improving the quality of the urban microclimate. Carter et al. [15] cite research of Gill et al. [40], who proved the benefits of green areas in terms of temperature reduction in Great Manchester, where the modelled surface temperatures were lower in medium density housing zones and woodlands than in the city centers [40], which were characterized by greater vegetation cover. Carter et al. [15] focused on the Oxford Road Corridor (Manchester, the UK) and discussed the partnership of stakeholders and landowners, which was aimed at improving an economic development area. To do so, the methodology consisted of two parts: the first one -a scenario-driven approach- included land cover assessment (Aerial photograph interpretation and Ordnance Survey MasterMap data), finding potential future land cover scenarios, examining climate change projections (information acquired through the Weather Generator), modelling surface temperatures (current and future); the second one regarded structured interviews involving the members of the Corridor Partnership Board. Among other things, they found that green spaces within the Oxford Road Corridor can mitigate rising temperatures and increase the quality of human life, which may be incentives for landowners and developers for investing in green areas. They also found a positive perception from the Corridor Partnership Board of the benefits brought by green spaces. Yao et al. [38] concentrated on the urban block as elementary spatial units with the purpose of investigating the role of urban green areas on affecting seasonal land surface temperatures in the urbanized region of Beijing (China). The authors used IKONOS and Landsat-8 series images in GIS environment and the ENVI (v 5.3) platform. They considered a set of factors clustered in landscape metrics of UGS (e.g., total area, patch density, etc.), urban block morphology (landscape shape index, Shannon’s diversity index, etc.), and UGS ratio. Yao et al. [38] confirmed the cooling effect of vegetation (e.g., urban forest) as solution to mitigate the UHI effect. Overall, citizens consider the shade and cooling provided by trees to be very important benefits, whose relevance is set to further increase in the expected scenario of increased frequency of heat waves [56].

Zhang et al. [41] investigated the carbon sequestration in urban green areas in Zhengzhou Green Expo Park, Zhengzhou City, Henan Province (China). The methodological approach consisted of three main processes: (i) data collection and quantification methods for carbon sequestration (photosynthetic instrument, LiDAR scanning system, soil drill, questionnaire survey, etc.); (ii) data analysis with MATLAB; and (iii) optimal strategies for increasing the carbon sequestration capacity of UGS. They found that plant density was more effective than coverage in terms of carbon sequestration, although a too high density may increase the mortality risk of individual plants. Russo et al. [57] proposed a protocol with the purpose of quantitatively assessing aboveground carbon storage and sequestration in Bolzano (Italy). The method was based on the use of context-specific allometric and dendrometrics equations and field measurements of selected trees (e.g., total and crown base height, percent missing canopy, tree circumference, etc.) and two models from the United States of America (USA). The study aimed at (i) assessing the carbon storage and sequestration through European and Italian allometric equations and local data from field measurements, and (ii) assess the performance—in terms of carbon storage and sequestration—of the context-specific method compared with the two USA models. The authors found statistical and practical differences in the three methods adopted for quantifying carbon storage and sequestration. Feng et al. [43] focused on the main urbanized areas of Xi’an (China) to assess the carbon neutrality potential of urban green spaces. They collected data consisting of (i) satellite images via the Google Earth Engine, which were used for calculating the kernel normalized difference vegetation index (kNDVI), land use classification, and surface temperature, and (ii) field sampling. The authors adopted specific equations to calculate carbon neutrality capacity estimation, carbon sequestration estimation, kNDVI, carbon emission reduction estimation, urban heat island intensity, and land surface temperature. They found that the relevance of urban green spaces in reducing carbon dioxide emissions is more relevant than the carbon sequestration capacity. The authors stated that carbon sequestration is less relevant than carbon emission reduction in terms of achieving carbon neutrality [43]. As stressed by Zhang et al. [58], urban green spaces are not always a carbon sink. The authors showed that irrigation and pesticide in the construction and maintenance of parks play a pivotal role as contributors to carbon emissions (see also [59]). Thus, the accurate selection of species and careful management of irrigation water are crucial for garden managers [58].

In this study, we cannot fully apply any of the previous approaches as it would be too demanding and time-consuming acquiring and elaborating on some key elements of the methods (i.e., software licenses, mobile measurements, IKONOS imagery, LiDAR scanning system, field sampling, and allometric equations). On the one hand, we can count on Landsat images, orthophotos, National Inventory of Forests, and Forest Carbon Reservoirs. On the other hand, we found that NDVI and GIS are adopted in previous research. Thus, we have tailored the method to our context (the municipality of Sassari) by using well-known approaches to obtain the maximum result with the minimum available data.

3. Materials and Methods

In this section, we describe the method applied to quantitatively assess ES1 (greenhouse gas sequestration by terrestrial ecosystems) and ES2 (mitigating the heat island effect through vegetation). Firstly, we describe the municipality (e.g., geographical context and planning instruments). Secondly, we state how we measured ES1 and ES2.

3.1. Case Study: The Municipality of Sassari (Italy)

The city of Sassari—120,875 inhabitants in 2023—is located in the island of Sardinia (Italy), about 10 km from the sea on a limestone plateau, a substratum that characterizes most of the neighboring areas (Figure 1). The elevation on the sea level is on average equal to 225 m in the main settlement and gradually decreases in the northwest historical region of ‘Nurra’ and the Gulf of Asinara, while the southeastern area is characterized by hilly land. Valleys and ravines alternate throughout the urban and peri-urban areas, significantly affecting the outline of the city. On the other hand, olive groves, wooded areas, and horticultural crops characterize the areas bordering the city.

The climate is typical of the geographic area and classified as inland Mediterranean, marked by mild, relatively wet, winters and dry, hot summers (Figure A3 in Appendix A provides details on average yearly temperature and yearly precipitation [60]). Average monthly temperatures range from 5 to 12 °C in the coldest months (typically, December, January, and February), and from 18 to 31 °C in the hottest months (July and August). However, the future climate scenarios of Sassari emphasized an increase in average temperature, temperature extremes, and periods with high temperatures [61]. Temperatures rarely fall below 0 °C and rainfall averages around 450–500 mm/year, but Barbato et al. [61] argue that Sassari is expected to face an increase in the intensity and frequency of extreme precipitation events.

UGS include important resources, such as the public municipal gardens. Built in 1870, these gardens span a surface area of over 29,000 m² and are divided into three main areas, two of which are completely fenced in [62]. Another remarkable green asset is the olive grove belt. It represents a typical identitarian element of the peri-urban context and serves as a connective element between the center and the agricultural zones. The olive grove belt has prominent relevance for the agricultural and historical landscape of Sassari. In this regard, the municipal master plan (MMP) provides for interventions aimed at preserving the olive grove strip [63]. Other flora consists of Mediterranean species, e.g., Quercus ilex (holm oak), Quercus suber (cork oak), Taxus baccata (yew), Ilex aquifolium (holly), Laurus (laurel), Alaternus (alaternus), Myrtus (myrtle), Pistacia lentiscus (lentisk), Erica arborea (tree heather), and Juniperus lycia (juniper lyceum). Chamaerops humilis (dwarf palm), Centranthus ruber (Jupiter’s beard), and Calluna vulgaris (pink heather) cover smaller areas [64].

The municipality of Sassari regulates the management of green spaces through a MMP adopted in 2012 [63] and a municipal code for the protection of urban green areas (MCPUGA) adopted in 2018 [65]. The MMP aims to achieve a green system useful for connecting urban areas to peri-urban and rural areas surrounding the city. The aim is to encourage the connection and merging of these areas, with the purpose of building an urban ecological network accessible to a variety of users, also recovering small areas, but that are sufficient to create micro neighborhood parks [63]. The MMP provides rules for the use of green areas through zoning, i.e., “[the] division of a city or county by legislative regulations into areas, or zones, which specify allowable uses for real property and size restrictions for buildings within these areas” [66]. The MMP of Sassari set zones such as agricultural areas, areas for general services, protection areas, etc., (see Appendix A) that can be relevant to the purpose of this study.

On the other hand, MCPUGA aims to promote protection, safeguard, improvement, and increase of the municipal plant heritage. The document establishes rules to ensure the protection and rational management of the green areas, which include all tree, shrub, and herbaceous cover grown in flowerbeds, gardens, parks, along roads, green areas of school and cultural buildings, in municipally owned homes, in cemeteries, and in private areas set aside as greenery and/or uncultivated.

The municipality is currently devoid of a green plan and an updated official census of green spaces.

3.2. Assessing the Ecosystem Services

The procedure of assessing ESs involved four steps and was inspired by Serra et al. [67]. In step 1, the study of the context has the purpose of identifying green spaces that are candidates for supplying ESs and have potential to be included in a green infrastructure. In step 2, we selected a set of target ESs that could be relevant to mitigation and adaptation to climate changes. In step 3, we defined a set of indicators to measure the potential delivery of ESs by the green spaces. In step 4, we assessed the capacity of the green areas to supply ESs.

Step 1 focuses on the environmental and ecological characteristics of the area of interest and serves the aim of choosing the most promising green areas to be included in the potential green infrastructure. Steps 2 and 3 are the cornerstones of the assessment framework and depend on the need of mitigating and adapting to a changing climate in a context where little data is available to plan adequate strategies and measures. In this study, the green areas (components) that could be part of a green infrastructure have a key role in terms of adaptation to climate change and carbon dioxide storage measures (greenhouse gas sequestration, which is a mitigation measure). In step 3, the set of indicators depends on the availability of adequate data. Step 4 focuses on the quantitative assessment of the selected ESs. In

Table 1. Ecosystem services (ESs) and selected indicators for the assessment of the delivery capacity of the UGS in the municipality of Sassari (Italy).

N	Ecosystem Services (ESs)	Indicators
		CICES Code	Description
ES1	Greenhouse gas sequestration by terrestrial ecosystems	2.2.6.1	Increased carbon storage in forests
ES2	Mitigating the heat island effect through vegetation	2.2.6.2	Heat Island Mitigation Index, HIMI (based on the Normalized Difference Vegetation Index, NDVI)

, we list the indicators that were adopted.

We selected two ESs classified by the Common International Classification of Ecosystem Services (CICES; [53]), whose supply capacity is assessed through two indicators: increased carbon storage in forests (CICES code 2.2.6.1) and Heat Island Mitigation Index (CICES code 2.2.6.2). The reasons for choosing the selected indicators depend on the availability of data, i.e., we could count only on two sets of data for assessing the supply capacity: data retrieved from the ‘2005 National Inventory of Forests and Forest Carbon Reservoirs’ [68,69], in textual format, for assessing ES1, and satellite images [70] for ES2.

3.2.1. Greenhouse Gas Sequestration by Terrestrial Ecosystems—ES1

Urban contexts are generally not considered for this type of analysis [69] for the following reasons: the idea that the impact of land use change on ecosystem service is greatest in areas with a high naturalness; the low ecological value that is attributed to the urban ecosystem; the small size of carbon storage reservoirs, compared to actual emissions; and the very low storage capacity attributed to the urban setting [69].

As for the study area, no site-specific analyses or data on the potential carbon storage capacity were available, thus we considered data provided by the National Inventory of Forests and Forest Carbon Reservoir [68,69]. The rationale we adopted is rooted in the use of the normalized difference vegetation index (NDVI). NDVI is useful for acquiring information on vegetation cover and is largely adopted by scholars [71]. NDVI is calculated from satellite data and varies depending on the reflection of solar radiation by vegetation cover.

NDVI is expressed according to the Equation (1) [39,72]:

$$\mathrm{NDVI}={\displaystyle \frac{\mathrm{N}\mathrm{I}\mathrm{R}-\mathrm{R}\mathrm{E}\mathrm{D}}{\mathrm{N}\mathrm{I}\mathrm{R}+\mathrm{R}\mathrm{E}\mathrm{D}}}$$

(1)

where NIR (near-infrared—Band_08) is the measure of spectral reflectance in the near-infrared region, while RED (Red—Band_04) is the measure of spectral reflectance in the red acquired in the visible region. NDVI ranges from −1 (e.g., lakes, rivers) to +1 (e.g., rainforests or temperate forests); Huang et al. [71]. Values of NDVI in the range 0.1–0.5 characterize minimal to sparse vegetation while 0.6–1 implies areas with dense green vegetation [72,73].

The National Inventory of Forests and Forest Carbon Reservoirs provides us with three ranges of NDVI, which are linked to specific forest categories (

Table 2. Carbon stored per unit area (t · ha⁻¹) according to the values of NDVI.

Classes	Values of NDVI	Forest Category	Carbon Stored per Unit Area (t · ha−1)
Non-photosynthetic areas	−0.553–0.226	Areas devoid of vegetation	0.0
Low canopy cover	0.226–0.488	Temperate areas devoid of topsoil	6.9
High canopy cover	0.488–0.807	Deciduous evergreen forests	21.4

).

According to Table 2, ‘Non-photosynthetic areas’ do not contribute to carbon storage, while low and high canopy cover have the maximum potential store capacity of 6.9 and 21.4 t · ha⁻¹ [69].

3.2.2. Mitigating the Heat Island Effect Through Vegetation—ES2

Reis and Lopes [39] argue that urban vegetation has proved to be useful for reducing air and surface temperature. Green areas are, therefore, usually cooler than the surrounding environment: this effect is called Park Cool Island (PCI) or Cool Island Effect [39]. Typology and features of vegetation affect PCI and NDVI are commonly adopted as quantitative metrics to assess vegetation in urbanized areas [39].

In this study, we applied the Heat Island Effect Mitigation Index (HIMI; [67]) to quantitatively assess the performance of the green cover in the municipality of Sassari. HIMI lies on the ratio between A_green and a reference surface area (A). HIMI obeys Equation (2):

$$\mathrm{HIMI}={\displaystyle \frac{{\mathrm{A}}_{\mathrm{g}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}}}{\mathrm{A}}}\times 100$$

(2)

where HIMI (in percentage) ranges from 0% (A_green = 0) to 100% (A_green = A). A_green is the area occupied by green areas, which can be considered as potential components of green infrastructure; we consider green areas (A_green) showing NDVI ≥ 0.429, which implies lush vegetation [67]. The value of 0.429 is also consistent with the threshold imposed by Speak and Salbitano [56], who considered 0.4, which stands for medium- to high-density greenery, excluding areas covered by patchy grass and small shrubs [56]. Finally, note that 0.429 is close to 0.488, which characterizes high canopy cover [74].

A refers to two different contexts: A_zon for the surfaces occupied by green areas included in the homogeneous territorial areas (

Table A1. Zones that include green spaces that can be relevant to the purpose of this study.

Zone Code	Label	Sub-Zone Code	Description
E	Agricultural areas	E5.a	Marginal agricultural areas that require adequate environmental conditions. These areas can also be used for low-impact agro-zootechnical and silvo-pastoral activities.
		E5.c	Marginal agricultural areas that require adequate environmental conditions. Areas of low agricultural importance, which are relevant to soil protection and conservation.
G	Areas for general services	G2	Urban parks, sports and leisure facilities.
H	Protection areas	H2.8	Areas of environmental and landscape value (urban valleys).
		H2.9	Areas of environmental and landscape value (woods and forests).
		H4	Green areas, which are mainly adjacent to urban and extra-urban public roads. The construction of buildings is forbidden.
S	Areas for equipped public spaces	S3	Areas defined in the green plan and regulation such as simple green areas, neighborhood parks, etc.
		S3/p	‘Building credit’ areas. ‘Building credit’ means building capacity recognized after implementing measures concerning the improvement of urban, landscape, and environmental quality.
		SD3 e SD4	Areas for equipped public spaces in industrial areas.

) and A_pu for the peri-urban and urban contexts. The higher the HIMI, the larger the surface covered by vegetation, which implies high NDVI.

We used satellite images available online for free [70] and additional data, whose main characteristics are listed in

Table 3. Description of the data.

Data	Format	Resolution/Scale	Year	Source
Satellite images (Copernicus)	Raster	100 m2 per pixel	2024	[70]
Orthophotos	Raster	0.20 m	2019	[75]
Municipal master plan of Sassari—zoning	Shapefile	scale 1:4000	2012	[76]

.

Satellite data were considered for assessing both ES1 and ES2. The images date back to 29 July 2024, which fell in the hottest weeks of that summer: this is relevant to properly consider the heat island effect. Furthermore, carbon dioxide capture and storage achieve maximum values in summer. The 20 cm resolution orthophotos date back to 2019 and use the national reference system RDN2008 UTM32N, where RDN2008 means ‘Rete Dinamica Nazionale 2008’, i.e., 2008 National Dynamic Network, and UTM32N, means Universal Transverse Mercator Zone 32 North. Finally, zoning represents the most important green areas of the municipality in terms of surface area. Both ES1 and ES2 have been assessed using the surface areas occupied by urban and peri-urban areas (see Table A1). Zone E3 is the boundary (buffer) that separates the urban and peri-urban areas from the rural areas. Beyond the limit of this buffer, the surface of green areas is negligible when compared to that of the urban and peri-urban context. Data were analyzed and managed in Quantum GIS version 3.40.

4. Results

As a first step, we calculated the values of NDVI for the urban and peri-urban zones of Sassari (Figure 2).

Figure 2 shows that, especially in the western and central part, most areas are characterized by low to medium values of NDVI, while in the eastern part the values are predominantly high. In Section 3.1 and Section 3.2, we summarize the main findings concerning the quantitative assessment of ES1 and ES2, which relies on the calculated values of NDVI.

4.1. Greenhouse Gas Sequestration by Terrestrial Ecosystems

We focus on the urban and peri-urban areas set by the zoning of the MMP of Sassari. Although no data are available about the potential carbon storage capacity in the urban and peri-urban areas, the rationale for estimating the ESs is to select three classes of NDVI, which are associated with specific forest categories (Table 2, Section 2). The total amount of organic carbon is assessed by linking the NDVI to each category.

Table 4. Carbon stored by low and high canopy cover.

Classes	Surface Area (ha)	Carbon Stored per Unit Area (t · ha−1)	Carbon Stored (t)	Carbon Stored in Percentage
Non-photosynthetic areas	248.37	0.00	0.00	0.00%
Low canopy cover	880.73	6.90	5525.04	13.14%
High canopy cover	1706.90	21.40	36,527.66	86.86%
Total	2836.00		42,052.70	100.00%

summarizes the findings.

The non-photosynthetic areas amount to 248.37 ha. According to the NDVI (values from −0.553 to −0.226), these areas are devoid of vegetation, then the quantity of carbon stored is equal to 0. The low canopy cover of 880.73 ha has a potential store capacity of about 5525 t, while the high canopy cover (1706.90 ha), has a potential store capacity of about 36,528 t. The high canopy cover absorbs 6.6 times (86.86%) carbon more than the low canopy cover (13.14%). Overall, the potential amount of carbon storage is 42,052.7 t.

4.2. Mitigating the Heat Island Effect Through Vegetation

We identified the forest areas with NDVI ≥ 0.429, which amounted to 19.21 km² (A_green). Then, we divided this value for the surface occupied by green areas included in the homogeneous territorial areas (28.36 km², A_zon; Table A1) and for the peri-urban and urban context (129,78 km², A_pu). We found HIMI = 67.73% for the homogeneous territorial areas and 14.80% for the peri-urban and urban context.

5. Discussion

As for ES1, the contribution provided by the high canopy cover (86.86%) is clearly higher than that provided by the low canopy cover (13.14%). This is mainly due to two reasons: the notable variety of tree species, which better assimilate atmospheric carbon dioxide and the surface area, which is almost double (1706.90 ha) if compared to the low canopy cover (880.73 ha). As for ES2, the maximum efficiency (HIMI = 100%) cannot be achieved because some areas, which are delimited as homogeneous territorial zones including a certain type of greenery, have little or no vegetation cover. The findings are comparable to those of Serra et al. [67], who found lower values of HIMI for the metropolitan city (MCC) of Cagliari (Italy). In this regard, the municipality of Sassari shows better performance than the MCC of Cagliari in terms of potential mitigation of heat island effect and this is relevant to the safeguard of people’s health [77]. The findings are strongly rooted in the NDVI, as in previous research [39,67]. However, Yao et al. pointed out that NDVI is susceptible to precipitation, temperature, and other factors [38]. Thus, although it allows us to obtain findings potentially comparable with other international research, we have to stress this limitation.

As a response to the research question, the methodology adopted in this study allowed us to quantify two ESs by considering free available data. The ESs are potentially relevant to the population living in the municipality of Sassari, if we consider that its future climate scenarios stressed an increase in average temperature, temperature extremes, and periods with high temperatures [61]. Thus, the green cover plays a pivotal role in both mitigation (greenhouse gas sequestration) and adaptation to climate change (mitigating the heat island effect). In this regard, the methodological approach is consistent with the objectives of the Sustainable Energy and Climate Action Plan in terms of making cities more climate-resilient [37].

This exercise has relevance in terms of landscape and spatial planning. The ESs we assessed are usable by local administrations (i.e., municipalities) that can count on limited financial resources to acquire data and specialists. In fact, the method proposed in this study relies on a few data available to everyone and open-source software (QGIS v. 3.32.3 Lima), which is well-documented (manuals, tutorials, videos, etc.; e.g., [78,79]). Practitioners who would like to assess the performance of the local greenery to face the effects of a changing climate may be interested in the methodological approach we proposed, as it is applicable in practice. Furthermore, we found that the municipality of Sassari lacks a green urban plan (GUP), which is necessary to protect and enhance the existing green heritage and to plan its increase [80]. The GUP is strategic for urban transformation, and is considered as a complementary tool in the context of urban planning [81,82]. However, in Italy, the GUP is not compulsory [80]. Sassari lacks an updated green census (i.e., a systematic and detailed census of the tree and shrub heritage available in private and urban green spaces), since it dates to the early 2000s. This is a critical issue as it is the fundamental tool for (i) planning new green spaces, (ii) maintenance of greening, (iii) re-developing green areas, and (iv) estimating the economic investments necessary for the maintenance and enhancement of the functionality of the green heritage [81].

6. Conclusions

In this study, we proposed and applied a methodological approach that may be adopted by Italian as well as Mediterranean municipalities to integrate the quantitative assessment of ESs in local planning tools. The purpose was to provide the municipalities with support for a rough assessment of ecosystem services (ESs) through freely available data and software. We focused on two ESs, which were assessed for the municipality of Sassari (Italy): (i) greenhouse gas sequestration by terrestrial ecosystems; and (ii) mitigating the heat island effect through vegetation. We considered the green areas belonging to both urban and peri-urban context, and the green areas included in specific zones as set by the zoning. We found that the potential amount of carbon storage is about 42,052.7 t and that the Heat Island Effect Mitigation Index (HIMI) is significantly higher in the homogeneous territorial areas than in the urban and peri-urban context.

We did not come up with innovative methods for quantifying ESs. The novelty of the study lies in the applied practical approach, which is implementable by public bodies lacking data and resources, to assess prima facie the need for climate adaptation and mitigation strategies. In other words, the approach we adopted to assess the greenhouse gas sequestration by terrestrial ecosystems through the National Inventory of Forests and Forest Carbon Reservoirs is one of the first attempts to link NDVI with specific forest categories and carbon stored per unit area in the context of Italian municipalities. Furthermore, HIMI has been applied for the first time in another study concerning a metropolitan city. Thus, we have proved that the method is replicable in other contexts.

This study has limitations. First, we had limited data and could not assess the ESs in further detail. The amount of carbon sequestration needs to be considered a rough value: we did not know the details, such as the age and health of the trees, the species involved, the volumes of the canopies, etc. Furthermore, we are not aware of the effectiveness of the green cover against the UHI effect, i.e., we did not quantify the benefits of greenery in terms of temperature reduction (absolute values) and effects on perceived temperature (e.g., the effect of shadow). Second, we used data with different resolutions/scales, and this may be relevant to uncertainty (e.g., by leading the planners into potential misinterpretations). In this regard, an updated large-scale green census and field surveys are the minimum requirements to plan reasonable measures aimed at improving the functionality of the green spaces and designing multi-functional green infrastructure. Third, we did not investigate potential ecosystem disservices resulting from an increase in green spaces. This is essential to design adequate adaptation and mitigation solutions compatible with human needs. Four, the method adopted for calculating the HIMI does not provide spatially explicit results, limiting its value for practical planning and decision-making. Future research should address and overcome these limitations.