Prerpints.org logo
Preprint
Article

Assessment of the Potential Contribution of the Urban Green System to the Carbon Balance of Cities

Altmetrics

Downloads

112

Views

23

Comments

0

A peer-reviewed article of this preprint also exists.

supplementary.xlsx (62.59KB )

Submitted:

28 March 2024

Posted:

29 March 2024

You are already at the latest version

Alerts
Abstract
Reducing GHG emissions is a crucial challenge in urban areas, characterized by high energy consumption and reduced exposure to nature. In this context, the urban green system could play a pivotal role. In the literature, scholars analyzed both the ability of species-specific and layout-specific green infrastructures to increase carbon sequestration and the best location sites of new green infrastructures, to increase the provision of overall ecosystem services. There is a lack of studies helping green urban planners and designers choose where and which green infrastructure to implement, based on vegetation species-specific performance and local carbon emissions of city components. This paper uses tree inventory data from a medium-sized city in central Italy (Perugia) to develop a spatial analysis of the urban parks' performance in carbon sequestration. Then, the method evaluates the carbon emission of a public city building to generate a spatialized balance between buildings’ demand and trees’ supply, to support local decisions about the best locations for new green infrastructures and the choice between species. The paper contributes to GIS-based tools that vary the recommended location sites and species for new green infrastructures based on the demanded ecosystem service.
Keywords: 
Subject: Biology and Life Sciences  -   Forestry

1. Introduction

The European Green Deal, converted into the European Climate Law [1], aims to achieve climate neutrality by 2050, developing multiple and multi-sectorial actions. Actions dedicated to cities represent a critical challenge because urban areas require an uninterrupted energy supply. In this context, the valorization of the urban green system for climate mitigation and adaptation through carbon sequestration and storage is becoming increasingly relevant [2].
The urban green system is a complex collection of urban areas covered by vegetation having high variability of functions, dimensions, and characteristics, such as parks, forests, community gardens, representative green spaces, street trees, green roofs and walls, and service and marginal green areas [3]. Scholars studied the carbon sequestration potentiality of different types of urban green areas. Kong et al. [4] studied urban turfgrass in Hong Kong and Shenzhen, showing they could represent carbon sinks by adopting efficient management strategies. McPherson et al. [5,6] studied street trees in Los Angeles, comparing their carbon balance in scenarios with different pruning strategies and types of equipment and vehicles for their management. Park and Jo [7] estimated the carbon balance of Korean urban parks over their life cycle. Among these types of areas, urban parks and urban forests are crucial for implementing carbon sequestration strategies thanks to their high number of trees [8,9].
Urban parks offer greenery that aids in carbon storage and sequestration, along with providing citizens with numerous ecosystem benefits such as the mitigation of urban heat islands [10], the improvement of air quality through pollutant absorption and deposition [11,12], the enhancement of flood protection via increased soil permeability [13], and the promotion of human well-being [14]. Quantifying the capacity of the urban green system to impact the city-level carbon balance and offset anthropogenic emissions is a complex issue. Furthermore, this estimation is strongly influenced by the type of data collected and the methodological approaches used for collecting them [2]. Concerning the data about urban greenery, at the international level, there is no standard for realizing urban tree databases, but generally, the attributes are localization, genus, species, Diameter at Breast Height (DBH), height, crown dimension, and health status [15]. The fieldwork to collect them requires much effort, and public administrations struggle to complete their tree database [16]. Overall, these databases that many Public Administrations are implementing represent valuable input data to estimate the provision of ecosystem services, including carbon storage and sequestration, and the i-Tree Eco software is an international standard for achieving this goal. This software belongs to i-Tree, a suite of freely available open software tools designed and certified by the United States Forest Service [17]. i-Tree Eco uses data about trees to quantify their effects on the environment, the forest structure, and their value to the community. Concerning the estimation of carbon sequestration, this software uses the yearly diameter increase of every tree according to a computerized model based on the growth rate, which considers the expected annual growth of the tree (adjusted for the local growing season), the media growth rate by species, the competition with other trees, and the percentage of crown dieback [17]. Results depend on the quantity and quality of the input data, the methodological approach for their collection, and their level of detail [2,18].
Besides the challenge of reliable estimation of carbon sequestration in an urban park, carbon flux dynamics also have a complex structure [19]. Indeed, urban parks are social-ecological systems with flexible feedback, and interactions with internal and external variables [20,21]. Therefore, carbon flux dynamics are composed of vegetation, which sequesters and stores carbon [8,22], and people doing actions such as design and build, greenery management, and the uses of equipment and structures, which release carbon [7,23]. Developing a new urban park releases carbon due to its realization and the subsequent maintenance of vegetation and connected services [24,25,26]. Some authors have used LCAs to evaluate different urban green typologies in the last decade. Nicese et al. [27] assessed the carbon balance connected with planning, planting, and maintaining an urban park in Milan, Italy. Zhang et al. [23] studied the carbon sequestration and emissions of four urban parks in China over 50 years.
Overall, the valorization of urban parks to achieve carbon neutrality in cities is a complex challenge without a valid solution internationally. Therefore, further studies are needed to develop multi-level scenarios and geo-specific and species-specific evaluations. This paper develops a yearly carbon balance of an urban park of a medium-sized European city (Perugia, Italy) to:
  • evaluate the carbon flux dynamic of a significant urban park of the city for the year 2023;
  • suggest species-specific and geo-specific solutions to move toward carbon neutrality;
  • upscale the carbon balance with different multi-level afforestation scenarios.

2. Materials and Methods

2.1. Study Area

The Municipality of Perugia is in central Italy (Umbria region). It covers 449.88 km2 and has 178283 inhabitants [28]. Figure 1, letter a, shows the land uses of the study area using the codes of the pan-European inventory Corine Land Cover [29]. The surface is covered 55% by agricultural areas, 31% by forest and semi-natural areas, 13% by artificial surfaces, and 1% by water bodies. The whole municipality has a population density of 396 inhabitants per km2. 63% of its population lives in the urban area of Perugia, which covers 41 km2 and has 2731 inhabitants per km2, reaching 7699 inhabitants per km2 in the most populated neighborhoods (Figure 1, lett. b). Perugia has an altitude between 300 and 500 meters and a transitional (temperate/mediterranean) climate [30]. Air temperature is relatively mild, with the warmest month (August) ranging between 13.9 (Min) and 37.2◦C (Max) and the coldest month (January) between -6.11 (Min) and 13.9 ◦C (Max) [31,32]. Mean annual precipitation equals 961.4 mm; two pronounced maximums characterize the precipitation pattern during May (154 mm) and November (235 mm). The yearly amount of photosynthetic radiation is 684810.5 W/m2, and the maximum hourly radiation is in June and is 436 W/m2.
The park chosen as the study area (the green point of Figure 1) is the city's botanical garden, covering 3 hectares. Created in 1962, this park is interesting in developing species-specific evaluations because it hosts young and mature trees and has the highest biodiversity among the parks in the city's urban green system. Concurrently, it hosts the tree species most represented in the urban areas of Perugia [33]. Carbon sinks of this study area are 362 trees belonging to 142 species organized in three sections: gymnosperms, angiosperms, and angiosperms-arboretum (Figure 2). While a greenhouse, a service building, and the garden machines used for maintenance constitute carbon sources.

2.2. Steps of the Method

The method consists of the evaluation of the yearly carbon flux dynamic of the studied park (steps 1-5), the ranking of species based on their performance in carbon sequestration (step 6), and the building of multilevel scenarios to balance the supply and demand of carbon dioxide (step 7). The steps are:
  • TREES INVENTORY OF THE STUDY AREA. The tree inventory was realized through a single operator's fieldwork in November and December 2023. The operator is an expert in species recognition and tree health status evaluation. A device that combines laser, ultrasound, and tilt sensors to provide accurate and reliable distance, height, and angle measurements (distance accuracy of 4 cm; resolution height 0.1 m; resolution and typical accuracy of angles 0.1°) [34] was used for the assessment. The tree inventory reports only trees higher than 1.8 meters. The inventory developed for this paper has a row for every tree of the park and columns to describe their parameters. The columns regard localization, species, DBH at 1.3 meters, height, crown dimension (height and width), crown light exposure, and percentages of crown dieback and missing;
  • YEARLY CARBON SEQUESTRATION. We used the i-Tree Eco software to calculate the annual carbon sequestrated by trees in the study area. The mandatory data for the software are only tree species and DBH, but all the data inventoried in the previous step are highly recommended for improving the model estimations. Besides data regarding trees, i-Tree Eco requires microclimatic information for a year (hourly temperature, hourly precipitation, hourly concentration of pollutants in the air) as input data for the study area. These data are generally free downloadable by regional datasets [31,32,35] and are used by the software to develop geo-specific estimations of trees' performance. The ecosystem services estimated by i-Tree Eco are air pollution removal, hydrology effects, and carbon storage and sequestration;
  • YEARLY CONSUMPTION TRENDS IN THE STUDY AREA. We inventoried all the activities of the study area that release carbon for their functioning. The park has a service room with gardeners' lockers and office staff, and we collected the yearly electricity (kWh) and natural gas (m3) consumption for its lighting and heating. Furthermore, the park has a greenhouse that hosts tropical-subtropical species and succulent xerophytes, and we collected the consumption of liquefied petroleum gas (liters) for its functioning. Finally, we monitored the garden machines' yearly diesel (liters) consumption and the garden tools used to maintain the park. Electricity emits carbon dioxide (CO2) during its production process, varying based on the method used to generate it, and natural gas, liquefied petroleum gas, and diesel, when burned, releases carbon dioxide (CO2) into the atmosphere as a byproduct of combustion;
  • CONVERSION OF THE CONSUMPTION IN KG OF CARBON DIOXIDE EQUIVALENT. In Italy, the country of the case study, the Italian Institute for Environmental Protection and Research [36] annually publishes a report containing emission factors. These factors are crucial for converting consumption measurements into kilograms of carbon dioxide equivalent. Emission factor databases play a vital role in this conversion process, transforming the units that calculate consumption factors into carbon dioxide equivalent. Emission factors vary between countries depending on the energy sources and technology used for production, energy infrastructure, fuel mix, and regulatory standards, as the International Energy Agency highlighted [37];
  • EVALUATION OF THE CARBON COMPENSATION LEVEL IN THE URBAN PARK. To assess the park's carbon compensation level, we compared the supply, represented by the yearly kilograms of CO2 sequestered by the park's trees, and the demand, represented by the yearly kilograms of CO2 equivalent produced for the park's management. This evaluation aims to determine the extent of annual compensation achieved within the urban park and ascertain whether the study area acts as a carbon sink or source;
  • SPECIE-SPECIFIC SOLUTIONS TO IMPROVE THE CARBON SEQUESTRATION POTENTIALITY. We ranked the species of the park based on their performance in carbon sequestration to define a dataset and suggest the optimal species in this regard. We used the Jenks Natural Breaks Classification to determine the classes of performance. This method is used to minimize within-group variance and maximize between-group variance. The Jenks optimization algorithm works by iteratively testing different potential class breaks to find the arrangement of breaks that produces the lowest total deviation from the class means, resulting in internally homogeneous and externally heterogeneous classes [38]. It is commonly applied in geographic information systems and spatial analysis to symbolize and analyze continuous data [39,40]
  • TOWARD CARBON NEUTRALITY. We used the resulting tree with the best performance in carbon sequestration to define the physical characteristics (canopy cover, leaf area, and biomass) and value of performance (yearly kilograms of carbon dioxide sequestered) of an Ideal Tree, called I-Tree_CS. Then, we used this tree to simulate three multi-level scenarios at different geographical scales to improve the carbon balance.

3. Results

The park has 362 trees with a height of over 1.8 meters. Table S1 reports the resulting dataset. The information of the dataset regards trees' structure (DBH, Height, crown height and width, canopy cover, tree condition, leaf area, leaf biomass, LAI, basal area, stratum) and their performance in carbon sequestration (gr m-2 of carbon uptake by canopy cover, kg yr-1 of carbon sequestration, percentage of the total carbon sequestrated by the park; class of performance). Rows 366-375 of Table S1 report the main statistics. Overall, the park's trees have a canopy cover of 10078 m2, a leaf biomass of 6551 kg, and a value of Leaf Area Index of 6.4. In 2023, they sequestered 3762 kilograms of carbon dioxide, and the individual trees' performance ranged from 0.1 to 76.3 kg yr-1. Figure 3 reports the trees' classification resulting from the Jenks optimization algorithm. In the figure, the "null" class corresponds to trees that sequestrated less than 5.3 kg yr-1 of carbon dioxide; "Very poor" between 5.3 and 11.8 Kg yr-1; "Poor" between 11.8 and 19.4 Kg yr-1; "Acceptable" between 19.4 and 27.6 Kg yr-1; "Good" between 27.6 and 48.5 Kg yr-1; and "Very good" greater equal to 48.5 Kg yr-1.
Table 1 shows that 47.51% of trees belong to the class of performance equal to "null," 17.40 % are classified "very poor," 14.92 % "poor," 13.54 % "acceptable," 5.80% "good," and 0.83% "very good."
The tree with the best performance, used to define the characteristic of the I-Tree_CS, is a Populus nigra L. and reaches 76.3 kg yr-1in carbon sequestration. Still, this value does not represent the inventoried trees because they have a high variability of species and dimensions. Table 1 shows that a tree in the study park sequesters 6.10 kg yr-1 of carbon dioxide (median), with values of first and third quartiles equal to 2.10-16.68. In urban contexts, a single species of tree could have high variability in its performance linked to the space available for the growth and the type and intensity of pruning. Indeed, other trees of the park of Populus Nigra L. (Table S1) belong to the carbon sequestration classes of performance "null," "poor," "acceptable," and "very good" (their DBH range between 10.5 to 86.3 cm, and their canopy cover range between 9.1 and 132.7 m2.) The other species in the class "very good" are Populus canadensis Moench and Eucalyptus camaldulensis Dehnh. In contrast, angiosperms belonging to the class "good" are Quercus ilex L., Populus canadensis Moench, Olea europaea L., Quercus cerris L., Ulmus pumila L., Platanus orientalis L., Liriodendron tulipifera L., and gymnosperms are Cedrus deodara (Roxb.) G.Don and Pinus pinea L.
The year used to calculate the energy consumption for the park's management is 2023 (Table 2). The study park has a vast greenhouse (675 m2) that hosts tropical-subtropical species and succulent xerophytes during the whole year, and a building of 73 m2 with lockers and a service room. Furthermore, the park's management (pruning and lawn mowing) needs numerous energy-intensive garden machines. Overall, the yearly carbon dioxide equivalent is 42774.50 Kg CO2e. Comparing carbon emission and sequestration, the results show that currently, the percentage offset is 9 %.
In the last step of the method, we organized three different scenarios, increasing the surface used to improve the offset and using the characteristics of the I-Tree_CS to calculate the carbon sequestration potentiality. Scenario 0 is the current composition of the park, having an offset of 9%. Scenario 1 maintains the surface of scenario 0 and evaluates the number of I-Tree_CS that could be put in place; scenario 2 keeps the same number of trees of scenario 0 and evaluates the necessary surface with all I-Tree_CS; scenario 3 converts the overall municipality's areas classified mixed forest, sparsely vegetated areas, and green urban areas (Figure 1, lett. a), in green spaces with I-Tree_CSs and evaluates the overall po-tentiality of carbon sequestration. Table 3 shows the three scenarios and their percentage offset, except for scenario 3, involving the whole city. Indeed, scenario 3 estimates the municipality's carbon sequestration potentiality, which should be compared with the city's overall carbon emissions.
The scenario analysis reveals that substituting the existing arboreal mix with optimal trees in the study area would decrease the tree count by 52% to ensure a suitable growth space, with a resultant offset of 31%. Conversely, maintaining the current tree count with all trees possessing I-Tree_CS traits would necessitate doubling the area, yielding a 64% offset. Additionally, the third scenario shows that covering all the suitable areas in the municipality with optimal trees would sequester 7497 tons of carbon annually.

4. Discussion

4.1. Urban Parks: Sinks or Sources of Carbon?

Currently, the study park is a carbon source, not a sink. Indeed, the park's trees sequester 9% of the carbon emitted for its functioning. The high carbon emissions related to the study park maintenance confirm the need to decarbonize the emission sectors substantially [2]. The case study hosts a greenhouse designed to generate different climate conditions for not native plants, following the main aim of the botanical garden, which consists of preserving and showing the worldwide biodiversity. Considering that the city's other urban parks do not have greenhouses, we have also evaluated the carbon balance without the carbon emission linked to the greenhouse. In this case, the park studied reaches an offset of 84%. Therefore, the study park remains a carbon source due to its high maintenance cost. This result confirms the need to reduce maintenance operations generally carried out in urban green areas [4,5,6]. Many strategies are implemented in this regard. For example, Teixeira et al. [41] have applied an adaptive planting design and management for urban climate change adaptation and mitigation in the city of Porto (Portugal). Management strategies to change this balance include different pruning or thinning intensities [27], decreasing mulch application rates and expanding tree canopy extent, permitting spontaneously growing species by a reduced management intensity [42] and management frequency [21].
The main weakness of the method developed to evaluate the urban park's carbon balance is to gate the study to a single year. Other scholars used a life cycle assessment approach, considering the emissions for planting and managing vegetal residues, pruning, lawn mowing, and the carbon stored inside them during their lives [7,27]. Nicese et al. [27], studying a well-managed park in northern Italy, showed a carbon sequestration capacity ten times higher than the carbon emission over 50 years. A further limitation of the method is that it focuses on the carbon sequestrated by the trees higher than 1.8 meters in the study area, neglecting the contribution of little trees, shrubs [15], and lawns [4]. Despite these weaknesses, this paper contributes to develop methods for using data-informed and nature-based patterns, that Convertino [43] highlights as essential for achieving optimal strategic decisions. Future development of our work will apply a life cycle assessment approach to this park and other city parks, considering all the vegetational components, defining the optimal planting, maintaining, and residues converting strategies, to refurbish the network of the city's urban parks and transform them into valuable sinks of carbon.

4.2. Carbon Balance Requires a Multi-Level Approach

Currently, the park's carbon offset is 9%. This percentage rises to 31% using ideal trees (trees having optimal performance in carbon sequestration) and to 64% by maintaining the same number of trees but doubling the surface to ensure adequate growth. The park studied, using ideal trees and without the greenhouse, passes from carbon source to carbon sink and reaches an offset of 297%.
These results confirm previous findings highlighting that the urban green system, when not a source of carbon due to its high maintenance costs [4], contributes marginally to the carbon balance of cities. Brilli et al. [2] evaluated the current offset by urban forests to achieve carbon neutrality of an Italian town (Prato) at 7.1%, reaching up to 11% using an afforestation scenario. Teo et al. (2021) observed that 17.6 % of worldwide city areas suitable for afforestation could offset about 1 % of city emissions. Doorga et al. [44] developed a scenario to move toward Mauritius Island's carbon neutrality by converting all inhabited lands into forests.
A multi-level approach follows the regional science approaches that address the issue of local interactions and consider the concepts of space and distance as essential in landscape planning and design processes [45,46]. Geographical proximity, defined as the physical distance between two entities, weighted by the cost in time and money of covering that distance, can reinforce interactions between actors and generate positive externalities [47].
However, activating the benefits of this proximity depends on the studied benefit, because the urban green system provides both proximity and territorial services. That is, some ecosystem services need proximity to the inhabitants to be best provided, such as neighborhood greenery for daily inhabitants' walks [47] or proximity to the emission sources, such as the green areas planned to absorb PM10 and PM5 [3]; while other services are territorial and do not need proximity to be provided. This is the case of the carbon balance, so it is possible to evaluate its compensation, also varying the scale of the investigation.
Scenario 3 of this paper varies the level of study and develops a hypothesis involving the whole municipality. It uses the ideal tree' characteristics to evaluate the optimal performance obtainable by mixed forests, sparsely vegetated areas, and green urban areas. Table 3 shows this scenario offers 7496910 kg yr-1 of carbon sequestration potential. Comparing the 21% of the carbon sequestered with this scenario (1574351 Kg yr-1) with the consumption of Prato (465800 Kg CO2e yr-1) [2], an Italian city with a surface equal to 21% of Perugia and a population equal to 110% [28], we obtain an offset of 338%. This result seems to be promising. A further development will calculate the carbon emission of the city of Perugia and its hamlets. It is crucial to highlight that scenario 3 represents an ideal solution limited to carbon sequestration. For this reason, it represents only a hypothetical scenario to offer a value of maximum potentiality. Indeed, many trees and shrubs that do not perform well in this sense provide numerous other ecosystem services and increase the biodiversity of territories [20,48,49].

4.3. Species-Specific Suggestions to Move toward Carbon Neutrality

Our results show high variability in carbon uptake by tree canopy cover (median 434.29, interquartile range from 226.73 to 913.15 g m-2, Table S1), confirming that an adequate species selection can influence the carbon balance [27]. Findings of previous studies in other areas report values included inside our interquartile range, such as 600±200 g m-2 in a study in Boston [50] and 283 g m-2 in a study in Prato [2]. The high variability in carbon sequestration of trees in the case study is due to the variety of species (142 species) and the different trees' dimensions.
Regarding the specie having the best results in carbon sequestration (Populus nigra L.), the study park hosts trees that belong to classes of performance equal to "null," "poor," "acceptable," and "very good" because they have had different available spaces to grow. Table S1 shows that their DBHs range between 10.5 to 86.3 cm, and their canopy covers between 9.1 and 132.7 m2. This result confirms that, to optimize ecosystem services provided by the urban green system, besides considering species selection, there is a need to consider the physical needs of every species in their different growth phases and respect them [20].
In our study, the other species with optimal carbon sequestration performance are Populus canadensis Moench and Eucalyptus camaldulensis Dehnh. Zhang et al. [23], in a different geographical context (China), suggest Populus tomentosa Carrière, Fraxinus chinensis Roxb, and Lonicera maackii (Rupr.) Maxim. Al-Nadabi and Sulaiman [51], in the Sultanate of Oman, suggest Ficus spp., followed by Azadirachta indicia, Conocarpus erectus, and Tabebuia rosea. The geographical contexts and the existent species in the case studies influence the different findings.
Instead, it is significant to develop a reflection about the three best-performing species of our paper (Populus nigra, Populus x canadensis, and Eucalyptus camaldulensis), that well exemplify the need for a broader evaluation of the species to select them in the design phase. First, ecological and morpho-physiological factors are to be considered. Indeed, poplars are scarcely drought-resistant plants from humid environments, with fast growth but a short lifecycle and weak and brittle wood [52,53]. Furthermore, the seed hairs can cause irritations in the respiratory tract [54]. Populus x canadensis and Eucalyptus are exotic plants naturalized in many regions of Italy, manifesting invasive behavior in the most favorable climates [52]. The choice should result from a cost-benefits combination of these aspects: the type and quantities of ecosystem services provided, the species' demands, the lifecycle duration (compared to the growth rate), and the interaction with the human environment and the local flora.

5. Conclusions

Urban parks provide differentiated ecosystem services to citizens living in urban areas, and their sustainable development and management should be encouraged and stimulated. However, from the carbon emission mitigation perspective, this study's results indicate that to achieve carbon neutrality, cities should not limit their planning effort to urban areas but must implement green infrastructures in the whole municipality effectively. Our results indicate that, from a carbon neutrality perspective, implementing actions to reduce carbon dioxide emissions should be considered the priority, in parallel to valorizing the trees' performance in natural and rural areas. The key points to guide plans and designs of urban green systems should focus on choices to ensure that they represent a carbon sink, not a source of it. This only results from a process evaluating adequate species with high performance in carbon sequestration and at the same time low maintenance requirements, leaving to trees adequate spaces for their healthy growth, and optimizing other ecosystem services that need proximity to inhabitants.

Supplementary Materials

The following supporting information can be downloaded at the website of this paper posted on Preprints.org. Table S1: dataset of the tree inventory of the study park.

Author Contributions

Conceptualization, D.G. and M.M.; methodology, M.M.; software, M.M.; validation, D.G, L.B. and M.M; formal analysis, M.M. and D.G.; investigation, L.B.; resources, L.B.; data curation, L.B. and M.M.; writing—original draft preparation, M.M.; writing—review and editing, D.G. and L.B.; visualization, M.M.; supervision, D.G.; project administration, D.G.; funding acquisition, M.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research was co-funded by the European next generation program and the Italian Research Ministry. Project code P20229ZC79; project name: Urban green systems: integrated modelling of environmental, energy and microclimatic benefits and GHG offsetting for strengthening resilience and adaptation to climate change – GRACE (Green foR climAte resilience).

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. European parliament. Regulation (EU) 2021/1119 of the European Parliament and of the Council of 30 June 2021 establishing the framework for achieving climate neutrality and amending Regulations (EC) No 401/2009 and (EU) 2018/1999 (‘European Climate Law’), Official Journal of the European Union, 243, 9.7.2021. Available online: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=uriserv%3AOJ.L_.2021.243.01.0001.01.ENG&toc=OJ%3AL%3A2021%3A243%3AFULL (accessed on 26 03 2024).
  2. Brilli, L.; Carotenuto, F.; Chiesi, M.; Fiorillo, E.; Genesio, L.; Magno, R.; Morabito, M.; Nardino, M.; Zaldei, A.; Gioli, B. An Integrated Approach to Estimate How Much Urban Afforestation Can Contribute to Move towards Carbon Neutrality. Science of The Total Environment 2022, 842, 156843. [Google Scholar] [CrossRef] [PubMed]
  3. Menconi, M.; Abbate, R.; Simone, L.; Grohmann, D. Urban Green System Planning Insights for a Spatialized Balance between PM10 Dust Retention Capacity of Trees and Urban Vehicular PM10 Emissions. Sustainability 2023, 15, 5888. [Google Scholar] [CrossRef]
  4. Kong, L.; Shi, Z.; Chu, L.M. Carbon Emission and Sequestration of Urban Turfgrass Systems in Hong Kong. Science of The Total Environment 2014, 473–474, 132–138. [Google Scholar] [CrossRef]
  5. McPherson, E.G.; Kendall, A. A Life Cycle Carbon Dioxide Inventory of the Million Trees Los Angeles Program. Int J Life Cycle Assess 2014, 19, 1653–1665. [Google Scholar] [CrossRef]
  6. McPherson, E.G.; Kendall, A.; Albers, S. Life Cycle Assessment of Carbon Dioxide for Different Arboricultural Practices in Los Angeles, CA. Urban Forestry & Urban Greening 2015, 14, 388–397. [Google Scholar] [CrossRef]
  7. Park, H.M.; Jo, H.K. Ecological Design and Construction Strategies through Life Cycle Assessment of Carbon Budget for Urban Parks in Korea. Forests 2021, 12, 1399. [Google Scholar] [CrossRef]
  8. Shadman, S.; Ahanaf Khalid, P.; Hanafiah, M.M.; Koyande, A.K.; Islam, Md.A.; Bhuiyan, S.A.; Sin Woon, K.; Show, P.-L. The Carbon Sequestration Potential of Urban Public Parks of Densely Populated Cities to Improve Environmental Sustainability. Sustainable Energy Technologies and Assessments 2022, 52, 102064. [Google Scholar] [CrossRef]
  9. Singkran, N. Carbon Sink Capacity of Public Parks and Carbon Sequestration Efficiency Improvements in a Dense Urban Landscape. Environ Monit Assess 2022, 194, 750. [Google Scholar] [CrossRef] [PubMed]
  10. Grover, A.; Singh, R. Analysis of Urban Heat Island (UHI) in Relation to Normalized Difference Vegetation Index (NDVI): A Comparative Study of Delhi and Mumbai. Environments 2015, 2, 125–138. [Google Scholar] [CrossRef]
  11. Zheng, Y.; Chen, L. Modeling the Effect of Green Roofs for Building Energy Savings and Air Pollution Reduction in Shanghai. Sustainability 2024, 16, 286. [Google Scholar] [CrossRef]
  12. Picone, N.; Esposito, A.; Emmanuel, R.; Buccolieri, R. Potential Impacts of Green Infrastructure on NOx and PM10 in Different Local Climate Zones of Brindisi, Italy. Sustainability 2024, 16, 229. [Google Scholar] [CrossRef]
  13. Jiang, Y.; Yuan, Y.; Piza, H. A Review of Applicability and Effectiveness of Low Impact Development/Green Infrastructure Practices in Arid/Semi-Arid United States. Environments 2015, 2, 221–249. [Google Scholar] [CrossRef]
  14. Kim, G.; Miller, P.A. The Impact of Green Infrastructure on Human Health and Well-Being: The Example of the Huckleberry Trail and the Heritage Community Park and Natural Area in Blacksburg, Virginia. Sustainable Cities and Society 2019, 48, 101562. [Google Scholar] [CrossRef]
  15. Rossi, L.; Menconi, M.E.; Grohmann, D.; Brunori, A.; Nowak, D.J. Urban Planning Insights from Tree Inventories and Their Regulating Ecosystem Services Assessment. Sustainability 2022, 14, 1684. [Google Scholar] [CrossRef]
  16. Esperon-Rodriguez, M.; Quintans, D.; Rymer, P.D. Urban Tree Inventories as a Tool to Assess Tree Growth and Failure: The Case for Australian Cities. Landscape and Urban Planning 2023, 233, 104705. [Google Scholar] [CrossRef]
  17. 17. Nowak, D.J. Understanding i-Tree: 2021 Summary of Programs 638 and Methods. Forest Service U.S. Department of Agriculture, Northern Research Station, General Technical Report NRS-200-2021.
  18. Teo, H.C.; Zeng, Y.; Sarira, T.V.; Fung, T.K.; Zheng, Q.; Song, X.P.; Chong, K.Y.; Koh, L.P. Global Urban Reforestation Can Be an Important Natural Climate Solution. Environ. Res. Lett. 2021, 16, 034059. [Google Scholar] [CrossRef]
  19. Moon, T.; Kim, M.; Chon, J. Adaptive Green Space Management Strategies for Sustainable Carbon Sink Parks. Urban Forestry & Urban Greening 2024, 94, 128236. [Google Scholar] [CrossRef]
  20. Menconi, M.E.; Palazzoni, L.; Grohmann, D. Core Themes for an Urban Green Systems Thinker: A Review of Complexity Management in Provisioning Cultural Ecosystem Services. Urban Forestry & Urban Greening 2021, 65, 127355. [Google Scholar] [CrossRef]
  21. Hwang, Y.H.; See, S.C.; Patil, M.A. Short-Term Vegetation Changes in Tropical Urban Parks: Patterns and Design-Management Implications. Urban Forestry & Urban Greening 2021, 64, 127240. [Google Scholar] [CrossRef]
  22. Lin, J.; Ma, Q.; Ju, Y.; Zhang, H.; Wang, Q.; Huang, B. Relationships between Urbanization, Tree Morphology, and Carbon Density: An Integration of Remote Sensing, Allometric Models, and Field Survey. Urban Forestry & Urban Greening 2022, 76, 127725. [Google Scholar] [CrossRef]
  23. Zhang, Y.; Meng, W.; Yun, H.; Xu, W.; Hu, B.; He, M.; Mo, X.; Zhang, L. Is Urban Green Space a Carbon Sink or Source? - A Case Study of China Based on LCA Method. Environmental Impact Assessment Review 2022, 94, 106766. [Google Scholar] [CrossRef]
  24. Lin, X.; Ren, J.; Xu, J.; Zheng, T.; Cheng, W.; Qiao, J.; Huang, J.; Li, G. Prediction of Life Cycle Carbon Emissions of Sponge City Projects: A Case Study in Shanghai, China. Sustainability 2018, 10, 3978. [Google Scholar] [CrossRef]
  25. Kabisch, N.; Strohbach, M.; Haase, D.; Kronenberg, J. Urban Green Space Availability in European Cities. Ecological Indicators 2016, 70, 586–596. [Google Scholar] [CrossRef]
  26. Strohbach, M.W.; Arnold, E.; Haase, D. The Carbon Footprint of Urban Green Space—A Life Cycle Approach. Landscape and Urban Planning 2012, 104, 220–229. [Google Scholar] [CrossRef]
  27. Nicese, F.P.; Colangelo, G.; Comolli, R.; Azzini, L.; Lucchetti, S.; Marziliano, P.A.; Sanesi, G. Estimating CO2 Balance through the Life Cycle Assessment Prism: A Case – Study in an Urban Park. Urban Forestry & Urban Greening 2021, 57, 126869. [Google Scholar] [CrossRef]
  28. Eurostat. Population distribution/demography. 2023. Available online: https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/population-distribution-demography/geostat (accessed on 26 03 2024).
  29. European Environmental Agency. Corine Lnad Cover. 2023. Available online: https://www.eea.europa.eu/en/datahub/datahubitem-view/a5144888-ee2a-4e5d-a7b0-2bbf21656348 (accessed on 26 03 2024).
  30. Capotorti, G.; Guida, D.; Siervo, V.; Smiraglia, D.; Blasi, C. Ecological Classification of Land and Conservation of Biodiversity at the National Level: The Case of Italy. Biological Conservation 2012, 147, 174–183. [Google Scholar] [CrossRef]
  31. Cerlini, P. B.; Silvestri, L.; Saraceni, M. Quality control and gap- filling methods applied to hourly temperature observations over central Italy. Meteorological Applications 2020, 27(3), e1913. [Google Scholar] [CrossRef]
  32. Silvestri, L.; Saraceni, M.; Cerlini, P.B. Quality management system and design of an integrated mesoscale meteorological network in Central Italy. Meteorological Applications 2022, 29(2), e2060. [Google Scholar] [CrossRef]
  33. Municipality of Perugia. Urban green asset strategy. Municipal council resolution of 30 March 2022. Available online: https://www.comune.perugia.it/pagine/deliberazioni-adottate-e-deliberazioni-approvate (accessed on 26 03 2024).
  34. Haglof Sweden. Available online: https://haglofsweden.com/project/laser-geo/ (accessed on 26 03 2024).
  35. Umbria Region. Regional Hydrographic Service. 2023. Available online: https://www.regione.umbria.it/ambiente/servizio-idrografico (accessed on 26 03 2024).
  36. ISPRA Efficiency and decarbonization indicators in Italy and in the biggest European Countries. 2023. Available online: https://www.isprambiente.gov.it/en/publications/reports/efficiency-and-decarbonization-indicators-in-italy-and-in-the-biggest-european-countries-2013-edition-2023 (accessed on 26 03 2024).
  37. International Energy Agency. Emission factor database. 2022. Available online: https://www.iea.org/data-and-statistics/data-product/emissions-factors-2022 (accessed on 26 03 2024).
  38. Jenks, G. F. The Data Model Concept in Statistical Mapping. International Yearbook of Cartography 1967, 7, 186–190. [Google Scholar]
  39. Zhao, W.; Li, X.; Zhang, L.; Peng, L.; Liu, Y.; Wang, Z.; Jiao, L.; Wang, H. The Multi-Scale Spatial Heterogeneity of Ecosystem Services’ Supply–Demand Matching and Its Influencing Factors on Urban Green Space in China. Forests 2023, 14, 2091. [Google Scholar] [CrossRef]
  40. Mallick, J.; Ibnatiq, A.A.; Kahla, N.B.; Alqadhi, S.; Singh, V.P.; Hoa, P.V.; Hang, H.T.; Hong, N.V.; Le, H.A. GIS-Based Decision Support System for Safe and Sustainable Building Construction Site in a Mountainous Region. Sustainability 2022, 14, 888. [Google Scholar] [CrossRef]
  41. Teixeira, C.P.; Fernandes, C.O.; Ahern, J. Adaptive Planting Design and Management Framework for Urban Climate Change Adaptation and Mitigation. Urban Forestry & Urban Greening 2022, 70, 127548. [Google Scholar] [CrossRef]
  42. Sikorski, P.; Gawryszewska, B.; Sikorska, D.; Chormański, J.; Schwerk, A.; Jojczyk, A.; Ciężkowski, W.; Archiciński, P.; Łepkowski, M.; Dymitryszyn, I.; et al. The Value of Doing Nothing – How Informal Green Spaces Can Provide Comparable Ecosystem Services to Cultivated Urban Parks. Ecosystem Services 2021, 50, 101339. [Google Scholar] [CrossRef]
  43. Convertino, M. Sensing Linked Cues for Ecosystem Risk and Decisions. Environments 2023, 10, 169. [Google Scholar] [CrossRef]
  44. Doorga, J.R.S.; Deenapanray, P.N.K.; Rughooputh, S.D.D.V. Geographic Carbon Accounting: The Roadmap for Achieving Net-Zero Emissions in Mauritius Island. Journal of Environmental Management 2023, 333, 117434. [Google Scholar] [CrossRef]
  45. Polge, E.; Torre, A. Territorial Governance and Proximity Dynamics. The Case of Two Public Policy Arrangements in the Brazilian Amazon. Papers in Regional Science 2018, 97, 909–930. [Google Scholar] [CrossRef]
  46. Torre, A.; Rallet, A. Proximity and Localization. Regional Studies 2005, 39, 47–59. [Google Scholar] [CrossRef]
  47. Menconi, M.E.; Sipone, A.; Grohmann, D. Complex Systems Thinking Approach to Urban Greenery to Provide Community-Tailored Solutions and Enhance the Provision of Cultural Ecosystem Services. Sustainability 2021, 13, 11787. [Google Scholar] [CrossRef]
  48. Camps-Calvet, M.; Langemeyer, J.; Calvet-Mir, L.; Gómez-Baggethun, E. Ecosystem Services Provided by Urban Gardens in Barcelona, Spain: Insights for Policy and Planning. Environmental Science & Policy 2016, 62, 14–23. [Google Scholar] [CrossRef]
  49. Davies, C.; Lafortezza, R. Transitional Path to the Adoption of Nature-Based Solutions. Land Use Policy 2019, 80, 406–409. [Google Scholar] [CrossRef]
  50. Hundertmark, W.J.; Lee, M.; Smith, I.A.; Bang, A.H.Y.; Chen, V.; Gately, C.K.; Templer, P.H.; Hutyra, L.R. Influence of Landscape Management Practices on Urban Greenhouse Gas Budgets. Carbon Balance and Management 2021, 16, 1. [Google Scholar] [CrossRef]
  51. Al-Nadabi, A.S.; Sulaiman, H. Screening Tree Species for Carbon Storage Potential through Urban Tree Inventory in Planted Vegetation. Open Journal of Forestry 2022, 13, 61–74. [Google Scholar] [CrossRef]
  52. Actaplantarum. Available online: https://www.actaplantarum.org/flora/flora_info.php?id=6070&nnn=Populus%20nigra (accessed on 26 03 2024).
  53. The Purdue Arboretum explorer. https://www.arboretum.purdue.edu/explorer/plant_genus/populus-poplar-aspen-cottonwood/.
  54. Pignatti, S. Flora d’Italia; Edagricole Calderini: Bologna, Italy, 2017. [Google Scholar]
Preprints 102504 g001
Figure 1. (a) Land use of the municipality of Perugia by interpretation of aerial images - year 2021 (5 meters spatial resolution); (b) Distribution of inhabitants by the population census of 2021 (grid of 1 km2).
Figure 1. (a) Land use of the municipality of Perugia by interpretation of aerial images - year 2021 (5 meters spatial resolution); (b) Distribution of inhabitants by the population census of 2021 (grid of 1 km2).
Preprints 102504 g001
Preprints 102504 g002
Figure 2. Urban park – case study.
Figure 2. Urban park – case study.
Preprints 102504 g002
Preprints 102504 g003
Figure 3. Inventoried trees classified per class of performance in carbon sequestration.
Figure 3. Inventoried trees classified per class of performance in carbon sequestration.
Preprints 102504 g003
Table 1. Statistics about the structural values and carbon sequestration capacity of the study park's trees, by class of performance and for the resulting tree with the best performance, called I-Tree_CS.
Table 1. Statistics about the structural values and carbon sequestration capacity of the study park's trees, by class of performance and for the resulting tree with the best performance, called I-Tree_CS.
Class of performance
study area null very poor poor acceptable good very good I-Tree_CS
Trees (number) 362 172 63 54 49 21 3 1
Canopy cover (m2) x̅ 27.84 11.24 24.95 40.20 47.72 85.30 91.00 132.70
median 13.70 4.60 19.20 27.65 27.00 100.30 70.90
first quartile 4.50 2.38 10.50 13.80 22.50 24.25 70.15
third quartile 35.15 10.35 35.00 63.88 70.10 136.50 101.80
Leaf Area (m2) x̅ 179.36 49.58 183.13 283.21 336.71 534.38 616.93 643.70
median 47.30 8.15 143.40 129.35 124.50 534.36 643.70
first quartile 8.25 5.08 26.15 43.68 86.60 102.60 586.60
third quartile 208.50 27.45 268.20 510.75 567.80 743.75 660.65
Leaf Biomass (kg) x̅ 18.10 4.44 22.85 24.94 26.65 72.02 60.77 46.40
median 4.00 0.85 13.30 10.25 9.60 42.10 62.60
first quartile 0.90 0.40 2.50 43.68 6.80 7.95 54.50
third quartile 20.38 2.40 26.60 36.70 44.40 56.65 67.95
DBH (cm) x̅ 26.32 11.73 26.20 38.96 47.24 58.21 72.4 86.30
median 22.15 7.90 24.40 37.50 46.10 58.90 81.0
first quartile 8.33 5.05 19.45 30.65 38.40 44.45 65.50
third quartile 40.63 11.93 32.30 44.58 53.20 70.40 83.65
Height (m) x̅ 7.48 4.46 9.1 9.99 10.08 12.68 21.9 24.10
median 4.95 3.35 7.6 8.85 5.70 14.80 23.5
first quartile 3.30 2.50 4.75 4.10 5.10 5.45 20.80
third quartile 11.83 5.00 13.40 15.55 15.90 16.70 23.80
Carbon sequestration (kg yr-1 ) x̅ 10.39 2.23 8.35 15.31 23.39 33.09 61.77 76.30
median 6.10 1.90 8.30 15.25 23.50 30.80 60.50
first quartile 2.10 1.20 7.00 13.50 22.00 29.60 54.50
third quartile 16.68 3.10 9.90 16.98 25.00 33.05 68.40
Table 2. CO2 emission to manage the study area in the year 2023 expressed in kilograms of Carbon dioxide equivalent (CO2e).
Table 2. CO2 emission to manage the study area in the year 2023 expressed in kilograms of Carbon dioxide equivalent (CO2e).
Sources of consumption power supply (measure unit) power supply (value) conversion factor (measure unit/kWh) Energy consumption (kWh) emission factors (Kg CO2/kWh) (Kg CO2e)
locker and service rooms derived
electricity (kWh)		 1957 1 1957.00 0.43 841.51
Natural gas (m3) 1453 9.94 14442.82 0.19 2744.14
greenhouse LPG (liters) 17706 10.3 182371.80 0.21 38298.08
garden machines diesel (liters) 333 10.7 3563.10 0.25 890.78
total 202334.72 total 42774.50
Table 3. Scenarios toward carbon neutrality.
Table 3. Scenarios toward carbon neutrality.
scenario 0 scenario 1 scenario 2 scenario 3
trees (n.) 362 175 362 98644
Surface (m2) 23234 23234 48037 13090000
Surface (ha) 2.32 2.32 4.80 1309.00
Surface (%) 100 100 207 56339
Canopy cover (m2) 10078 23223 48037 13090000
leaf area (m2) 64930 112648 233019 63496858
Leaf Area Density (m²/ha) 27945 48483 48508 48508
LAI (Leaf Area Index) 6.44 4.85 4.85 4.85
tree density (n.of trees/ha) 156 75 75 75
leaf biomass (kg) 6551 8120 16797 4577061
Carbon Sequestration (kg yr-1 ) 3762 13300 27512 7496910
KgCO2e 42775 42775 42775 -
% offset 9 31 64 -
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
Prerpints.org logo

Preprints.org is a free preprint server supported by MDPI in Basel, Switzerland.

facebook logotwitter logolinkedin logo
MDPI Initiatives
Important Links
Subscribe

Disclaimer

Terms of Use

Privacy Policy

© 2024 MDPI (Basel, Switzerland) unless otherwise stated