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Article

Influence of Forest Management on the Sustainability of Community Areas in Northern Inland Portugal: A Simulated Case Study Assessment

by
André Sandim
1,2,
Dalila Araújo
1,2,
Teresa Fonseca
1,2 and
Maria Emília Silva
1,2,*
1
Department of Forest Sciences and Landscape Architecture, University of Trás-os-Montes and Alto Douro, Quinta de Prados, 5000-801 Vila Real, Portugal
2
CITAB—Centro de Investigação e de Tecnologias Agroambientais e Biológicas, University of Trás-os-Montes and Alto Douro, Quinta de Prados, 5000-801 Vila Real, Portugal
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(18), 8006; https://doi.org/10.3390/su16188006
Submission received: 31 July 2024 / Revised: 6 September 2024 / Accepted: 10 September 2024 / Published: 13 September 2024
(This article belongs to the Section Sustainable Forestry)
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Abstract

:
The northern inland region of Portugal has experienced significant population decline due to the exodus of younger generations and an aging population. This has led to the abandonment of traditional activities in these territories, contributing to territorial abandonment, degradation of local economic conditions, increased social vulnerability, and a heightened risk of rural fires. The presence of communal lands, known as “baldios”, is an important facilitator for implementing actions that revitalize local villages, making them more attractive to the community. Forests, which are abundant in the baldios of northern inland Portugal, have the potential to generate environmental, social, and economic value through carbon sequestration, job creation, population stabilization, and wealth generation in the villages. However, the viability of this asset as a driver for sustainable development depends on the forest management model implemented. This case study aims to demonstrate that different forest management models have varied impacts on sustainability indicators, particularly economic and environmental sustainability. Based on naturally regenerated forests in the Carvalhelhos baldio in the Tâmega Valley region, data were collected to simulate in software four management scenarios, varying the number (0 to 4) and age of thinnings until the final cut. The simulation allowed for the calculation of the following economic indicators: Internal Rate of Return (IRR), Net Present Value (NPV), and Net Profitability Index (NPI), as well as environmental indicators related to carbon capture and accumulation, such as Gross Carbon Accumulation, Net Carbon Accumulation, Carbon accumulated in dead and suppressed trees, and carbon balance per management model. The simulations indicate that, for the studied area, Scenario 2, which involves only one thinning, yielded the highest total wood volume (cubic meters per hectare) over the cycle, making it the most suitable for biomass production. Meanwhile, Scenario 4, with three thinnings, showed the best results for individual volume (cubic meters per tree), making it more suitable for producing higher-value logs. Scenario 5 presented the best economic results and carbon capture. In all simulations, Scenario 1 showed the worst performance in the analyzed indicators. It was found that the indicators varied among the studied crop plans, highlighting that the adoption of a silvicultural regime depends on the forest characteristics, objectives, exploitation conditions, and local population sensitivity to regional priorities.

1. Introduction

It is undeniable that forests play a crucial role globally in controlling climate change by balancing greenhouse gases through carbon capture and storage. Increasingly, forest management practices aim to guide forests towards a condition more favorable to mitigating the greenhouse effect, maintaining biodiversity, and fulfilling their social and economic functions, in what is conventionally called Climate-Smart Forestry [1].
Many actions worldwide converge towards sustainability through the forest management concepts for sustainability indicated by Climate-Smart Forestry. In Europe, ref. [2] highlighted the main indicators to be observed: Forest Damage, Tree Species Composition, and Carbon Stock, to identify trends and plan actions.
In Portugal, the instruments for forest regulation and development generally adhere to global guidelines, aiming at the prevention and mitigation of threats to forest resources, the full valorization of forests, and the participation of stakeholders in formulating forest policies [3].
Extensive areas of the Portuguese national territory, particularly low-density rural regions, face significant socio-economic problems such as rural exodus, depopulation, aging population, impoverishment, unemployment, emigration, and the associated abandonment of agricultural land [4,5]. In 2023, only 11.4% of the mainland population resided in predominantly rural areas [6]. However, the development of the rural regions, with their multiple ecosystem services, acquires much greater social and political importance than their demographic weight, especially in light of the current environmental crisis [7,8].
In this context, common lands, community territories with their integral parts and equipment, owned and managed by local communities with prior administrative organization [9], can have important impacts on the implementation of actions that allow the maintenance of traditional productive activities, the socio-economic context of rural communities, the maintenance of multifunctional landscapes, and consequently, their capacity to provide ecosystem services, and the feasibility of making them more attractive and culturally valued [8,10,11].
Considering the importance of common lands, both environmentally and socio-economically, as well as their total area occupation in mainland Portugal (5%) and the forest area they cover (14%) [12], it is essential to understand the perspectives of sustainable development of these areas. Environmental sustainability involves the responsible management of natural resources to maintain the conservation of these resources, protect biodiversity, preserve ecosystem services, contribute to climate change mitigation, and prevent ecosystem degradation [13,14].
The Pinus pinaster (Maritime Pine), a species abundantly present in the common lands of northern Portugal, is a potential source of environmental, social, and economic value generation through carbon capture, job creation, population retention, and wealth generation in interior villages. The exploitation of Maritime Pine is seen as an important economic activity in terms of a circular, low-carbon, and sustainable economy, whether due to the wide range of products and by-products derived from this sector, such as wood, logging residues, resin, pine cones, bark, and needles or due to the carbon retention capacity in the biomass of the stand [15].
Establishing sustainable conditions in common land forests in northern Portugal has a level of complexity that extends far beyond the study presented in this article. However, it certainly encompasses the issues addressed here, requiring some considerations. For example, the market is quite dynamic and sometimes volatile, and the indicators presented vary according to the prices and costs at the time. Thus, the timing of the analysis is very important when concluding the indicators. Regarding the Maritime Pine sector, it can be stated that the supply of wood from this species has been relatively stable in recent years, around 1.7 million cubic meters, while the demand is around 4 million cubic meters. This results in an average deficit of 2.3 million cubic meters, with an accumulated value of approximately 17 million cubic meters between 2015 and 2022 [16,17,18,19,20,21,22].
At the same time, forest fires consumed 23,512 hectares of Maritime Pine forests [23], with an average volume of 93 m3/ha [12]. This means that approximately 2.2 million cubic meters of wood were lost due to fires, which underscores the importance of forest management, as reducing the burned area not only minimizes the wood deficit and brings profitability to the interior of the country, but also prevents the emission of approximately 1000 tons of carbon into the atmosphere per year, minimizes risks, and saves resources allocated to fire fighting.
The objective of this case study is to demonstrate that different forms of forest management impact sustainability indicators differently, namely those associated with economic and environmental sustainability. To verify the influence of forest management on these indicators, a section of a forest in the common land of Carvalhelhos, in the municipality of Boticas, in the Tâmega Valley region, was characterized. Following this characterization of dendrometric characteristics, data on operational income equipment costs, and personnel consumption during the first intervention in the forest, which consisted of clearing vegetation strips and reducing density in the remaining leave strips, were collected. These data served as the basis for projecting costs and income in the simulation of forest evolution until 45 years of age, the final cut age.

2. Materials and Methods

2.1. Characterization of the Area

The following study was based on an area characterized by the presence of a young naturally regenerating Maritime Pine (Pinus pinaster) forest in the village of Carvalhelhos, in the municipality of Boticas, located in the northern region of Portugal, more precisely in the Tâmega River basin. The area spans a total of 5 hectares and was selected because it represents a typical ecosystem of Maritime Pine forests in northern Portugal and already has a forest management plan for the specific area chosen, which can be used as a comparison element between the simulated management models.
The study area is located in the common land of Carvalhelhos, at the geographical coordinates 41°41’7.88” N latitude and 7°43’7.28” W longitude and has a temperate climate of the Csb type according to the Köppen classification, with a rainy winter and a dry, mildly hot summer [24]. Figure 1a represents the location of the study area in Portugal, while Figure 1b shows the detailed actual area of 5 hectares, and Figure 1c,d illustrate the general aspect of the forest found in the area before and after the first thinning.

2.2. Characterization of the Forest

The simulation of forest growth that allows the estimation of wood volume, stored carbon, and the calculation of economic indicators through cash flow depends on the original characteristics of the forest, such as dendrometric parameters, exposure, and slope of the terrain. Therefore, to ensure a simulation based on a real area, field data were collected to feed the simulator and project the forest growth and its configuration over the years until the final cut.
In the field, in February 2022, an expedited survey was conducted, which consisted of installing 4 square plots of 4 m × 4 m. The distribution of the plots was preliminarily indicated on a map in the office, selecting the most representative regions of the forest on-site, respecting the area delimited by the respective Forest Management Plan (PGF). All trees contained in the plots were counted and checked for whether they were alive or dead, and for the living trees, their height was measured using a Vertex III instrument Haglof (Langsele, Sweden), and the diameter at breast height (DBH) was measured using a graduated caliper Haglof (Langsele, Sweden). The whorls were also counted to determine the age of the stand.
The data were processed in the office, where the density of natural regeneration in the number of trees per hectare (N), average diameter (dm), dominant diameter (Ddom), average height (hm), and dominant height (Hdom) were calculated.

2.3. Simulation of Forest Evolution in Modispinaster (Dendrometric, Carbon, and Resin Yield)

The simulation of the development of the forest studied in the Carvalhelhos common land was conducted using the Modispinaster model [25] always for an area of 1 hectare, where the forest growth was governed by the standard Stand Density Index (SDI) of the model and adjusted for the average characteristics of forests in the Tâmega valley, varying between 55 and 60. The numbers used in Modispinaster are considered the standard for the studied area and determine the density at which the trees begin to compete with each other.
The intensity of thinnings (intermediate tree cuts) was determined by the Wilson Factor (WF), which relates the average dominant tree height to the average spacing between trees, as the dominant height. Unlike the mean breast height diameter, it is not influenced by spacing but rather by the species characteristics and site quality [26]. The Wilson Factor entered into the software for the study was 0.21 as indicated in the PGF of the area [27]. According to the model indicated by [28], the exception was the first thinning, which aimed to reduce the number of trees to 3584 to explain the real field scenario, assessed by a post-thinning characterization conducted after the area had been intervened. Five forest management scenarios were considered, where the initial age corresponded to the age of the regeneration in the studied area (16 years) and the final cut projected at 45 years for all scenarios, varying the number of thinnings in each scenario from 0 to 4 thinnings, with scenario 3 corresponding to the one proposed in the PGF of the area. The decision to use the 4 scenarios presented was based on the assumption that the common practice of intervention in the forest in the PGFs (Forest Management Plans), foresees two thinnings, so scenario 1 did not foresee intervention (for comparison purposes with the other scenarios), while scenarios 2 and 4 have one less thinning and one more, respectively, than the standard used in Portugal.
Table 1 indicates the characteristics of each scenario.
The simulations generated tables that allowed for the definition of the evolution of dendrometric indicators (total forest volume at 45 years, number of trees per hectare, individual tree volume, volume and dimensions of thinned wood), resin production capacity (number of trees with DBH greater than 20 cm per hectare), and total biomass in tons. Using the conversion factor of 0.47 [29], the amount of carbon stored in aboveground forest biomass was calculated.
The carbon stored in the forest was divided into two distinct fractions: the biomass fraction represented by trees with a diameter of less than 20 cm, referred to here as fine biomass, which, when exploited, rapidly degrades as it is invariably used for burning and energy generation; and the biomass consisting of trees with a diameter greater than 20 cm, known as coarse biomass, which has a longer cycling period as it is typically transformed into long-life products. It was also considered that all biomass derived from natural mortality within the forest, being more susceptible to degradation agents, has a short cycling period. Thus, two categories of biomass were established: short-cycle (dead and fine biomass) and long-cycle (coarse biomass), which were simulated and compared, generating a carbon balance for the analyzed scenarios.

2.4. Cash Flow Preparation and Economic Performance Indicators

The selected economic performance indicators were NPV (Net Present Value), Internal Rate of Return (IRR), Benefit/Cost ratio, and payback period. To determine these indicators, a cash flow was prepared, where the costs (outputs) of intervention and exploitation in the forest were based on the CAOF (Commission for Forest Operations Monitoring) tables from DGADR (Directorate General for Agriculture and Rural Development) of the Portuguese state [30].
The revenues (inputs) were determined by multiplying the volume of the trees extracted during interventions by the value assigned to the average dimensions (average DBH) of the wood extracted at each thinning and by multiplying the revenue from renting trees for resin extraction by the number of trees with DBH above 20 cm per hectare.
The market value of the wood was considered to be that indicated in SIMEF (Simplified Market Quotations System for Forest Products), where wood with a diameter up to 2.5 cm was priced at EUR 15.00/m3, the wood between 2.5 cm and 20 cm was valued at EUR 32.00/m3, and wood with a diameter greater than 20 cm was priced at EUR 40.48/m3 [31].
For the rental of trees for resin extraction, the rate used was EUR 0.45 per tap, for areas where the number of trees per hectare with DBH above 20 cm exceeds 400 (Oral consultation with Resipinus—Association of Resin Distillers and Harvesters of Portugal).
The opportunity cost used in constructing the cash flow was 3% and the inflation rate considered was 0%, and the analysis period was 29 years, with the initial age of natural regeneration at 16 years and the projected final cut at 45 years. The first thinning, when considered, was scheduled in the simulation for 16 years, as it actually occurred in the field and was foreseen in the PGF.

3. Results

3.1. Dendrometric Characteristics of the Forest: Wood Volume and Dimensions

The simulation results showed that the evolution of the five scenarios tended to converge towards similar numbers of trees per hectare and wood volume by the end of the cycle. However, individual tree volume appears to be more strongly influenced by a greater number of thinnings.
Scenario 2 exhibited the highest final forest volume in the simulation, while Scenario 5 had the lowest total forest volume in cubic meters at 45 years.
Figure 2 illustrates the evolution of the forest volume in m3 throughout the assessed cycle.
The evolution of the number of trees throughout the cycle, from 16 to 45 years, taking into account both mortality and the number of trees removed during thinnings, reveals that competition among trees tends to establish a pattern of decreasing tree numbers over the simulated cycle. In all scenarios, there is a convergence towards a similar number of trees per hectare.
Whether due to natural mortality or scheduled thinnings, a reduction in the number of trees per hectare was observed across all scenarios, with final tree numbers ranging between 767 and 1118. The scenario with the lowest density (number of trees per hectare) was Scenario 5, which involved the highest number of scheduled thinnings, totaling 4. In contrast, Scenario 1, which involved no scheduled thinnings, exhibited the highest number of trees per hectare.
Figure 3 presents a graph showing the simulation of the evolution of the number of trees per hectare (N).
The individual volume, i.e., the volume of each tree throughout the simulated cycle, shows a greater increase in scenarios with a higher number of thinnings, specifically Scenarios 4 and 5. Conversely, the scenario with the lowest performance in this indicator was Scenario 1, where no interventions in the forest were planned. To complete the analysis of wood volumes in the evaluated scenarios, the simulation of average individual volumes, i.e., the average volume in m3 of trees in each scenario at 45 years, was performed. Among the criteria assessed, individual volume provided a more precise view of the growth curve distinctions presented in Figure 4. Scenarios 4 and 5, with a higher number of thinnings (3 and 4, respectively), had trees with the largest individual volumes, while, by reverse analogy, Scenario 1, with no intervention, had trees with the smallest average volume.
The quotient between the total stand volume and the number of trees per hectare results in the individual tree volume. In this indicator, Scenario 5, with the highest number of thinnings, stood out with the highest average individual volume (0.58 m3/tree), while Scenario 1, with no planned thinnings, had the lowest individual volume (0.39 m3/tree).
Figure 4 illustrates the simulation of the evolution of the average individual volume across the 5 scenarios throughout the cycle.

3.2. Evolution of Carbon Stock According to Forest Management Model

The weight in tons representing the fraction of carbon in biomass exhibits an increasing trend over time due to the growth in diameter and height of the trees. For the following data, the carbon fraction in the soil and the carbon contained in root biomass were not considered, as the study focused solely on evaluating the aboveground biomass and the carbon contained within it. The first result produced by the simulation is expressed in a set of curves representing the gross carbon accumulation in the forest, i.e., all the carbon captured through photosynthesis and transformed into biomass in the trunk, branches, and leaves. This simulation also accounts for carbon retained in dead trees due to natural mortality, which is significant in high-density Pinus pinaster regeneration stands.
The pattern of the graph lines shows that all scenarios exhibit very similar gross carbon weight increments, reaching around 190 tons of carbon per hectare at 45 years, except for Scenario 5, which, with the highest number of thinnings, reached approximately 185 tons of carbon per hectare.
Figure 5 displays the results for the gross carbon accumulated in the forest in tons.
The forest growth simulation indicates that some of the carbon captured in the Pinus pinaster forest across all five analyzed scenarios will be in trees that eventually die due to natural competition or are thinned. Regardless of whether tree mortality is due to thinning or natural causes, the return of this carbon to the atmosphere depends on the fate of the dead or thinned biomass. Smaller trees tend to have a faster cycling of carbon, while larger trees tend to retain carbon for a longer period.
The graph lines representing the scenarios start from zero, as the simulations were initiated at 16 years of age when the first intervention occurred. It is observed that all scenarios converge to a similar final carbon weight (S2 = 85 tons, S3 = 82 tons, S4 = 82 tons, S5 = 85 tons), except for Scenario 1, which showed a noticeably higher value than the other scenarios (S1 = 94 tons).
Figure 6 illustrates the simulated evolution of the carbon weight contained in dead or thinned trees for each scenario.
Subtracting the weight of carbon in dead and thinned trees from the gross carbon weight captured and converted into biomass by the forest yields the net carbon weight accumulated over the cycle in each scenario.
The carbon accumulated in the forest (excluding mortality and thinnings) was lowest in Scenario 1 (S1 = 98 tons), where the forest went through its cycle without any thinnings, and highest in Scenario 4 (S4 = 107 tons) with three thinnings. Scenarios 2, 3, and 5 reported values ranging from 102 tons to 106 tons (S2 = 106 tons, S3 = 104 tons, S5 = 102 tons) at 45 years.
Figure 7 shows the graph representing the net carbon weight accumulated in the forest from 16 to 45 years.
With the simulations conducted, it was also possible to calculate the total weight of the above-ground biomass of the studied stand and, with the application of the conversion factor, obtain the total weight of carbon stored in each of the simulated scenarios at 45 years. Although the carbon weights across scenarios were quite close, Scenarios 2 and 4 exhibited the highest accumulated carbon weights, while Scenario 1 showed the lowest carbon weight at 45 years, as illustrated in Figure 8. When evaluating the simulations of carbon weight retained in thinnings and mortality, Scenario 1, which had no thinnings, accumulated the highest weight in carbon due to natural mortality. Scenario 4, with three thinnings, showed the lowest accumulated carbon weight in thinnings and mortality, with a value only slightly higher than Scenario 5, which had one additional thinning.
The simulated projections of coarse and fine biomass allowed the creation of a comparative graph between short-cycle and long-cycle carbon storage. It is evident that Scenario 5, with the highest number of thinning operations, had the greatest carbon storage balance, totaling 52 tons over the cycle. In contrast, Scenario 1, which had no thinning operations, showed a balance of 0 tons between carbon stored in fast-cycling and slow-cycling biomass, as indicated in Figure 9.

3.3. Economic Performance of Wood and Resin Production Indicators

The cash flow developed through forest growth simulations, volumes extracted in thinnings, and cost projections via CAOF allowed for the creation of a cash flow statement that enabled the determination of financial performance indicators. In addition, it also allowed for the calculation of the number of trees over 20 cm in diameter (DBH) suitable for resin tapping, and consequently, the calculation of the resources (inputs) derived from this activity.
The following simulation shows the evolution of the number of trees with a diameter greater than 20 cm over the cycle, with the dashed line indicating the minimum threshold of 400 trees per hectare where resin tapping is viable. It can be observed that the scenario that reaches the resin tapping threshold earliest is scenario 5, with 4 thinnings at 25 years, while the scenario that takes the longest to reach this threshold is scenario 1, with no interventions, at 30 years, as shown in Figure 10.
The revenues from resin extraction depend not only on an earlier start of resin extraction but also on the integration between the resin extraction time and the number of trees being tapped. The same reasoning applies to the revenues from the sale of extracted wood, which will depend on the number of trees and the individual volume found in each of the simulated scenarios as shown in Table 2.
In the Table 3 records the economic performance results of the cash flows from the simulated scenarios, highlighting scenario 4 as having the highest NPV and Benefit/Cost, while scenarios 4 and 5 showed the best IRR. Scenario 1 did not present IRR or Benefit/Cost as it had no initial investment.
The combined analysis of economic results with carbon retention capacity will underpin the discussion that follows.

4. Discussion

An important point to address as the basis for this discussion concerns the local scope of the presented study. However, it is necessary to highlight some points about the global impacts of the cumulative effects of specific forest management actions. The starting point for this discussion should be based on the concept of thinking globally and acting locally, as environmental impacts inadvertently affect the entire planet. Nevertheless, mitigation actions, even when guided by global directives, must take into account local conditions, such as consumption relationships, level of administrative control, distribution of costs and benefits, and attachment to place [32,33].
The pursuit of sustainability, given its characteristics where the impact is global and demands local solutions, can be classified as a wicked problem due to its challenging nature and the need for multiple solutions tailored to each particular situation [34].
The study of the influence of forest management models on the sustainability of rural communities in Portugal is limited to the area where the study was conducted. However, it seeks to address problems closely linked to climate change, which is the root cause of forest fires, now considered one of the greatest threats to the sustainability of the territory [35]. This highlights the relevance of local studies for mitigating global environmental problems.
The study was divided into three main parts (Wood Volume, Carbon, and Economic Viability). The first part concerns the quantitative and qualitative potential for wood supply from the studied forest under different scenarios. A total of three criteria were established: the final volume of wood in the forest at 45 years, the number of trees per hectare, and the average individual tree volume in each of the simulated scenarios.
The importance of forest management to achieve optimized scenarios is emphasized by ref. [35] confirming that higher intensity thinning in Pinus pinaster in northeastern Spain increased the survival rate of young plants and stand density. However, it also concluded that the effect on tree growth was minimal.
The wood volume at 45 years, the age of the final harvest and end of the current cycle, reflects the economic value of the forest. It is expected that the management model ensures the maximum possible volume, as the economic value of the forest will be the product of the wood price and the volume in cubic meters or tons. In this aspect, Scenario 2, with only one intervention at 16 years, showed the highest final volume, while Scenario 5 had the lowest final volume, possibly because with more thinning (4), the forest may not have time to reach its maximum volume gain potential before the final harvest.
The scenario with the highest volume is not necessarily the one that yields the best revenue, as the forest value depends not only on the final volume but also on the price paid for the wood, which in turn depends on the average diameter of the trees. Thus, it makes sense to examine the number of trees in the area, where the ratio with the total volume of the standing forest will indicate the average volume per tree.
The scenario with the highest number of trees per hectare was Scenario 1, which, without thinning, only lost trees due to natural mortality from competition. This naturally results in much less drastic decreases each year, as evidenced by the curve pattern for this scenario shown in Figure 3. Conversely, in reverse logic, Scenario 5, with the highest number of thinnings, ended the simulated cycle with the lowest number of trees per hectare. Ref. [36] also found that the number of thinnings affects biomass production for energy purposes, but their approach differed, where a 20-year cycle with only one thinning in high productivity areas was advantageous over a 45-year cycle with four thinnings.
The relationship between the number of trees and average volume versus wood price will further define the most economically attractive scenario. A similar study by ref. [37] in central Portugal corroborates the fact that the management model, production, and associated costs significantly affect the quantitative and qualitative results of volumes generated throughout a forest production cycle.
The simulation of stored carbon in forest biomass presented a simplified approach to assess the forest’s effectiveness in fulfilling its environmental function, which is one of the pillars of sustainability. The analysis presented is not intended to be a carbon balance—a much more complex topic requiring a deeper knowledge of additional variables—but as the aerial tree component, mainly the trunk, is included among the necessary variables for calculating the carbon footprint, it was used as an indicator of the forest’s carbon retention capacity.
The simulated scenario that accumulated the highest carbon weight was Scenario 1, while Scenario 5 accumulated the lowest carbon weight over 45 years, aligning with the biomass accumulation logic. However, it cannot be concluded which is more or less efficient in terms of carbon capture and retention, as some of this carbon will return to the atmosphere in a short cycle, as mentioned earlier in the article. Therefore, it is also important to assess the carbon stored and eventually extracted from the forest through thinning or natural mortality.
To complete the analysis and draw conclusions about carbon stock, the net carbon weight accumulated—i.e., the total carbon captured minus the thinnings until 45 years—should be examined with caution. The separation of short-cycle and long-cycle biomass will define the best and worst scenarios. In terms of net carbon weight, Scenario 4 had the highest weight, while Scenario 1 had the lowest. A strong correlation between thinning intensity in Pinus pinaster and carbon storage capacity was also found by ref. [38] in the Andalusia region of Spain. However, contrary to this study, the criterion used was the intensity of the decrease in the number of trees per hectare, where larger decreases in density indicated higher carbon accumulation in biomass and soil.
The final analysis to determine the best scenario considering carbon stock in the trunks involves considering the already mentioned long-cycle and short-cycle biomasses. For this article, long-cycle biomass is defined as all wood from trees extracted from the forest through thinning with an average diameter at breast height above 20 cm, while short-cycle biomass is the sum of all wood from natural mortality in the forest and from trees thinned and extracted with an average diameter at breast height below 20 cm. The division of dead wood or woody material into classes was also adopted by [39], who identified material above 20 cm in diameter as coarse and determined that the most abundant material in the forest was fine material with some degree of decomposition, reinforcing the thesis of a shorter carbon cycle for dead wood in the forest.
It is assumed that long-cycle wood, after 29 years of the study cycle (16 to 45 years old), will still be retained in products with a longer life cycle, while short-cycle biomass will have been degraded and emitted carbon back into the atmosphere through decomposition in the forest or use in short-cycle products such as biomass chips for energy. The decomposition of dead wood in the forest is an important global carbon emission source (11 Peta grams/year), occurring mainly in tropical forests. Temperate forests, such as those in this study, are more efficient at carbon retention, especially due to the climate [40].
The best-performing scenario for carbon storage, based on the above discussion, was Scenario 5, followed by Scenario 4. Scenario 1, which had a zero balance, was the worst performer, as it did not capture any carbon. This result may be due to unmanaged forests having more dead wood and therefore more decomposition and carbon release [41], indicating that any applied management treatment (among the scenarios analyzed) is better than no management. Additionally, the study by ref. [42] provides an important conclusion that in drought and extreme heat conditions, expected for the Iberian Peninsula due to climate change, areas of natural regeneration are more efficient in carbon sequestration and storage compared to plantations, highlighting the importance of these ecosystems and adjacent populations.
Resin production was not included in the carbon footprint assessment, as it is not the main focus of the study, but it is an important factor in the economic viability of forest exploitation. It was found that a higher number of thinnings anticipates the point at which resin production can be exploited, coinciding with the number of trees with a diameter above 20 cm exceeding 400 trees, as seen in Scenario 5. Conversely, a lower number of thinnings delays the start of resin production, as seen in Scenario 1.
By combining wood production indicators with resin production and associated costs, it was possible to analyze economic performance indicators through the construction of a cash flow for each scenario. The results showed that using the highest NPV as a project selection criterion [43], Scenario 4 was indicated as the best, while Scenario 1 was the worst. All scenarios with intervention showed increases in volume, stored carbon, and economic indicators, suggesting that all scenarios are viable, and their values could be further enhanced if carbon credit revenue is considered, as noted by [44].
One aspect that cannot escape the discussion on the relevance of studies concerning the sustainability of common areas is the fact that the interior of Portugal faces social and environmental issues such as depopulation, an aging population, and weak economic dynamics. Improving public policies based on research and systemic solutions that consider all components of the relevant ecosystem is essential. This involves forest management geared towards sustainable development, where the interests of the local community are safeguarded, and autonomy over decisions regarding the use of forest assets is always supported by a technical and legal framework that allows for decision-making leading to a more sustainable scenario than the current one [45,46].
The study was limited to demonstrating the usefulness of the forest management model in the development of comprehensive forest management plans and confirming the hypothesis that there is an impact on the selected sustainability indicators. This study is relevant because, although limited, it is still scarcely explored operationally in the field in Portugal, where forest management almost always focuses on techniques for reducing combustible material in forests due to the high incidence of wildfires.

5. Conclusions

After analyzing the simulated scenarios, it can be concluded that a single thinning operation was sufficient for the wood volume produced over the studied cycle to reach its maximum among the compared scenarios. This indicates that in forests with similar characteristics to the one studied, performing only one thinning intervention may offer the best model for timber production aimed at markets where volume is more important than log characteristics, such as the energy industry, pulp production, and reconstituted panel manufacturing.
For the production of larger-diameter logs, in the case studied, performing three thinning operations proved more efficient in supplying timber with larger dimensions (diameter), surpassing the other scenarios.
The most positive carbon balance, meaning the highest amount of carbon captured, occurred in the simulation where there was also the highest number of thinning interventions. This suggests the presence of a trend where a greater number of thinnings favors carbon capture and retention among the scenarios analyzed for the local situation.
Finally, although it is possible to choose different scenarios for different forest purposes, the economic factor is essential for the forest to generate wealth and contribute to local sustainability. Therefore, the management model with the best economic performance indicators can be considered the best-performing model among all those analyzed, especially if the forest is not committed to a specific market. This model was the one that predicted the highest number of thinning, with three or four interventions throughout the cycle.
It is important to highlight that the number of variables that contribute to the viability of a model is much broader than the scope of this limited study. Elements such as carbon credits, environmental services, market characteristics, logistics, operational conditions (climate, terrain, road network, distance to consumers), and business scale should be included in an analysis when selecting the ideal scenario. However, the results presented may be relevant in creating a “template” of essential indicators when discussing sustainability. Additionally, in the continuation of this study, it is important to expand the analysis to consider the physiological behavior of the forest, which has a direct impact on the SDI (Stand Density Index) and WF (Wilson Factor), and to also consider different economic scenarios throughout the forest cycle. The application of these parameters may initiate a database of information that could, in a broader sense, suggest specific management models for other local conditions.

Author Contributions

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

Funding

This work was supported by RN21 Integrated Project—Innovation in the Natural Resin Sector to Strengthen the Portuguese Bioeconomy, co-funded by Fundo Ambiental through Component 12—Promotion of Sustainable Bioeconomy (Investment TC-C12-i01—Sustainable Bio-economy No. 02/C12-i01/202), through European funds provided to Portugal by the Recovery and Resilience Plan (RRP), in the scope of the European Recovery and Resilience Facility (RRF), framed in the Next Generation UE, for the period from 2021–2026. And the European Union through the European Regional Development Fund and by National Funds from FCT—Portuguese Foundation for Science and Technology, under the project UIDB/04033/2020 (https://doi.org/10.54499/UIDB/04033/2020).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. (a) Location of the study area in Portugal, (b) Detailed boundaries of the study area (Google Earth, 2024), (c) General aspect of the forest before any intervention, (d) General aspect of the forest contained in the area after first thinning.
Figure 1. (a) Location of the study area in Portugal, (b) Detailed boundaries of the study area (Google Earth, 2024), (c) General aspect of the forest before any intervention, (d) General aspect of the forest contained in the area after first thinning.
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Figure 2. Evolution of Standing Forest Volume Throughout the Cycle in Each Simulated Scenario.
Figure 2. Evolution of Standing Forest Volume Throughout the Cycle in Each Simulated Scenario.
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Figure 3. Evolution of the Number of Trees per Hectare for Each Analyzed Scenario from 16 to 45 Years of Age.
Figure 3. Evolution of the Number of Trees per Hectare for Each Analyzed Scenario from 16 to 45 Years of Age.
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Figure 4. Evolution of Individual Tree Volume in Each Simulated Scenario.
Figure 4. Evolution of Individual Tree Volume in Each Simulated Scenario.
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Figure 5. Simulation of the Evolution of Aboveground Carbon Weight Captured by the Forest from 16 to 45 Years.
Figure 5. Simulation of the Evolution of Aboveground Carbon Weight Captured by the Forest from 16 to 45 Years.
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Figure 6. Simulation of the Evolution of Carbon Weight in Dead and Thinned Trees Across the Five Evaluated Scenarios from 16 to 45 years.
Figure 6. Simulation of the Evolution of Carbon Weight in Dead and Thinned Trees Across the Five Evaluated Scenarios from 16 to 45 years.
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Figure 7. Simulation of the Evolution of Net Carbon Weight in the Forest Across the Evaluated Scenarios from 16 to 45 Years.
Figure 7. Simulation of the Evolution of Net Carbon Weight in the Forest Across the Evaluated Scenarios from 16 to 45 Years.
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Figure 8. Simulated Biomass and Carbon (in tons) Present in the Studied Forest at 45 Years Across the Five Adopted Scenarios.
Figure 8. Simulated Biomass and Carbon (in tons) Present in the Studied Forest at 45 Years Across the Five Adopted Scenarios.
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Figure 9. The balance between Carbon Stored in Fast−Cycling Biomass and Carbon Stored in Slow-Cycling Biomass.
Figure 9. The balance between Carbon Stored in Fast−Cycling Biomass and Carbon Stored in Slow-Cycling Biomass.
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Figure 10. Number of trees per hectare above 20 cm diameter.
Figure 10. Number of trees per hectare above 20 cm diameter.
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Table 1. Measurement metrics of forest stand and description of the Scenarios used for forest evolution simulation.
Table 1. Measurement metrics of forest stand and description of the Scenarios used for forest evolution simulation.
Scenario 1Scenario 2Scenario 3 (PGF)Scenario 4Scenario 5
Age (years)1616161616
Dominant Height (m)7.57.57.57.57.5
Number of trees (n/ha)13,75013,75013,75013,75013,750
Basal area (m2/ha)48.648.648.648.648.6
Dominant diameter (cm)9.19.19.19.19.1
Terrain Direction (°)60°60°60°60°60°
Terrain Slope (°)20°20°20°20°20°
Final Cut Age (years)4545454545
Number of thinnings01234
1st Thinning (years)-16161616
2nd Thinning (years)--312624
3rd Thinning (years)---3632
4th Thinning (years)----40
1st Thinning Criterion-N = 3584N = 3584N = 3584N = 3584
2nd Thinning Criterion--WF = 0.21WF = 0.21WF = 0.21
3rd Thinning Criterion---WF = 0.21WF = 0.21
4nd Thinning Criterion----WF = 0.21
Table 2. Volume (m3) from wood thinning and clear cut at 45 years in each scenario considered.
Table 2. Volume (m3) from wood thinning and clear cut at 45 years in each scenario considered.
AgeScenario 1Scenario 2Scenario 3Scenario 4Scenario 5
16 114114114114
24 103
26 114
31 129
32 90
36 114
40 77
45 471498468486444
Table 3. Financial indicator results derived from the simulations of the 5 forest management scenarios over the 16 to 45-year cycle.
Table 3. Financial indicator results derived from the simulations of the 5 forest management scenarios over the 16 to 45-year cycle.
Scenario 1Scenario 2Scenario 3 (PGF)Scenario 4Scenario 5
Net Present value (NPV)EUR 7035.54EUR 7682.65 EUR 8792.64EUR 10,808.06 EUR 9944.94
Internal Rate of Return (IRR)-11%11%12%12%
Benefit/Cost-7.78.710.49.7
Pay Back3028282624
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Sandim, A.; Araújo, D.; Fonseca, T.; Silva, M.E. Influence of Forest Management on the Sustainability of Community Areas in Northern Inland Portugal: A Simulated Case Study Assessment. Sustainability 2024, 16, 8006. https://doi.org/10.3390/su16188006

AMA Style

Sandim A, Araújo D, Fonseca T, Silva ME. Influence of Forest Management on the Sustainability of Community Areas in Northern Inland Portugal: A Simulated Case Study Assessment. Sustainability. 2024; 16(18):8006. https://doi.org/10.3390/su16188006

Chicago/Turabian Style

Sandim, André, Dalila Araújo, Teresa Fonseca, and Maria Emília Silva. 2024. "Influence of Forest Management on the Sustainability of Community Areas in Northern Inland Portugal: A Simulated Case Study Assessment" Sustainability 16, no. 18: 8006. https://doi.org/10.3390/su16188006

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