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Article

Modeling the Effects of Strict Protection of Forest Areas—Part of the Provisions of the EU Biodiversity Strategy 2030

by
Katarzyna Giełda-Pinas
1,
Monika Starosta-Grala
2,
Marek Wieruszewski
3,*,
Joanna Dynowska
4,
Marta Molińska-Glura
2 and
Krzysztof Adamowicz
2
1
Regional Directorate of State Forests in Poznan, ul. Gajowa 10, 60-959 Poznan, Poland
2
Department of Forestry Economics and Technology, Faculty of Forestry and Wood Technology, Poznan University of Life Sciences, Wojska Polskiego 28, 60-637 Poznan, Poland
3
Department Mechanical Wood Technology, Faculty of Forestry and Wood Technology, Poznan University of Life Sciences, Wojska Polskiego 28, 60-637 Poznan, Poland
4
Institute of Management and Quality Sciences, Faculty of Economic Sciences, University of Warmia and Mazury in Olsztyn, ul. Michała Oczapowskiego 2, 10-719 Olsztyn, Poland
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(2), 737; https://doi.org/10.3390/su17020737
Submission received: 25 November 2024 / Revised: 6 January 2025 / Accepted: 15 January 2025 / Published: 18 January 2025
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Abstract

The case study included approx. 0.5 million ha of forest areas in Poland that are managed by the Regional Directorate of State Forests. The objective was to assess the impact of four different scenarios restricting the size of forest areas available for commercial use. Based on different criteria, each scenario set aside 10% of the total land area for strict protection on forested land, which is in line with the EU Biodiversity Strategy 2030. The economic impact (volume of reduced wood raw material) was statistically estimated for each of the four scenarios. It was confirmed that the layout of forest habitats is essential for specific limitations in forest production. For the optimal implementation of the provisions of the strategy, a balance in the selection of social, economic, and natural elements must be considered. This protects primarily the most valuable natural habitats characterized by the highest level of biodiversity, age diversity, and dispersion within the studied forest unit. The presented results may support decision-making processes used to maximize biodiversity protection while minimizing the negative economic impact of this environmental protection.

1. Introduction

The European Commission published the EU Biodiversity Strategy for 2030 on 20 May 2020 (in the following referred to as “Strategy”) [1]. It is a part of the European Green Deal [2] and provides a plan to protect nature and reverse the degradation of ecosystems [1]. The goal is to stop the loss of biodiversity and thus promote long-term environmental, social, and economic sustainability, focusing on restoring degraded habitats, expanding the network of protected areas, and improving sustainable management practices.
For European forestry, the most critical requirements included in the strategy include the protection of at least 30% of EU land areas and the strict protection of 10% of land areas, including all remaining primary forests in the EU, which provide refuge for European plant and animal species. These agreed-to commitments are to be implemented by 2030 at the latest.
Zawiła-Niedźwiecki and Borkowski [3] indicate that forests cover 80% of global biodiversity. The biodiversity crisis is widely recognized [4,5]. The strategy addresses many aspects of nature protection: sea, inland waters, wetlands, forest areas, and steppes. Due to geographical conditions, the primary environmental aspect associated with Europe is forest management. The proposed strategy should be a helpful tool supporting climate change mitigation efforts, using nature-based solutions that enable a society to prepare for changes [1].
The forest sector is critical in climate change mitigation and biodiversity protection, so integrating forest policy and its effective implementation is important [6,7,8]. Forests are an indispensable natural, social, and economic part of the ecological cycle and can contribute to European climate neutrality. According to the Treaty of Lisbon [9], European Union Member States shall share competencies regarding environmental protection, which is placed on an equal footing with other sectors of the national economy, such as transport, energy, or the internal market. It should be noted that the EU does not have a standard forest policy, and EU treaties may refer to forests only as a component of the environment.
The success of the new EU Biodiversity Strategy for 2030 [1] in achieving its objectives will depend on the ability of Member States to effectively plan the implementation of conservation measures despite limited and uncertain budgets. Public involvement and avoiding or resolving potential conflicts related to socioeconomic needs will be crucial for the successful implementation of the strategy [10]. A good policy concerns not only the number of protected areas [11] but also the quality of protection and management of the surrounding environment [12]. The goals of the strategy affect economic [13,14] and social [15] processes. However, limited knowledge of the interactions of ecosystems with these processes may undermine forest conservation efforts [10].
The management of regional forests in Poland is carried out within the framework of sustainable forest management, the assumptions of which were established in 1990 within the framework of the Ministerial Conference on the Protection of Forests in Europe [16]. Based on the assumptions of sustainable development, sustainable forest management ensures the durability of biological diversity by limiting the role of forests while simultaneously implementing the production function [17]. Forest management in Poland is based on forest management plans (FMPs), which are prepared for each forest district for 10-year periods [18]. The decisions included in the forest management plans regarding forest use result from premises related to rational management and are the effect of the adopted method of forest management. The level of permitted felling significantly affects forest management objectives and functions while maintaining the principle of continuity and durability of use in implementing a multifunctional forest management enterprise [19]. Adapting forests to climate change requires human action [20] to prevent environmental degradation and loss of biodiversity. Studies show that European state forest management is based on science-based principles, which ensures the preservation of the biodiversity hotspots and supports the achievement of sustainable development goals [21,22,23].
The study’s objective was to analyze four scenarios regarding the possible implementation of strict protection measures as part of the provisions of the strategy. For each scenario, we determine the extent of forest protection in Poland and the resulting economic impact due to the reduction in wood harvesting. The four scenarios took into account different levels of exclusion of forest land from economic use. A key element of the assessment was the economic impact that will result from reduced timber harvesting in the short and long term. The analysis was carried out using 25 forest districts in Poland, which are under the purview of the Regional Directorate of State Forests (RDLP) in Poznan.

2. Materials and Methods

The methodological concept of this study takes into account the strategy’s provision requiring the strict protection of 10% of the land area and assumes that the strictly protected areas will consist of forested land. Since the strategy does not provide clear guidelines on its implementation, the provisions need to be further examined. Therefore, the paper proposes four scenarios, each describing another form or intensity of area set-asides and interprets the results using these approaches.
The study included 25 forest districts managed by the State Forests (FSs) and belonging to Poznan’s Regional Directorate of State Forests (Figure 1). The total area this entity manages is 440,351.96 ha, of which forests occupy 419,650.83 ha. The terrain, shaped by a glacier, is mostly lowland with some diversified relief. The highest elevation in Wielkopolska is the Ostrzeszowskie Hill (284 m above sea level), and the lowest elevation is within the area of the Notec estuary to the Warta (21 m above sea level) [24]. Stands over 80 years old constitute 24.1% of the area, while the youngest stands, in age class I (up to 20 years), represent 13.3% of the area. Wood resources amount to 104.7 million m3 of merchantable wood with an average stand volume of 261 m3/ha and an average age of 62. The study assumed that the State Forests, as an organizational unit managing forests, must designate areas to be strictly protected. Considering forests are a carbon reservoir and an ecosystem that accumulates nearly 80% of global biodiversity, it was further assumed that excluding forest areas from economic activity will be important in meeting the goals of the 2030 Biodiversity Strategy [25].
Data for this study were obtained from the State Forests Information System (SILP) databases. Established in 1995, SILP is a computer-aided management system in the State Forests National Forest Holding, which consists of five closely integrated subsystems: forest management, commodity management, payroll and personnel, finance and accounting, and infrastructure. SILP is an IT tool that takes into account both the complexity of the described economic processes and SLIP’s integrative approach, consistently illustrating the status at each level of management (forests, forest districts, plants, regional directorates of the State Forests, General Directorate of the State Forests) [27]. The 235,825 records were obtained from the 25 forest districts using the Business Objects report [28].
In order to model the possible effects of implementing the strategy, four scenarios were developed (Table 1).

The Scope of the Scenarios in the Adopted Models Are as Follows

Scenario S1—10% of the land area in each forest district was taken out of use (i.e., selected for protection). First, the oldest stands were identified and selected for protection, and then the remaining areas identified for possible felling were determined. Felling included clear cutting (cutting all trees in a mature stand in one cut), complex felling (partial, nest, gradual, and clear cutting), or late maintenance cutting for “late thinnings”, which were used in maturing stands (TP). The corresponding impact on land use was also considered.
Scenario S2—10% of the State Forests was taken out of use based on age of stand without regard to forest district boundaries. The model determined which districts would be most impacted economically and which would experience the most significant reduction in wood harvesting. Areas for exclusion were ranked by age in descending order and selected until the 10% threshold target was met (the minimum age of the stand is 106 years). Then, areas eligible for clear cutting, complex felling, or maintenance thinning (MT) were determined for each forest district. The impacted stands’ reserve, protective, and economic value were also determined and defined.
In scenarios S3 and S4, the most important criterion for increasing biodiversity was not the age criterion but the criterion of natural richness represented by diagnosed natural habitats in the area of the Regional Directorate of State Forests in Poznan (scenario S3) and additionally by established protected areas, reserves and Natura 2000 network areas (scenario S4).
Scenario S3—The set-aside of forest belonging to natural habitats of European importance is defined in Annex No. 1 to the Regulation of the Minister of Environment (13 April 2010) on natural habitats and species of community interest as well as areas eligible for recognition or designation as Natura 2000 areas [29]. These exclusions were uneven distributed across forest districts regardless of the age criterion and location in Natura 2000 areas [30]. The goal of 10% strict protection has not yet been achieved so that further areas eligible for protection were assigned. Each forest district’s share of the total protected area was calculated. Furthermore, the age range of stands for a given natural habitat was determined as well as areas eligible for clear cutting, complex cutting, and late thinning. The impacted stands’ reserve, protective, and economic value were also determined and defined.
Scenario S4—protected areas were selected irrespective of the forest districts’ boundaries or stand age based on the following ranking. First, reserves outside the Natura 2000 network were selected, which were followed by reserves within Natura 2000 areas and all-natural habitats regardless of standard age. The cumulative land area designated in this way did not reach the 10% threshold. Therefore, the oldest stands in Natura 2000 areas were also selected. Based on these selection criteria, each forest district’s share of protected areas was estimated as well as each forest district’s share of areas eligible for clear cutting, complex felling, and late thinning.
To analyze the impact of the scenarios instructed before, the economic impact was determined by estimating the volume of wood (roundwood with a diameter of at least 7 cm over bark and 5 cm under bark at the thinner end) that would no longer be harvested as part of any planned economic activities specified in the forest management plans. Based on operational data [31], thinning intensity (maintenance cuts), and the adopted average yield in m3/ha (Table 2) [31], this volume of wood was calculated.
In the analyzed scenarios, the volume of harvested wood in individual cuts was determined as follows (1, 2, and 3):
(1) Volume of harvested wood within clear-cutting Vc:
Vc = A × Y × 95% *
  • A—area [ha];
  • Y—average yield [m3/ha];
  • * average wood extraction in clear-cutting.
(2) Volume of harvested wood within complex cuttings for separation Vs:
Vs = A × Y × 50% **
  • A—area [ha];
  • Y—average yield [m3/ha];
  • ** average wood collection in complex cuttings (taking into account nest, partial, cleaning cuts, etc.).
(3) Volume of harvested wood within late thinnings Vt:
Vt = A × It
  • A—area [ha];
  • It—thinning intensity specified in the PUL [m3/ha].
A statistical analysis was conducted for each of the four scenarios, considering six variables, to assess whether and how the scenario results differ from each other. We compare the means of the following variables:
  • The volume of large wood (m3) from prevent final felling;
  • The volume of large wood (m3) from prevent pre-final felling;
  • Percentage share of pre-final felling;
  • 10-year final felling (m3) after reduction;
  • 10-year pre-final feeling max. (m3) after reduction.
For this purpose, we use the mean analysis of variance (ANOVA) to determine whether the average values of individual variables differ significantly among scenarios S1–S4. Tukey’s post hoc test was used to identify which specific groups differ significantly after ANOVA showed overall differences. The conformity to the normal distribution of the analyzed characteristics was verified with the Kolomogorov–Smirnov test. The level of statistical significance was assumed to be α = 0.05. All calculations were performed using Statistica 13 © Copyright StatSoft Polska (Kraków, Poland).

3. Results

In implementing part of the provisions (strict protection of 10% to the area of forests in Poland) of the EU Biodiversity Strategy for 2030 [25] and taking into account the assumptions of this study, it is possible to indicate clear consequences of introducing this directive in the form of a reduction in the applicable harvesting quotas for individual forest districts. In scenario S1, the most significant reduction in the harvesting quota (>40%) was observed in four “disaster” forest districts (areas where significant windfalls were taken in 2019), where the average age of stands had dropped drastically to 53 years. In scenario S2, the most significant reduction in the harvesting quota (>40%) was observed in forest districts characterized by higher-quality (species, volume, and age) stands and a large number of natural habitats. A comparison of scenarios S1 and S2 indicates that equal treatment of forest districts (scenario S1) is much less effective at implementing the environmental goals of the strategy. As a result, the S1 simulation will often protect poor stands growing on soils characterized by low-quality classes. Consequently, protecting lower-quality stands will not increase biodiversity.
Moreover, scenario S2, in which the oldest stands will be strictly protected, determined that the largest area of these stands is located in the Krotoszyn Forest District. This area covers 5183.16 ha, which is 27.7% of the total forest district area or 12.3% of the Regional Directorate of State Forest area. The protected area accounts for 239,038 m3 of roundwood. A significant share of the oldest stands is found in the so-called Krotoszyn Plate. Here, the dominant forest species is oak, which reaches felling maturity at 140–180 years. The oldest, most valuable, and most desirable oak stand, which grows in the specific conditions of the Krotoszyn Plate, is currently exposed to loss of stability and deadwood. This area is unique to Europe and valuable in nature and the environment. Not protecting this stand would be counterproductive to meeting the goals and spirit of the strategy.
In scenarios S3 and S4, the most important criterion for increasing biodiversity was not the age criterion but the criterion of natural richness represented by diagnosed natural habitats in the area of the Regional Directorate of State Forests in Poznan (scenario S3) and additionally by established protected areas, reserves and Natura 2000 network areas (scenario S4). A comparison of the scenarios shows that the selection of natural habitats for strict protection does not drastically affect the volume of unharvested wood that now is not harvested due to the implementation of protection goals (Figure 2). It is the S3 scenario that guarantees lower economic costs while meeting EU criteria. The choice of this scenario is also related to adaptation to the structure of forests in Poland.
A statistical analysis was conducted for each of the four scenarios, considering six variables. Based on Table 3, the coefficient of variation varies among all scenarios, particularly for Scenario 3, with significant variances for all 25 forest districts.
The first scenario, S1, has low variability for the studied forest districts with strong higher variability for S2. Scenario S3 has significant differences in variance. Features and their average values differ significantly between scenarios S1–S4 regarding roundwood (m3) volume from felling cuts.
The minor reduction in roundwood from final cutting (Figure 3a) was confirmed for scenario S3 (p = 0.000), and a similar trend was determined for the volume of roundwood (m3) from selected protection areas (p < 0.000). No significant difference exists between S1 and S2 and S3 and S4, indicating that scenario S3 is the best selection for protecting the environment and forests.
The change in the level of felling cuts (Figure 3b) was statistically significant between groups S1 and S2 and S3 and S4 (p = 0.000).
Excluding pre-final cutting (Figure 3c) from economic use results in a comparable increase in wood harvest in scenarios S3 and S4. Significant differences between scenarios for the premature felling of large-dimensional timber were indicated for the pairs of scenarios S1 and S2 and S3 and S4 but also for scenarios S2 and S4.
The percentage change in the pre-existing cuts (Figure 3d) for the tested scenarios results in a similar lack of significant difference for S1 and S2 and S3 and S4 (p = 0.001).
The tested scenarios’ harvesting stages (Figure 4a) lack no significant difference for S1 and S2 and S3 and S4. Pre-separate positions over 10 years by scenario (Figure 4b) after reduction indicate no significant differences in assessing the impact of harvesting (p = 0.100). This suggests that the effect of scenario selection on long-term harvesting results fades over time.
The comparison of scenarios (Figure 4c) indicates the similarity of numerical values for S1 and S2 (up to 10% difference) and S3 and S4 (similar at about 30%) and similarly in similarity within blocks.

4. Discussion

The EU Biodiversity Strategy for 2030 [25] highlights the need for expanding the current network of protected areas, including strictly protected areas. To address this, the European Commission has set ambitious targets for Member States to increase protected areas to at least 30% of Europe’s land area, of which one third should be strictly protected. However, finding sustainable solutions to conflicts between conservation and other socioeconomic objectives will be crucial in this respect [10].
This strategy raises controversy regarding biodiversity protection in the context of other forest ecosystem services [33,34,35,36,37,38,39]. There may be some economic consequences for the forest sector and the wood industry [40]. Integrating biodiversity with ecosystem services aims to improve human well-being and the state of the environment as well as the profitability of pro-ecological investments [41,42,43]. Effective conservation planning maximizes benefits while minimizing impacts on other resources, such as the availability of productive land. If EU policies restrict future wood production from European forests in the face of growing demand, this could negatively impact domestic and international markets [44,45]. Experimental studies conducted in Germany [40] on the economic effects of implementing the strategy show that in a simulated period of 200 years, the volume of wood harvesting will decrease by 13% and 44%. According to Schier et al. [44], implementing the strategy would allow for maximum roundwood production in the EU of around 281 million m3 in 2030 in the intensive scenario and 490 million m3 in the moderate scenario. Since in the reference scenario, the production of roundwood in the EU amounts to 539 million m3, this means reductions of −48% and −9% in 2030, respectively. By 2050, production will continue to decrease and reach 58% and 10% of the reference production. On a global scale, the EU’s roundwood production deficit is partially compensated, approximately 50–60%, by increased roundwood production in non-EU countries, e.g., the USA, Russia, Canada, China, and Brazil.
The range of potential restrictions on forest use have numerous negative impacts, including increased prices for wood products and job losses. The reduced production and consumption of wood products could increase the use of non-biological resources to replace wood products. Reducing wood harvesting in Europe may also increase the share of non-renewable energy sources [46,47]. As a result, this would be at odds with the aim of the EU Bioeconomy Strategy and its accompanying climate protection goals [44]. Spatial conservation prioritization, based on complementarity, could support the identification of areas where restrictions on wood harvesting are both ecologically necessary and socioeconomically feasible, helping to balance conservation objectives with economic impacts [48,49]. Cerullo et al. [50] draw attention to the risk that EU restrictions on wood harvesting may shift wood production to tropical regions. Therefore, EU actions are necessary to prevent deforestation in other parts of the world and biodiversity loss in third-world countries.
In Poland, nature conservation areas already constitute over 30% of the land area, which means that additional actions to adapt Polish forestry to the requirements of the strategy are optional. However, within the strategy, there is an ongoing discussion on the potential benefits and losses in Polish and European forestry, taking into account the issues of nature conservation, forest sustainability, and the importance of the wood industry for the economy [51,52,53,54].
The presented scenarios show the potential consequences during the achievement of the goal of strict protection of forests areas. Covering the oldest stands with protection in Poland will significantly reduce the volume of unharvested wood in scenarios S1 and S2 compared to scenarios S3 and S4, in which stands will be covered with passive protection regardless of age class. In the oldest stands, both economic and protective final felling is carried out, which is associated with a significant (95% in clear cutting and approx. 50% in complex felling) harvesting of wood volume.
The optimal variant for implementing the provisions of the EU regarding biodiversity protection is the selection of natural habitats consisting of the most valuable tree stands, which are characterized by the most valuable biodiversity, age diversity, and uneven distribution in the studied area (scenario S3). In the S4 scenario, passive protection will cover the most valuable natural habitats in protected areas. Still, at the European Union level, not all valuable natural tree stands will be subject to protection. Because artificial compartments are created that do not reflect reality, some valuable ecosystems are located outside the Natura 2000 network.
Schematic and imposed solutions regarding the designation of tree stands for strict protection, without taking into account many factors such as the sanitary condition of the forest, the profitability of units, the location of forest areas about cities, and social expectations, will not bring the expected results of the EU directive scenario.

5. Conclusions

A goal of the 2030 European Strategy for Biodiversity is to exclude land areas from use and place them under strict protection. Forests will be high-priority areas for strict protection under the strategy. Forest managers will face an increasing challenge of correctly indicating forest areas for strict protection. Transforming forest management in a direction consistent with the assumptions of the strategy should meet both biodiversity and socioeconomic requirements. Regarding socioeconomic development, the proposed changes should support economic growth, sustainable supply chains and business models, and the creation of new jobs and technological development.
Developing an implementation model that considers many variables, including arbitrary governance boundaries and high-quality environmental areas, would facilitate maximum functionality of forest management from both an ecological and societal perspective. Introducing a systemic model can help optimize procedures for identifying and protecting forest areas while monitoring costs and potential changes in supply chains related to forestry.
The results serve as a basis for recommending further research to exclude specific forest areas from economic production. Recommended are natural habitats scattered in and outside Natura 2000 areas. It is important to emphasize the importance of identifying high-value stands before developing forest management practices, allowing for better qualification and quantification of areas of low industrial value but of high importance for biodiversity.

Author Contributions

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

Funding

The publication of this study was financed by the Polish Minister of Science and Higher Education as part of the Strategy of the Poznan University of Life Sciences for 2024–2026 in the field of improving scientific research and development work in priority research areas.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data were included in the article.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Location of the Poznan Regional Directorate of State Forests [26].
Figure 1. Location of the Poznan Regional Directorate of State Forests [26].
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Figure 2. Estimated volume of unharvested wood according to S1–S4 scenarios [m3].
Figure 2. Estimated volume of unharvested wood according to S1–S4 scenarios [m3].
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Figure 3. Scenario S1–S4: (a) volume of wood shutdown from final cutting; (b) percentage of the final cutting; (c) volume of wood (m3) shutdown from pre-final cutting; (d) percentage of pre-final cutting. Letter description indicates the absence of significant differences (a a; b b) or significant differences in results (a b; a c; b c).
Figure 3. Scenario S1–S4: (a) volume of wood shutdown from final cutting; (b) percentage of the final cutting; (c) volume of wood (m3) shutdown from pre-final cutting; (d) percentage of pre-final cutting. Letter description indicates the absence of significant differences (a a; b b) or significant differences in results (a b; a c; b c).
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Figure 4. Interdependence of the scenarios S1–S4: (a) on possibly of intermediate cutting for 10 years max. (m3) after reduction; (b) on pre-separate positions over a 10-year period; (c) vectors in value comparison.
Figure 4. Interdependence of the scenarios S1–S4: (a) on possibly of intermediate cutting for 10 years max. (m3) after reduction; (b) on pre-separate positions over a 10-year period; (c) vectors in value comparison.
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Table 1. Applied modeling scenarios.
Table 1. Applied modeling scenarios.
ScenariosType of Forest Exclusion from Economic Use
S1Equally set-aside 10% of the forest area in each forest district
S2Set-aside 10% of forest area based on the age of stands disregarding forest districts’ boundaries
S3Set-aside of natural habitats scattered in and outside Natura 2000 areas
S4Only Natura 2000 and attached reserves are set-aside
Table 2. Average stand volume by age classes and dominant species in Poland (m3/ha) [32].
Table 2. Average stand volume by age classes and dominant species in Poland (m3/ha) [32].
The Dominant SpeciesAverage Yield in m3/ha
On Non-Forested Forest LandsAge Class *Total Age Class on Forest Lands Covered with WoodTotal on the Forest Area
IIIIIIIVVVIVII and Older
Total169146295341380408418275270
Pine137165303342375397389279274
Spruce1911158339428459465425297293
Fir1711108327422468481476368365
Beech18751234349408450469263261
Oak21887251317366405435241236
Hornbeam3810111225288336373380264263
Birch2133152238287294293323231228
Alder2258180279332382406406269250
Poplar26115307321338352291242274269
Axen3313129252316389433413273273
* Age class: I—up to 20 years old, II—21–40, III—41–60, IV—61–80, V—81–100, VI—101–120, VII—121 and olden.
Table 3. Aggregated results of descriptive statistics for scenarios S1–S4 (N valid = 25).
Table 3. Aggregated results of descriptive statistics for scenarios S1–S4 (N valid = 25).
VariableDescriptive Statistics
ScenarioMinimumMaximumMeanStd DevVariance Factor
Volumes of wood (m3) from shutdown final cuttingS163,457.0275,963.0134,020.152,616.939.3
Volumes of wood (m3) from shutdown pre-final cuttingS1184.027,195.012,033.06420.353.4
10-year prescribed final cutting (m3) after reductionS1165,290.0605,731.0338,117.1125,998.637.3
Possibly of intermediate cutting for 10 years max. (m3) after reductionS1209,707.0563,476.0362,378.2105,023.829.0
Volumes of wood (m3) from shutdown final cuttingS229,946.0305,138.0123,141.266,587.554.1
Volumes of wood (m3) from shutdown pre-final cuttingS281.079,508.013,968.616,267.8116.5
10-year prescribed final cutting (m3) after reductionS2179,885.0735,807.0348,996.0144,503.741.4
Possibly of intermediate cutting for 10 years max. (m3) after reductionS2206,357.0571,640.0360,442.6107,450.629.8
Volumes of wood (m3) from shutdown final cuttingS33245.0152,057.043,952.636,463.183.0
Volumes of wood (m3) from shutdown pre-final cuttingS32775.0130,000.037,577.131,173.983.0
10-year prescribed final cutting (m3) after reductionS3212,876.0777,749.0428,184.5154,762.936.1
Possibly of intermediate cutting for 10 years max. (m3) after reductionS3186,349.0568,678.0336,834.1107,958.632.1
Volumes of wood (m3) from shutdown final cuttingS4707.0310,202.072,205.087,317.9120.9
Volumes of wood (m3) from shutdown pre-final cuttingS4318.0139,771.032,534.239,343.9120.9
10-year prescribed final cutting (m3) after reductionS4234,091.0787,975.0399,932.1154,636.038.7
Possibly of intermediate cutting for 10 years max. (m3) after reductionS4103,216.0553,196.0341,876.9116,706.634.1
Source: own research.
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Giełda-Pinas, K.; Starosta-Grala, M.; Wieruszewski, M.; Dynowska, J.; Molińska-Glura, M.; Adamowicz, K. Modeling the Effects of Strict Protection of Forest Areas—Part of the Provisions of the EU Biodiversity Strategy 2030. Sustainability 2025, 17, 737. https://doi.org/10.3390/su17020737

AMA Style

Giełda-Pinas K, Starosta-Grala M, Wieruszewski M, Dynowska J, Molińska-Glura M, Adamowicz K. Modeling the Effects of Strict Protection of Forest Areas—Part of the Provisions of the EU Biodiversity Strategy 2030. Sustainability. 2025; 17(2):737. https://doi.org/10.3390/su17020737

Chicago/Turabian Style

Giełda-Pinas, Katarzyna, Monika Starosta-Grala, Marek Wieruszewski, Joanna Dynowska, Marta Molińska-Glura, and Krzysztof Adamowicz. 2025. "Modeling the Effects of Strict Protection of Forest Areas—Part of the Provisions of the EU Biodiversity Strategy 2030" Sustainability 17, no. 2: 737. https://doi.org/10.3390/su17020737

APA Style

Giełda-Pinas, K., Starosta-Grala, M., Wieruszewski, M., Dynowska, J., Molińska-Glura, M., & Adamowicz, K. (2025). Modeling the Effects of Strict Protection of Forest Areas—Part of the Provisions of the EU Biodiversity Strategy 2030. Sustainability, 17(2), 737. https://doi.org/10.3390/su17020737

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