Balancing Forest Regulations and Stakeholder Needs in Latvia: Modeling the Long-Term Impacts of Forest Management Strategies on Standing Volume and Carbon Storage

Abstract

Forest ecosystems are significant carbon pools on a global scale, and also a source of renewable raw materials. Moreover, the European Union (EU) aims to tackle climate change and reach climate neutrality; therefore, forest regulations are designed to promote sustainable forest management practices and ensure the long-term health and productivity of forests. It is important to balance regulatory requirements with the economic, social, and environmental needs of forest stakeholders. This study analyses four theoretical scenarios (business as usual, green deal, intensive forestry, and intensive forestry with afforestation) and prognoses the management impact on standing volume and carbon stock in living trees and harvested wood products (HWPs). Thus, the aim of this study is to evaluate different theoretical forest management scenarios to predict changes in standing volume and carbon stock in living tree biomass and HWPs for the 100 next years. The results suggest that intensive targeted forestry practices may enhance carbon sequestration and were found to be the most suitable strategy for Latvia’s hemiboreal zone, as they balance economic benefits with carbon sequestration and ecosystem services. The obtained results can be valuable for policymakers and forest managers to promote sustainability and balance the diverse needs of society and forest stakeholders.

1. Introduction

Forestry has traditionally been a source of renewable raw materials, household fuelwood, and, more recently, biofuels for various industries [1,2]. The European Union (EU) forest regulations cover a wide range of activities related to forest management, including logging, replanting, and forest protection, focusing on several key areas, such as increasing the size, quality, and vitality of the Europe’s forests; enhancing forest resilience to climate change; and improving forest-based livelihoods and rural development. The primary goal of forestry regulations in Europe is to promote sustainable forest management practices [3,4]. The Europe Land Use, Land Use Change, and Forestry (LULUCF) regulation, under its 2030 Climate and Energy Framework, is designed to promote sustainable forest management practices and ensure the long-term health and productivity of forests [5]. This involves balancing the economic, social, and environmental benefits of forestry, while ensuring that the forest ecosystem is preserved for future generations. The preservation of natural habitats and promotion of diverse tree species growth, facilitated by EU forest regulations, serve as a critical measures for the conservation of biodiversity [6]. Forestry regulations in the EU play an important role in addressing climate change by promoting carbon sequestration in forests and reducing greenhouse gas emissions from forest management activities [5,7].

Harvested wood products (HWPs) are a significant contributor to the global forest product industry and have been recognized as a crucial resource for achieving sustainable development goals [8,9]. Utilization of HWPs has become increasingly important for climate change mitigation through carbon storage, substitution of carbon-intensive materials, and sustainable forest management practices. Harvested wood products, particularly in long-lived products such as building materials, possess considerable carbon storage potential, leading to a reduction in atmospheric carbon dioxide [10,11,12]. Furthermore, the replacement of steel and concrete with HWPs during production and construction can significantly reduce carbon emissions and contribute to the substitution effect [10]. Demand for HWPs can also promote sustainable forest management practices such as reforestation, afforestation, and forest certification, providing economic incentives for sustainable forestry. These attributes of HWPs have made them a fundamental component of a circular bioeconomy, as they offer opportunities for reuse, recycling, and energy recovery. However, the in production and use of HWPs, their environmental and social impacts must be considered to ensure they contribute to sustainable development.

While there is a list of benefits in land use and forestry regulation for forestry in the EU, there are some concerns in regulatory requirements regarding the economic, social, and environmental needs for forest stakeholders [13]. Compliance with forestry regulations can be expensive for forest owners and managers, particularly for small-scale operators who may not have the resources to meet regulatory requirements. Additionally, forest management is impacted by EU-level policies, which may have overlapping or competing objectives [14]. Implementing a forest strategy at the EU level requires careful consideration of various national and international policies and strategies that may hinder compliance efforts [15]. Forestry regulations also can affect the competitiveness of forest products in international markets, particularly if other countries do not have similar regulations in place. Additionally, in the context of the narratives on forests, several issues highlighted by researchers of EU forest policy in the past 20 to 30 years, including inadequate coordination and integration, persist to this day [15,16,17,18]. Overall, land use and forestry regulation for forestry in the EU play an important role in promoting sustainable forest management practices and ensuring the long-term health and productivity of forest ecosystems [5,7].

In the scientific community, recent focus has aimed to evaluate sustainable and climate smart forestry practices and possibilities to ensure carbon sequestration, storage, and substitution to address the climate crisis [19]. Studies of boreal and temperate regions have evaluated climate change mitigation potential with ecosystem-based forest management and concluded that management can increase the ecosystems’ carbon sink, but in boreal and temperate regions, climate warming would result in a shift to more hardwood tree species, which would require increased capacity of processing long-lived hardwood products [20]. Therefore, to effectively implement EU-wide sustainable forest management actions, there is a need to evaluate regionally and locally adapted management approaches that consider different ownership structures, interests, policies, and values in relation to climate change mitigation [15]. Moreover, it is important to balance regulatory requirements with the economic, social, and environmental needs of forest stakeholders. Therefore, the aim of this study was to generate and evaluate various theoretical forest management scenarios to predict changes in standing volume and carbon stock including living tree biomass and HWPs for the next 100 years in hemiboreal regions. Regionally generated and evaluated theoretical forest management scenarios can provide valuable insights to forest managers and policymakers for effective decision-making related to sustainable utilization and conservation of forests. Moreover, different functions of forest development trajectories and strategies compared on a time scale has good application in combination with neural networks to generate a country-scale forest management effect on climate change mitigation and carbon sequestration.

2. Materials and Methods

2.1. Study Area

This study was conducted in the hemiboreal biome across the territory of Latvia, characterized by a temperate climate, strongly influenced by the Baltic Sea and the North Atlantic. The average annual precipitation in the study area is 692 mm, with an average annual air temperature of +6.4 °C. February is the coldest month of the year, with an average air temperature of approximately −3.7 °C, while July is the warmest month of the year, with an average air temperature of +17.4 °C according to the Latvian Environment Geology and Meteorology Centre.

2.2. Dataset

To evaluate forest ecosystem changes based on various management practices, National Forest inventory (NFI) data were used in the analysis defining various theoretical forestry scenarios. The dataset comprises all forest land types in Latvia, characterizes current forest structure, regeneration and harvesting tendencies, and management practices, covering a total area of 3298 thousand hectares. The initial state of forests are in accordance with the current situation in Latvia. The sample dataset consists of 6640 plots (strata), of which 3205 are state forests, and the remaining 3445 are other forests. Tree species change after main felling is based on current management practices and planting tendencies, including natural regeneration. Detailed information about the modelling approach is available in the national forest accounting plan [21].

2.3. Simulated Forest Managament Scenarios

In total, four theoretical scenarios were opted for analysis (Table S1):

• Business as usual (BAU) represents the scenario where modelling was carried out according to the current forestry practice, regulation, and the current behavior of the forest owners in Latvia, which was valid until June 2022. The scenario does not model the change in economic activity restrictions and the increase or decrease in forest areas. The final harvest method is regeneration cut and typical management practices include precommercial thinning and one to three thinnings before the final harvest. Rotation period for coniferous (Norway spruce (Picea abies L.), Scots pine (Pinus sylvestris L. Karst.)) is 81 and 101 years, respectively, but for deciduous trees (birch (Betula spp.)), it is 71 years. In state forests, only regeneration cut by age is modelled; however, for private forest owners, it is assumed that 85% of forests are harvested by age, but 15% by target diameter (pine—30 cm; spruce—26 cm; birch—25 cm). Unmanaged forest area is 7.5% based on current proportion of areas restricted for management.
• Green deal (GD) scenario includes forest resource modelling in accordance with the new (after June 2022) regulatory framework. Green deal is a European Union (EU) forest strategy that includes a comprehensive plan to transform the region’s economy into a more sustainable one with the goal of achieving climate neutrality by 2050 [22,23]. According to the EU biodiversity strategy for 2030 and EU forest strategy for 2030, the GD scenario entailed modifications in forestry restrictions, wherein 15% of forests were designated as non-cut areas, 30% were modelled as non-clearcut forestry (shelterwood method), and 55% were modelled according to the BAU scenario. This scenario included afforestation of 23 thousand hectares of forest within the initial five-year period.
• Intensive targeted forestry (ITF) scenario aims to increase forest productivity. Forest resource modelling was based on the scientist recommendations: timely and more intensive thinnings, forest fertilization, drainage system establishment, and the total volume of harvested wood in each year in the main felling modelled as 50% from prognosed annual standing volume increment at country scale. More timely and intensive thinnings (one to three) at younger ages than current management practice to create stands with lower density. In this scenario, simulated forest fertilization was modelled after thinnings in pine, spruce, and birch stands where mean diameter at breast height (DBH) >16 cm. During the first 10 years of forest growth, drainage system establishment in periodically waterlogged forests with fertile soils is modelled in the area of 200 thousand hectares. The area of forests does not change, and there are no limitations according to the change in economic activity restrictions.
• Intensive targeted forestry with afforestation (ITFA) scenario aims to increase forest productivity and net present value. Forest resource modelling and management practices are based on scientist recommendations as in the ITF scenario, but intensive and timely commercial thinnings in five-year period proportion of thinned stands is 10% higher than in ITF scenario. The establishment of forest amelioration system is modelled in periodically waterlogged forests with fertile soils (in the area of 240 thousand hectares). The annual volume of harvested wood in the main felling was modelled as 55% from predicted annual standing volume increment. In this scenario, afforestation with selected planting material was included during the first 10 years (in the area of 100 thousand hectares).

2.4. Growth Rate Models

The modeling of changes in the tree stand parameters was conducted at the forest element level, where a group of trees belonging to the same species, generation, and occupying the same forest floor (tree layer) are considered as one unit. The modeling process of changes in forest resources was performed in discrete five-year intervals. Deterministic growth models developed by Latvian State Forest Research Institute “Silava” were used in this study with the latest growth rate equations and coefficient values developed in 2021 [24,25].

The previously developed Hossfeld IV equation [26] of the generalized algebraic difference approach model [27] was used to approximate the average height of the forest element:

$$H_2=1.3+\frac{A_2^{{\alpha }_1}}{{\alpha }_2+100 \ast {\alpha }_3\ast {\chi }_0+{\chi }_0\ast A_1^{{\alpha }_1}}$$

(1)

$${\chi }_0=\frac{\frac{A_1^{{\alpha }_1}}{H_1-1.3}-{\alpha }_2}{100\ast {\alpha }_3+A_1^{{\alpha }_1}}$$

(2)

where $H_2$ is the tree mean height (m) at the end of the period, $H_1$ is the tree mean height (m) at the beginning of the period, $A_1$ is the age (years) of the forest element at the beginning of the period, but $A_2$ is the age (years) of the forest element at the end of the period; ${\alpha }_{1-3}$ are the empirical coefficients [28]. Hossfeld IV equation [26] of the generalized algebraic difference approach model was used to calculate the average diameter of the forest element, additionally including relative stand density:

$$D_2=1.3+\frac{A_2^{{\alpha }_1}}{{\alpha }_2\ast \frac{N_1}{N_{max}}+100\ast {\alpha }_3\ast {{\rm X}}_0+{{\rm X}}_0\ast A_2^{{\alpha }_1}}$$

(3)

$${\chi }_0=\frac{\frac{A_2^{{\alpha }_1}}{D_1-1.3}-{\alpha }_2\ast \frac{N_1}{N_{max}}}{100\ast {\alpha }_3+A_2^{{\alpha }_1}}$$

(4)

$$N_{max}=\sum i{p}_i\ast n_{max i}$$

(5)

$$n_{max}={\beta }_1\ast D_1^{{\beta }_2}\ast H_1^{{\beta }_3}$$

(6)

where $D_2$ is the tree mean diameter (cm) at breast height at the end of the period, $D_1$ is the tree mean diameter (cm) at breast height at the beginning of the period, $A_1$ is the age (years) of the forest element at the beginning of the period, but $A_2$ is the age (years) of the forest element at the end of the period, $N_1$ is the stand number of the trees (ha⁻¹) at the beginning of the period, $N_{max}$ is stand maximal number of the trees (ha⁻¹) at the beginning of the period, $n_{max}$, element stand number of the trees (ha⁻¹) at the beginning of the period, $ip$ is the element proportion of the element, $H_1$ is the tree mean height (m) at the beginning of the period, ${\alpha }_{1-3}$; ${\beta }_{1-3}$ are the tree species specific coefficients [27]. Stand volume was calculated according to Liepa 1996 equation [29]:

$$m=\psi \ast h^{\alpha }{\ast d}^{(\beta \ast \log 10\left(h\right)+\phi )}\ast n$$

(7)

where $m$ is the stand volume (m³ ha⁻¹) of the forest element, $h$ is the height of the forest element, $d$ is the diameter at breast height of the forest element, $n$ is the number of trees (ha⁻¹) of the forest element, $\psi , \alpha ,\beta , \phi$ are the coefficients according to Liepa 1996 [29].

Fertilization effect modelling assumes that fertilization improves forest element growth for 10 years. Effect of wood ash application is determined to improve birch growing stock by 1.5–2 m³ ha⁻¹ yr⁻¹ and 2–2.5 m³ ha⁻¹ yr⁻¹ for pine and spruce. Forest element increment after fertilization is modelled as relative additional increment:

$$Z_m=\frac{\left(100+p_m\right)\ast z_i}{100}$$

(8)

where $Z_m$ is the increment after fertilization, $Z_i$ is the predicted increment without fertilization, $p_m$ is relative additional increment (%).

2.5. Carbon Evaluation

Carbon (C) in living tree biomass for individual trees was determined by multiplying living tree biomass (calculated according to local tree species specific biomass equation [30]) with the carbon content, which is assumed to be 50% [31,32].

To evaluate carbon stock between scenarios, all harvested timber was divided in assortment classes: saw logs, pulpwood, and firewood. The structural arrangement of timber, which encompasses the proportion of saw logs, pulpwood, and firewood (a fuel feedstock derived from tree stems), was determined through the application of Ozolins’ methodology, which relied on tree dimensions as a primary input variable [33]. Carbon stock present in HWPs is reliant on several components, such as the product’s end use, decomposition rate, harvesting and manufacturing emissions, and substitution effects (

Table 1. The decomposition rate and substitution rate of different product categories.

Product Category	Decomposition Rate (Base) 1	Decomposition Rate (Optimal)	Substitution Rate (Base) 2	Substitution Rate (Optimal)
Sawn wood, plywood	0.02 2	0.006 1	1	1
Mechanical mass	0.1 2	0.1 1	0	0
Chemical mass	0.1 2	0.1 1	0	0
Biofuel	0.3 2	0.3 1	0.8	2.8 3

¹: Karjalainen et al., 1994 [36]; ²: Pukkala 2014 and Lundmark et al., 2016 [35,37]; ³: Leskinen et al., 2018 [38].

). The methodology proposed by Pukkala in 2014 and 2017 was used to estimate carbon stock in HWPs employing decomposition rates published by Karjalainen et al. [34,35,36].

3. Results

3.1. Standing Volume

Four evaluated theoretical scenarios resulted in diverse modelled standing volumes after 100 years. For all scenarios, starting standing volume was 677.1 million m³, from which the management impact on standing volume was evaluated. According to the results of evaluated theoretical scenarios, the highest standing volume after the 100 years was found in the BAU scenario, which showed a similar tendency and standing volume (1.4% difference) to the ITF scenario (Figure 1). The projected standing volume of growing trees in the BAU scenario increased by 83.5 million m³ in 100 years (by 12.3%), reaching 760.5 ± 1.0 million m³. In this scenario, projected standing volume of growing trees in forest stands prognostically increases by 73.6 million m³ during the first 50 years (by 10.9%), but in the next 50 years, it increases by only 9.8 million m³ (by 1.4%). In the ITF scenario, living tree standing volume after 100 years was predicted to reach 750.2 ± 2.6 million m³. In this scenario, during the first 50 years, living tree standing volume was higher than in the BAU scenario; however, in subsequent years, standing volume in this particular situation exhibits a smaller increase compared to the BAU scenario. In the GD scenario, the standing volume of trees after 100 years is predicted to be 731.8 ± 3.3 million m³, thus increased by 54.7 million m³ (by 8.1%). Moreover, standing volume in the GD scenario after 100 years is significantly less (p < 0.05) by 28.7 million m³ (by 3.8%) compared to the BAU scenario. In the GD scenario, a tendency can be observed that in forests where forestry is prohibited, the stock of growing trees initially increases in the first 35 years by 28.1 million m³, but then increases slightly and even decreases in some periods in the further modelled 65 years. In the ITFA scenario, standing volume after 100 years is predicted to reach 680.8 million m³ (+0.6% from the beginning), which is significantly lower (p < 0.05), by 79.7 million m³ (by 10.5%), compared to the BAU scenario.

3.2. Carbon in Living Tree Biomass and Harvested Wood Products

All four evaluated theoretical scenarios showed different carbon stock (CS) based on defined scenario parameters; however, after the 100 year period, the differences between scenarios were not so distinct, except in the ITFA scenario (Figure 2). Starting carbon stock of living tree biomass and HWPs was 251.7 million tons C, from which the management impact on CS was evaluated. During the initial 40-year interval, the highest CS in living tree biomass and HWPs was exhibited by the ITF scenario. The GD and the BAU had similar C stock during the initial 40-year interval and after 100 years. Furthermore, the ITF scenario continued to display the highest C stock, and at the end of the 100 year period reached 310.9 million tons C (23.5% increase from the beginning). After the 100 year period, CS in living tree biomass and HWPs in the BAU scenario reached 308.8 million tons C (22.7% increase from the beginning), which was similar to the GD scenario (309.1 million tons C or 22.8% increase from the beginning). Differences between CS after 100 years were not significant (p > 0.05) between the BAU, GD, and ITF scenarios (0.1% (GD) to 0.7% (ITF) increase compared to BAU). The ITFA scenario showed the lowest carbon stock values in living tree biomass and HWPs (284.5 million tons C), and the differences after the modelled 100 years were statistically significant (p < 0.05) compared to all other scenarios (by 7.9% less compared to the BAU scenario). In the ITFA scenario, maximum C stock was reached after 50 years, but at the end of the 100 year period, C stock had already decreased. After 100 years in the ITFA scenario, CS had increased by 13% compared to CS in the beginning.

4. Discussion

The evaluated theoretical scenarios for projecting the standing volume and carbon stock (C in living tree biomass and in HWP) provide valuable insights into the potential growth of forests under different management strategies. The lowest standing volume after 100 years was predicted in the ITFA scenario (Figure 1). In this scenario, after the 100 years, standing volume almost goes back to initial level (+0.6% from the beginning). By intensively managing forests, there is a risk of reducing the multifunctionality of the forests [39,40], which means that it may not be able to provide as many benefits to society, leading to a long-term loss of ecosystem services; therefore, a balance between economic development and society needs to be found [39,41,42]. Additionally, the ITFA scenario showed significantly lower (by 7.9% less compared to the BAU scenario) carbon storage (in living tree biomass and HWPs) after 100 years (Figure 2). Carbon storage provided by forests and HWPs is an important cornerstone in a circular bioeconomy in the case of reaching EU Climate neutrality by 2050. Our data demonstrate that in the case of carbon stock, the ITFA scenario might not be the most suitable management strategy to fulfill the EU requirements and goals for achieving climate neutrality. Lower standing volume and carbon stock in the ITFA scenario can be explained with a 5% higher harvesting intensity compared to the ITF scenario and a 10% higher proportion of thinned stands than in the ITF scenario. This highlights the importance of adopting sustainable forestry practices with the aim of balancing the needs of economic development with the conservation of forest resources and other ecosystem services.

In the case of changes in standing volume during the 100-year period, the BAU, GD, and ITF scenarios showed similar tendencies. Between the evaluated scenarios, the highest standing volume after the 100-year period was observed in the BAU scenario (Figure 1), and standing volume in the GD, ITF, and ITFA scenarios was significantly lower. However, in the BAU, GD, and ITF scenarios, after 100 years, carbon storage in living tree biomass together with HWPs were similar (Figure 2), showing the highest carbon stock in the ITF scenario. The convergence of carbon storage after 100 years for the BAU, GD, and ITF scenarios could be explained with additionally afforested area and a higher proportion of unmanaged forests where large-dimension trees are the main forest elements in the GD scenario. In the BAU and ITF scenario, carbon stock similarity could be explained with the management effect on C stock variation, as indicated by the slight decrease in C stock in the ITF scenario after year 90, but an increase after 100 years, while in the BAU scenario, a slight C stock increase continues throughout the modelled period. In the ITF scenario, carbon stock with HWPs was notably higher during the beginning of the modelled period, especially in the first 40 years. In this scenario, forestry practices were modelled accordingly with the recommendations provided by scientists—more intensive and timely commercial thinnings than in the BAU scenario. Higher C stock in the ITF scenario could be explained by intensively applied commercial thinning, which increases the proportion of saw logs and amount of harvested long-lived wood products, also mentioned in other studies [34]. Additionally, forest soil fertilization with wood ash was modelled in the ITF scenario. Previous studies show that soil fertilization is a common practice in Scandinavian forests to correct nutrition deficiencies and acidity and has a positive effect on tree growth at the initial stage of development and the C pool in boreal forests [43,44,45,46,47]. However, soil fertilization in forests is not a common and widely used practice in Latvia or the hemiboreal region in general.

In the GD scenario, carbon stock after 100 years was similar to in the BAU scenario, but standing volume in the GD scenario was significantly lower (by 3.8%), which could cause economic losses in forestry. In the GD scenario, 15% of forests are strictly protected and in 30% of forests, clear-cuts are prohibited, therefore the standing volume of living trees initially increases in the first 35 years, but then increases slightly and even decreases in some periods in the further modelled 65 years. Changes in standing volume increase after the first 35 years could be explained by the annual standing volume growth of older forests, which decreases with age, and additional forest element mortality occurs more often compared to young to mature stands [48]. This indicates that large forest conservation areas can provide a short-term positive effect on standing volume increase, as also indicated by other studies of boreal and temperate regions [49,50], but in the long-term, even decreases tree standing volume (Figure 1); however, much is dependent on the scale of the protected area. Additionally, forest owners and managers, especially those operating on a small scale and lacking sufficient resources, may find it costly to adhere to forestry regulations and meet regulatory obligations. It is important to identify a shared factor that aligns economic advantages with carbon sequestration and ecosystem services, while simultaneously avoiding any negative impact on the interests of individual owners of forested lands.

Forest modeling can provide several benefits to the forestry sector such as predictive ability and an improved decision-making process. It helps forest policymakers and forest managers to understand the potential impacts of different management strategies on carbon sequestration and forest productivity. This information could be used to develop more effective forest management policies that balance the economic, social, and environmental needs of forest stakeholders while promoting sustainable forest management practices. On the other hand, we did not include several important factors in our models that can cause forest damage and carbon losses, for instance, unpredictable climatic conditions, potential drought periods, storms and consequently following windthrows, forest fires, pest infestations, and diseases. Additionally, impact and development trajectory of climate change and average temperature increase can largely shape forest growth, productivity, vitality, and carbon sequestration. In boreal and temperate biomes, climate change is associated with the decline in cold-tolerant tree species and shift towards hardwood tree species [20]. A similar tree species shift is predicted for hemiboreal regions where coniferous tree species will shift to the northern region while hardwood tree species such as beech and oak will become more common by the end of the century [51]. Changes in the species composition due to climate change can directly lead to higher greenhouse gas emissions by altering the range of products that can be derived from harvested wood over the course of time. It is essential to acknowledge that the variables used to describe the HWP assortment over time represent a simplified overview of a much more complex reality [20], and the industry must develop together with the forest sector in order to maximize the potential of substitution effect and develop alternatives to greenhouse gas-intensive fossil-based products.

Moreover, our forest models rely on data from hemiboreal biome conditions and are not representative of other biomes. Therefore, it is important to be aware of the limitations and potential biases of the modeling approach and to use it as a tool in conjunction with other sources of information and expertise to inform decision-making. Overall, based on National Forest Inventory data and our models, the ITF scenario could be the most suitable alternative to BAU for forest management practices in Latvia in order to align with the climate change mitigation objectives set by the EU, however additional costs and carbon footprint for management establishment is not included in the analysis. Further studies could focus on improving existing scenarios with climate change predictions (increased storm intensity and frequency, drought periods, fires, and pest infestation) and different climate warming scenario outcomes, and possible species shift in the future for more precise estimates of carbon stock and harvested wood products. Moreover, further studies could focus on substitution effect of long-lived wood products including products from tree species that are predicted to increase due to climate change, as there are still some uncertainties in the scientific community on the substitution effect potential.

5. Conclusions

In conclusion, this study demonstrated the utility of using forest modeling to estimate the potential impacts of different forestry management practices and policy decisions on carbon storage in living tree biomass, harvested wood products, and standing volume. The results suggest that intensive targeted forestry practices may enhance carbon sequestration, as the ITF scenario, which was modelled in accordance with scientific recommendations, was found to be the most suitable alternative strategy for Latvia’s hemiboreal zone, as it can balance economic benefits with carbon sequestration and ecosystem services. Nevertheless, it is important to acknowledge the potential limitations and biases of the modeling approach, particularly with respect to unpredictable climate conditions, pest infections, forest fires, windthrows, and diseases. Therefore, forest modeling should be used in conjunction with other sources of information and expertise to inform decision-making. The insights gained from this study can be valuable for forest policymakers and managers in developing more effective forest management policies that promote sustainability and balance the diverse needs of forest stakeholders and society at large.