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.

This paper presents the application of prediction in the analysis of market price volatility in Polish conditions of wood processing by-products in the form of biomass. The ARIMA model, which takes into account cyclical, seasonal, irregular fluctuations of historical data on the basis of which the forecast and long-term trends of selected wood products were made, was used in predicting prices. Comparisons were made between the ARIMA prediction method and the multiplicative Winters–Holt model. During the period studied (2017–2022), the changes in the market price of biomass were characterized by a wide spread of values. On average, the price of these products increased from 2017 to the end of 2022 by 125%. The price prediction analysis showed seasonal fluctuations in the case of wood chips. The uncertainty in price prediction is due to changes in supply resulting from the influence of global factors. The Diebold–Mariano test of matching accuracy confirms that the price prediction of the analyzed by-product sorts using the ARIMA and WH models is possible. The conclusion reached by comparing these two methods is that each can be used under certain market conditions of certain assortments. In the case of a stable wood product, the choice of the ARIMA model should be resolved, while in the case of price volatile products, WH will be a better choice. The difference between the predicted and actual price with ARIMA ranged from 2.4% to 11.6% and for WH from 3.7% to 29.8%.