Filters
Forest Biomass in Bioenergy Production in the Changing Geopolitical Environment of the EU
The article examines the potential utilization of forest biomass in bioenergy production in Europe, taking into account limiting and developmental factors. The methodology includes a strategic analysis and the use of PEST analysis to evaluate the market for wood biomass. In the context of the current geopolitical situation and the decarbonization goals of the EU, the authors recommend accelerating energy transformation and highlighting forest biomass as an alternative within renewable energy sources. A literature review indicates the need to revise EU assumptions to enable the use of wood for bioenergy production, taking into account the needs of the wood industry. The analysis of economic factors shows competitiveness of forest biomass against coal, yet challenges arise regarding resource availability and competition with other energy sources. Emphasis is placed on the necessity of sustainable forest resource management and technological innovation. In the context of an energy crisis, the article underscores the role of innovation and recycling in alleviating shortages in energy markets. Conclusions highlight the imperative to develop a sustainable energy strategy for forest resource management and engage EU countries in the development of new biofuel and renewable energy sources for energy security and environmental protection.
Prediction of costs of selected silvicultural treatments by linear approximation and the Holt-Winters model
Biomass Price Prediction Based on the Example of Poland
The aim of the study was to test the applicability of forecasting in the analysis of the variability of prices and supply of wood in Poland. It relies on the autoregressive integrated model (ARIMA) that takes into account the level of cyclic, seasonal, and irregular fluctuations and the long-term trend as tools for the assessment of the predictions of the prices of selected medium-sized wood assortments. Elements of the time series were determined taking into account the cyclical character of the quarterly distribution. The data included quarterly information about the supply (amount) and prices (value) of wood sold by state forests in the years 2018–2022. The analysis was conducted for the most popular assortments: logging slash (M2, M2ZE), firewood S4, and medium-sized wood S2AP. In the period studied (years 2018–2022), the average rate of price variation was widely scattered. The average rate of price variation for the M2ZE assortment amounted to 7%. The average rate for M2 assortment was 1%, while the medium-sized S2AP assortment displayed the greatest variation of 99%. This means that between 2018 and the present, the price increased by nearly 100%. No major fluctuations were observed for the S4 assortment and its average rate of variation amounted to 0%. The analysis found seasonal variation was observed only for S4 firewood, the price of which went up each year in October, November, and December. For this reason, the forecast was made with the seasonal autoregressive integrated moving average (SARIMA) version of the model. It is difficult to forecast the price of wood due to variations in the market and the impact of global factors related to fluctuations in supply.
Predicting Post-Production Biomass Prices
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%.