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Biomass Price Prediction Based on the Example of Poland

cris.virtual.author-orcid0000-0001-5033-1156
cris.virtual.author-orcid0000-0002-4867-195X
cris.virtual.author-orcid0000-0002-1806-0891
cris.virtual.author-orcid0000-0001-6285-2119
cris.virtual.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtualsource.author-orcid003d1ce3-8b63-4d59-a763-6aefe6bd745b
cris.virtualsource.author-orcid0241aa31-988f-4deb-9623-97f29e43d000
cris.virtualsource.author-orcid6a2f8857-003b-41ec-9112-0ef6941bfd06
cris.virtualsource.author-orcid9ac08db6-b765-47e6-bb26-ca12939523df
cris.virtualsource.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
dc.abstract.enThe 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.
dc.affiliationWydział Leśny i Technologii Drewna
dc.affiliationWydział Rolnictwa, Ogrodnictwa i Bioinżynierii
dc.affiliation.instituteKatedra Ekonomiki i Techniki Leśnej
dc.affiliation.instituteKatedra Mechanicznej Technologii Drewna
dc.affiliation.instituteKatedra Metod Matematycznych i Statystycznych
dc.contributor.authorGórna, Aleksandra Katarzyna
dc.contributor.authorWieruszewski, Marek
dc.contributor.authorSzabelska-Beręsewicz, Alicja
dc.contributor.authorStanula, Zygmunt
dc.contributor.authorAdamowicz, Krzysztof
dc.date.access2025-11-06
dc.date.accessioned2025-11-06T10:43:40Z
dc.date.available2025-11-06T10:43:40Z
dc.date.copyright2022-12-19
dc.date.issued2022
dc.description.abstract<jats:p>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.</jats:p>
dc.description.accesstimeat_publication
dc.description.bibliographyil., bibliogr.
dc.description.financepublication_nocost
dc.description.financecost0,00
dc.description.if2,9
dc.description.number12
dc.description.points100
dc.description.versionfinal_published
dc.description.volume13
dc.identifier.doi10.3390/f13122179
dc.identifier.issn1999-4907
dc.identifier.urihttps://sciencerep.up.poznan.pl/handle/item/5779
dc.identifier.weblinkhttps://www.mdpi.com/1999-4907/13/12/2179
dc.languageen
dc.relation.ispartofForests
dc.relation.pagesart. 2179
dc.rightsCC-BY
dc.sciencecloudnosend
dc.share.typeOPEN_JOURNAL
dc.subject.enwood market
dc.subject.enwood prices
dc.subject.enforecasting
dc.subject.enseasonality of supply
dc.subject.enslash
dc.subject.enpulpwood
dc.subject.enARIMA
dc.subject.enSARIMA
dc.titleBiomass Price Prediction Based on the Example of Poland
dc.title.volumeSpecial Issue Forest Assessment, Modelling and Management in a Changing World
dc.typeJournalArticle
dspace.entity.typePublication
oaire.citation.issue12
oaire.citation.volume13