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Predicting Post-Production Biomass Prices

cris.lastimport.scopus2025-10-23T06:56:48Z
cris.virtual.author-orcid0000-0001-5033-1156
cris.virtual.author-orcid0000-0002-1806-0891
cris.virtual.author-orcid0000-0002-4867-195X
cris.virtual.author-orcid0000-0002-8905-3664
cris.virtual.author-orcid0000-0001-6285-2119
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cris.virtualsource.author-orcid6a2f8857-003b-41ec-9112-0ef6941bfd06
cris.virtualsource.author-orcid0241aa31-988f-4deb-9623-97f29e43d000
cris.virtualsource.author-orcid96952274-3aa2-4f28-bd9d-69f143c4149f
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dc.abstract.enThis 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%.
dc.affiliationWydział Leśny i Technologii Drewna
dc.affiliationWydział Rolnictwa, Ogrodnictwa i Biotechnologii
dc.affiliation.instituteKatedra Ekonomiki i Techniki Leśnej
dc.affiliation.instituteKatedra Metod Matematycznych i Statystycznych
dc.affiliation.instituteKatedra Mechanicznej Technologii Drewna
dc.contributor.authorGórna, Aleksandra Katarzyna
dc.contributor.authorSzabelska-Beręsewicz, Alicja
dc.contributor.authorWieruszewski, Marek
dc.contributor.authorStarosta-Grala, Monika
dc.contributor.authorStanula, Zygmunt
dc.contributor.authorKożuch, Anna
dc.contributor.authorAdamowicz, Krzysztof
dc.date.access2025-09-02
dc.date.accessioned2025-09-02T08:57:56Z
dc.date.available2025-09-02T08:57:56Z
dc.date.copyright2023-04-15
dc.date.issued2023
dc.description.abstract<jats:p>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%.</jats:p>
dc.description.accesstimeat_publication
dc.description.bibliographyil., bibliogr.
dc.description.financepublication_nocost
dc.description.financecost0,00
dc.description.if3,0
dc.description.number8
dc.description.points140
dc.description.versionfinal_published
dc.description.volume16
dc.identifier.doi10.3390/en16083470
dc.identifier.issn1996-1073
dc.identifier.urihttps://sciencerep.up.poznan.pl/handle/item/4577
dc.identifier.weblinkhttps://www.mdpi.com/1996-1073/16/8/3470
dc.languageen
dc.relation.ispartofEnergies
dc.relation.pagesart. 3470
dc.rightsCC-BY
dc.sciencecloudsend
dc.share.typeOPEN_JOURNAL
dc.subject.entimber market
dc.subject.enwood biomass prices
dc.subject.enprediction
dc.subject.enseasonality of supply
dc.subject.encyclicality
dc.subject.enwoodchips
dc.subject.ensawdust
dc.subject.enbark
dc.subject.enARIMA
dc.subject.enWinters-Holt
dc.titlePredicting Post-Production Biomass Prices
dc.title.volumeSpecial Issue Bioenergy Economics: Analysis, Modeling and Application
dc.typeJournalArticle
dspace.entity.typePublication
oaire.citation.issue8
oaire.citation.volume16