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Impact of the Russia-Ukraine conflict on the quality and quantity of Malaysia’s palm oil production: A time series analysis

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Listed:
  • Azlizan Mat Enh
  • Hasrina Mustafa
  • Fahri Ahmed
  • Andika Wahab

Abstract

This study investigates the effects of the Russia-Ukraine conflict on the quality and quantity of Malaysia’s palm oil production through a time series analysis. The study uses three primary factors to evaluate palm oil production: the Monthly Oil Extraction Rate (OER), the Monthly Fresh Fruit Bunch (FFB) Yield, and the Monthly Oil Exports. The results indicate that the Russia-Ukraine conflict significantly impacted the quality and quantity of palm oil production in Malaysia. Marginal declines in both the quality and quantity of palm oil produced at the onset of the conflict indicate a slight but significant decline in palm oil production during the next four-year period.

Suggested Citation

  • Azlizan Mat Enh & Hasrina Mustafa & Fahri Ahmed & Andika Wahab, 2024. "Impact of the Russia-Ukraine conflict on the quality and quantity of Malaysia’s palm oil production: A time series analysis," PLOS ONE, Public Library of Science, vol. 19(5), pages 1-14, May.
  • Handle: RePEc:plo:pone00:0302405
    DOI: 10.1371/journal.pone.0302405
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    References listed on IDEAS

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    1. Peter R. Winters, 1960. "Forecasting Sales by Exponentially Weighted Moving Averages," Management Science, INFORMS, vol. 6(3), pages 324-342, April.
    2. Florin Aliu & Jiří Kučera & Simona Hašková, 2023. "Agricultural Commodities in the Context of the Russia-Ukraine War: Evidence from Corn, Wheat, Barley, and Sunflower Oil," Forecasting, MDPI, vol. 5(1), pages 1-23, March.
    3. David Maher, 2015. "Rooted in Violence: Civil War, International Trade and the Expansion of Palm Oil in Colombia," New Political Economy, Taylor & Francis Journals, vol. 20(2), pages 299-330, April.
    4. Mohd Syafiq Sabri & Norlin Khalid & Abdul Hafizh Mohd Azam & Tamat Sarmidi, 2022. "Impact Analysis of the External Shocks on the Prices of Malaysian Crude Palm Oil: Evidence from a Structural Vector Autoregressive Model," Mathematics, MDPI, vol. 10(23), pages 1-22, December.
    5. Yan Ding & Yue Liu & Pierre Failler, 2022. "The Impact of Uncertainties on Crude Oil Prices: Based on a Quantile-on-Quantile Method," Energies, MDPI, vol. 15(10), pages 1-35, May.
    6. Hasrina Mustafa & Fahri Ahmed & Waffa Wahida Zainol & Azlizan Mat Enh, 2021. "Forecasting the Impact of Gross Domestic Product (GDP) on International Tourist Arrivals to Langkawi, Malaysia: A PostCOVID-19 Future," Sustainability, MDPI, vol. 13(23), pages 1-16, December.
    7. Makridakis, Spyros, 1993. "Accuracy measures: theoretical and practical concerns," International Journal of Forecasting, Elsevier, vol. 9(4), pages 527-529, December.
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