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Factors Influencing the Productivity of Direct Energy Inputs in EU Agriculture

Author

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  • Ludwik Wicki

    (Institute of Economics and Finance, Warsaw University of Life Sciences, 02-787 Warsaw, Poland)

  • Hanna Dudek

    (Institute of Economics and Finance, Warsaw University of Life Sciences, 02-787 Warsaw, Poland)

  • Andrzej Parzonko

    (Institute of Economics and Finance, Warsaw University of Life Sciences, 02-787 Warsaw, Poland)

  • Dariusz Kusz

    (Faculty of Management, Rzeszow University of Technology, 35-959 Rzeszów, Poland)

  • Kaspars Naglis-Liepa

    (Faculty of Economics and Social Development, Latvia University of Life Sciences and Technologies, LV-3001 Jelgava, Latvia)

Abstract

Agriculture is a major energy consumer and a significant contributor to global greenhouse gas emissions. As the world’s population grows, increasing food production while reducing energy use presents a critical challenge. This study examined the trends in direct energy input productivity in agriculture across European Union (EU) countries from 2010 to 2021, focusing on the impact of structural factors, including production scale, mechanization, intensity, and output composition. The results showed a gradual decline in energy productivity, averaging a 1.04% annual decrease, reaching EUR 344,000 per terajoule (TJ) in 2021. Higher mechanization and production intensity improved energy productivity, while larger production scales and a greater share of animal farming had negative effects. Given the current trends of production expansion and extensification, further progress in energy productivity in agriculture appears limited. Policy measures should prioritize optimizing animal production’s share and adopting a sustainable use of renewable energy to lower the dependency on non-renewable fossil fuel sources. Future strategies must balance high agricultural output with sustainable energy consumption per food unit.

Suggested Citation

  • Ludwik Wicki & Hanna Dudek & Andrzej Parzonko & Dariusz Kusz & Kaspars Naglis-Liepa, 2025. "Factors Influencing the Productivity of Direct Energy Inputs in EU Agriculture," Sustainability, MDPI, vol. 17(3), pages 1-17, February.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:3:p:1217-:d:1582706
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    References listed on IDEAS

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    1. Gonzalez-Martinez, Ana Rosa & Jongeneel, Roel & Kros, Hans & Lesschen, Jan Peter & de Vries, Marion & Reijs, Joan & Verhoog, David, 2021. "Aligning agricultural production and environmental regulation: An integrated assessment of the Netherlands," Land Use Policy, Elsevier, vol. 105(C).
    2. Liton Chandra Voumik & Md. Azharul Islam & Samrat Ray & Nora Yusma Mohamed Yusop & Abdul Rahim Ridzuan, 2023. "CO 2 Emissions from Renewable and Non-Renewable Electricity Generation Sources in the G7 Countries: Static and Dynamic Panel Assessment," Energies, MDPI, vol. 16(3), pages 1-14, January.
    3. Arellano, Manuel & Bover, Olympia, 1995. "Another look at the instrumental variable estimation of error-components models," Journal of Econometrics, Elsevier, vol. 68(1), pages 29-51, July.
    4. Shi, Hongxu & Xu, Hao & Gao, Wei & Zhang, Jinhao & Chang, Ming, 2022. "The impact of energy poverty on agricultural productivity: The case of China," Energy Policy, Elsevier, vol. 167(C).
    5. Adam Wąs & Julia Tsybulska & Piotr Sulewski & Vitaliy Krupin & Grzegorz Rawa & Iryna Skorokhod, 2024. "Energy Efficiency of Polish Farms Following EU Accession (2004–2021)," Energies, MDPI, vol. 18(1), pages 1-19, December.
    6. Banaeian, Narges & Zangeneh, Morteza, 2011. "Study on energy efficiency in corn production of Iran," Energy, Elsevier, vol. 36(8), pages 5394-5402.
    7. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    8. Uhlin, Hans-Erik, 1998. "Why energy productivity is increasing: An I-O analysis of Swedish agriculture," Agricultural Systems, Elsevier, vol. 56(4), pages 443-465, April.
    9. Soltani, Shiva & Mosavi, Seyed Habibollah & Saghaian, Sayed H. & Azhdari, Somayeh & Alamdarlo, Hamed N. & Khalilian, Sadegh, 2023. "Climate change and energy use efficiency in arid and semiarid agricultural areas: A case study of Hamadan-Bahar plain in Iran," Energy, Elsevier, vol. 268(C).
    10. Jinxing Wang & Wanming Li & Shamsheer ul Haq & Pomi Shahbaz, 2023. "Adoption of Renewable Energy Technology on Farms for Sustainable and Efficient Production: Exploring the Role of Entrepreneurial Orientation, Farmer Perception and Government Policies," Sustainability, MDPI, vol. 15(7), pages 1-20, March.
    11. Hayakawa, Kazuhiko, 2007. "Small sample bias properties of the system GMM estimator in dynamic panel data models," Economics Letters, Elsevier, vol. 95(1), pages 32-38, April.
    12. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
    13. Yang Fan & Wu Guoyong & Noman Riaz & Kamila Radlińska, 2024. "Technical efficiency and farm size in the context of sustainable agriculture," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 70(9), pages 446-456.
    14. Shreesha Pandeya & Buddhi R. Gyawali & Suraj Upadhaya, 2025. "Factors Influencing Precision Agriculture Technology Adoption Among Small-Scale Farmers in Kentucky and Their Implications for Policy and Practice," Agriculture, MDPI, vol. 15(2), pages 1-17, January.
    15. Willems, Jaap & van Grinsven, Hans J.M. & Jacobsen, Brian H. & Jensen, Tenna & Dalgaard, Tommy & Westhoek, Henk & Kristensen, Ib Sillebak, 2016. "Why Danish pig farms have far more land and pigs than Dutch farms? Implications for feed supply, manure recycling and production costs," Agricultural Systems, Elsevier, vol. 144(C), pages 122-132.
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