IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i3p1217-d1582706.html
   My bibliography  Save this article

Factors Influencing the Productivity of Direct Energy Inputs in EU Agriculture

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/3/1217/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/3/1217/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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).
    3. 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).
    4. 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.
    5. 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).
    6. 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.
    7. 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.
    8. Banaeian, Narges & Zangeneh, Morteza, 2011. "Study on energy efficiency in corn production of Iran," Energy, Elsevier, vol. 36(8), pages 5394-5402.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Vieira, Flávio & MacDonald, Ronald & Damasceno, Aderbal, 2012. "The role of institutions in cross-section income and panel data growth models: A deeper investigation on the weakness and proliferation of instruments," Journal of Comparative Economics, Elsevier, vol. 40(1), pages 127-140.
    2. Abonazel, Mohamed R., 2016. "Bias Correction Methods for Dynamic Panel Data Models with Fixed Effects," MPRA Paper 70628, University Library of Munich, Germany.
    3. Ferdi Celikay, 2020. "Dimensions of tax burden: a review on OECD countries," Journal of Economics, Finance and Administrative Science, Emerald Group Publishing Limited, vol. 25(49), pages 27-43, March.
    4. Maurice J. G. Bun & Frank Windmeijer, 2010. "The weak instrument problem of the system GMM estimator in dynamic panel data models," Econometrics Journal, Royal Economic Society, vol. 13(1), pages 95-126, February.
    5. Elizabeth N. Appiah, 2017. "The Effect of Education Expenditure on Per Capita GDP in Developing Countries," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 9(10), pages 136-144, October.
    6. Kufenko, Vadim & Prettner, Klaus, 2016. "You can't always get what you want? Estimator choice and the speed of convergence," Hohenheim Discussion Papers in Business, Economics and Social Sciences 20-2016, University of Hohenheim, Faculty of Business, Economics and Social Sciences.
    7. Hayakawa, Kazuhiko, 2019. "Alternative over-identifying restriction test in the GMM estimation of panel data models," Econometrics and Statistics, Elsevier, vol. 10(C), pages 71-95.
    8. Younas, Javed, 2015. "Terrorism, openness and the Feldstein–Horioka paradox," European Journal of Political Economy, Elsevier, vol. 38(C), pages 1-11.
    9. Hughes Hallett, Andrew & Bernoth, Kerstin & Lewis, John, 2008. "Did Fiscal Policy Makers Know What They Were Doing? Reassessing Fiscal Policy with Real Time Data," CEPR Discussion Papers 6758, C.E.P.R. Discussion Papers.
    10. Kufenko, Vadmin & Prettner, Klaus, 2017. "You can't always get what you want? A Monte Carlo analysis of the bias and the efficiency of dynamic panel data estimators," ECON WPS - Working Papers in Economic Theory and Policy 07/2017, TU Wien, Institute of Statistics and Mathematical Methods in Economics, Economics Research Unit.
    11. Bernoth, Kerstin & Hughes Hallett, Andrew & Lewis, John, 2008. "Did Fiscal Policy Makers Know What They Were Doing? Reassessing Fiscal Policy with Real Time Data," CEPR Discussion Papers 6758, C.E.P.R. Discussion Papers.
    12. Sebastian Kripfganz & Claudia Schwarz, 2019. "Estimation of linear dynamic panel data models with time‐invariant regressors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(4), pages 526-546, June.
    13. Elizabeth Asiedu & Yi Jin & Isaac K. Kanyama, 2015. "The impact of HIV/AIDS on foreign direct investment: Evidence from Sub-Saharan Africa," Journal of African Trade, Springer, vol. 2(1), pages 1-17, March.
    14. Kazuhiko Hayakawa & M. Hashem Pesaran, 2012. "Robust Standard Errors in Transformed Likelihood Estimation of Dynamic Panel Data Models," Working Paper series 38_12, Rimini Centre for Economic Analysis.
    15. Katsushi S. Imai & Wenya Cheng & Raghav Gaiha, 2017. "Dynamic and long-term linkages among agricultural and non-agricultural growth, inequality and poverty in developing countries," International Review of Applied Economics, Taylor & Francis Journals, vol. 31(3), pages 318-338, May.
    16. Dong, Kangyin & Wei, Shuo & Liu, Yang & Zhao, Jun, 2023. "How does energy poverty eradication promote common prosperity in China? The role of labor productivity," Energy Policy, Elsevier, vol. 181(C).
    17. Kruiniger, Hugo, 2013. "Quasi ML estimation of the panel AR(1) model with arbitrary initial conditions," Journal of Econometrics, Elsevier, vol. 173(2), pages 175-188.
    18. Moritz Schularick & Solomos Solomou, 2011. "Tariffs and economic growth in the first era of globalization," Journal of Economic Growth, Springer, vol. 16(1), pages 33-70, March.
    19. Hayakawa, Kazuhiko & Nagata, Shuichi, 2016. "On the behaviour of the GMM estimator in persistent dynamic panel data models with unrestricted initial conditions," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 265-303.
    20. Torben M. Andersen & Jonas Maibom & Michael Svarer & Allan Sørensen, 2017. "Do Business Cycles Have Long-Term Impact for Particular Cohorts?," LABOUR, CEIS, vol. 31(3), pages 309-336, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:17:y:2025:i:3:p:1217-:d:1582706. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.