IDEAS home Printed from https://ideas.repec.org/a/hrs/journl/vxvy2023i1p29-42.html
   My bibliography  Save this article

Energy Crops: Assessments In The European Union Agricultural Regions Through Machine Learning Approaches

Author

Listed:
  • Vitor Joao Pereira Domingues MARTINHO

    (Coordinator Professor with Habilitation, Agricultural School (ESAV) and CERNAS-IPV Research Centre, Polytechnic Institute of Viseu (IPV), Portugal)

Abstract

There is an enormous potential to produce bioenergy from agriculture, forestry and other land use in the European Union (EU) farms. The agricultural sector in the EU member-states has conditions to increase the contributions of renewable energies through better use of the residues and the production of energy crops. Nonetheless, the profitability of these alternative agricultural outputs, in some circumstances, and the need for land for food production, for example, have been obstacles to effective positioning of the EU farms as sources of bioenergy. From this perspective, this study intends to assess the current context of the energy crops in the farms of the EU agricultural regions and identify a model that supports the prediction of these frameworks. For that, data from the Farm Accountancy Data Network (FADN) were considered for the year 2020. This statistical information was analysed through machine learning approaches, namely those associated with multilayer perceptron (MLP) algorithms from the artificial neural networks (ANN) methodologies. The results from these data show that energy crops do have not relevant importance in the European Union farms. On the other hand, when these crops appear, they are produced by larger farms, with greater competitiveness and which receive more subsidies.

Suggested Citation

  • Vitor Joao Pereira Domingues MARTINHO, 2023. "Energy Crops: Assessments In The European Union Agricultural Regions Through Machine Learning Approaches," Regional Science Inquiry, Hellenic Association of Regional Scientists, vol. 0(1), pages 29-42, June.
  • Handle: RePEc:hrs:journl:v:xv:y:2023:i:1:p:29-42
    as

    Download full text from publisher

    File URL: http://www.rsijournal.eu/ARTICLES/June_2023/02.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Miguel A. Jaramillo-Morán & Agustín García-García, 2019. "Applying Artificial Neural Networks to Forecast European Union Allowance Prices: The Effect of Information from Pollutant-Related Sectors," Energies, MDPI, vol. 12(23), pages 1-18, November.
    2. Navas, R Kaja Bantha & Prakash, S & Sasipraba, T, 2020. "Artificial Neural Network based computing model for wind speed prediction: A case study of Coimbatore, Tamil Nadu, India," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    3. Svajone Bekesiene & Ieva Meidute-Kavaliauskiene & Vaida Vasiliauskiene, 2021. "Accurate Prediction of Concentration Changes in Ozone as an Air Pollutant by Multiple Linear Regression and Artificial Neural Networks," Mathematics, MDPI, vol. 9(4), pages 1-21, February.
    4. Jarosław Brodny & Magdalena Tutak & Saqib Ahmad Saki, 2020. "Forecasting the Structure of Energy Production from Renewable Energy Sources and Biofuels in Poland," Energies, MDPI, vol. 13(10), pages 1-31, May.
    5. Bishop, Justin D.K. & Stettler, Marc E.J. & Molden, N. & Boies, Adam M., 2016. "Engine maps of fuel use and emissions from transient driving cycles," Applied Energy, Elsevier, vol. 183(C), pages 202-217.
    6. Jan Vanus & Ojan M. Gorjani & Petr Bilik, 2019. "Novel Proposal for Prediction of CO 2 Course and Occupancy Recognition in Intelligent Buildings within IoT," Energies, MDPI, vol. 12(23), pages 1-25, November.
    7. Magdalena Tutak & Jarosław Brodny & Dominika Siwiec & Robert Ulewicz & Peter Bindzár, 2020. "Studying the Level of Sustainable Energy Development of the European Union Countries and Their Similarity Based on the Economic and Demographic Potential," Energies, MDPI, vol. 13(24), pages 1-31, December.
    8. Magazzino, Cosimo & Mele, Marco & Schneider, Nicolas, 2022. "A new artificial neural networks algorithm to analyze the nexus among logistics performance, energy demand, and environmental degradation," Structural Change and Economic Dynamics, Elsevier, vol. 60(C), pages 315-328.
    9. Jarosław Brodny & Magdalena Tutak, 2020. "The Use of Artificial Neural Networks to Analyze Greenhouse Gas and Air Pollutant Emissions from the Mining and Quarrying Sector in the European Union," Energies, MDPI, vol. 13(8), pages 1-31, April.
    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. Jarosław Kaczmarek & Konrad Kolegowicz & Wojciech Szymla, 2022. "Restructuring of the Coal Mining Industry and the Challenges of Energy Transition in Poland (1990–2020)," Energies, MDPI, vol. 15(10), pages 1-48, May.
    2. Joanna Kisielińska & Monika Roman & Piotr Pietrzak & Michał Roman & Katarzyna Łukasiewicz & Elżbieta Kacperska, 2021. "Utilization of Renewable Energy Sources in Road Transport in EU Countries—TOPSIS Results," Energies, MDPI, vol. 14(22), pages 1-18, November.
    3. Wiktoria Sobczyk & Eugeniusz Jacek Sobczyk, 2021. "Varying the Energy Mix in the EU-28 and in Poland as a Step towards Sustainable Development," Energies, MDPI, vol. 14(5), pages 1-19, March.
    4. Robert Ulewicz & Dominika Siwiec & Andrzej Pacana & Magdalena Tutak & Jarosław Brodny, 2021. "Multi-Criteria Method for the Selection of Renewable Energy Sources in the Polish Industrial Sector," Energies, MDPI, vol. 14(9), pages 1-30, April.
    5. Qiucheng Li & Jiang Hu & Bolin Yu, 2021. "Spatiotemporal Patterns and Influencing Mechanism of Urban Residential Energy Consumption in China," Energies, MDPI, vol. 14(13), pages 1-17, June.
    6. Eckert, Jony Javorski & Silva, Fabrício L. & da Silva, Samuel Filgueira & Bueno, André Valente & de Oliveira, Mona Lisa Moura & Silva, Ludmila C.A., 2022. "Optimal design and power management control of hybrid biofuel–electric powertrain," Applied Energy, Elsevier, vol. 325(C).
    7. Katarzyna Chudy-Laskowska & Tomasz Pisula, 2022. "An Analysis of the Use of Energy from Conventional Fossil Fuels and Green Renewable Energy in the Context of the European Union’s Planned Energy Transformation," Energies, MDPI, vol. 15(19), pages 1-23, October.
    8. Svajone Bekesiene & Ieva Meidute-Kavaliauskiene, 2022. "Artificial Neural Networks for Modelling and Predicting Urban Air Pollutants: Case of Lithuania," Sustainability, MDPI, vol. 14(4), pages 1-24, February.
    9. Anna Duczkowska & Ewa Kulińska & Zbigniew Plutecki & Joanna Rut, 2022. "Sustainable Agro-Biomass Market for Urban Heating Using Centralized District Heating System," Energies, MDPI, vol. 15(12), pages 1-23, June.
    10. Davidescu, Adriana AnaMaria & Popovici, Oana Cristina & Strat, Vasile Alecsandru, 2022. "Estimating the impact of green ESIF in Romania using input-output model," International Review of Financial Analysis, Elsevier, vol. 84(C).
    11. Gürler, Hasan Emin & Özçalıcı, Mehmet & Pamucar, Dragan, 2024. "Determining criteria weights with genetic algorithms for multi-criteria decision making methods: The case of logistics performance index rankings of European Union countries," Socio-Economic Planning Sciences, Elsevier, vol. 91(C).
    12. Paúl Andrés Molina Campoverde, 2023. "Estimation of Fuel Consumption through PID Signals Using the Real Emissions Cycle in the City of Quito, Ecuador," Sustainability, MDPI, vol. 15(16), pages 1-20, August.
    13. Izabela Horzela & Sławomir Gromadzki & Jarosław Gryz & Tomasz Kownacki & Aneta Nowakowska-Krystman & Marzena Piotrowska-Trybull & Radosław Wisniewski, 2021. "Energy Portfolio of the Eastern Poland Macroregion in the European Union," Energies, MDPI, vol. 14(24), pages 1-28, December.
    14. Miretti, Federico & Misul, Daniela & Gennaro, Giulio & Ferrari, Antonio, 2022. "Hybridizing waterborne transport: Modeling and simulation of low-emissions hybrid waterbuses for the city of Venice," Energy, Elsevier, vol. 244(PB).
    15. Sergejus Lebedevas & Laurencas Raslavičius, 2021. "Prognostic Assessment of the Performance Parameters for the Industrial Diesel Engines Operated with Microalgae Oil," Sustainability, MDPI, vol. 13(11), pages 1-23, June.
    16. Kateryna Redko & Olena Borychenko & Anatolii Cherniavskyi & Volodymyr Saienko & Serhii Dudnikov, 2023. "Comparative Analysis of Innovative Development Strategies of Fuel and Energy Complex of Ukraine and the EU Countries: International Experience," International Journal of Energy Economics and Policy, Econjournals, vol. 13(2), pages 301-308, March.
    17. Magdalena Tutak & Jarosław Brodny & Dominika Siwiec & Robert Ulewicz & Peter Bindzár, 2020. "Studying the Level of Sustainable Energy Development of the European Union Countries and Their Similarity Based on the Economic and Demographic Potential," Energies, MDPI, vol. 13(24), pages 1-31, December.
    18. Miranda, Matheus H.R. & Silva, Fabrício L. & Lourenço, Maria A.M. & Eckert, Jony J. & Silva, Ludmila C.A., 2022. "Vehicle drivetrain and fuzzy controller optimization using a planar dynamics simulation based on a real-world driving cycle," Energy, Elsevier, vol. 257(C).
    19. Justė Jankevičienė & Arvydas Kanapickas, 2021. "Projected Near-Surface Wind Speed Trends in Lithuania," Energies, MDPI, vol. 14(17), pages 1-13, August.
    20. Mera, Zamir & Varella, Roberto & Baptista, Patrícia & Duarte, Gonçalo & Rosero, Fredy, 2022. "Including engine data for energy and pollutants assessment into the vehicle specific power methodology," Applied Energy, Elsevier, vol. 311(C).

    More about this item

    Keywords

    Agriculture 4.0; Artificial Neural Networks; Multilayer Perceptron;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • Q12 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources

    Statistics

    Access and download statistics

    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:hrs:journl:v:xv:y:2023:i:1:p:29-42. 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: Dimitrios K. Kouzas (email available below). General contact details of provider: .

    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.