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Development trajectories and technical efficiency of agriculture in the European Union countries (2007-2023) in the context of the impact of the common agricultural policy

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Języki publikacji
EN
Abstrakty
EN
Purpose: This study aims to identify agricultural development trajectories across 25 EU member states, determine clusters with homogeneous development patterns, and analyse variations in technical efficiency levels and dynamics within these clusters. Furthermore, it evaluates the impact of Common Agricultural Policy (CAP) instruments and proposes a novel classification system to enhance the effectiveness of agricultural policy interventions. Design/methodology/approach: The analysis employs a comprehensive set of variables reflecting the agricultural production business model's characteristics. The study utilizes EUROSTAT data spanning 2007-2023. Through systematic data analysis, clusters exhibiting similar development trajectories were identified. Technical efficiency measurements within these clusters were conducted using Data Envelopment Analysis (DEA) methodology. Findings: The research reveals significant heterogeneity in European agricultural development trajectories, enabling the identification of distinct clusters with similar characteristics. These clusters demonstrate varying levels and evolutionary patterns of technical efficiency. Practical implications: The empirical findings facilitate the formulation of evidence-based recommendations aimed at enhancing and harmonizing efficiency levels across the European agricultural sector. Originality/value: This research contributes to the existing literature on EU agricultural efficiency by proposing a novel analytical clustering approach that transcends the traditional dichotomy between 'old' and 'new' EU member states. Additionally, it provides policy recommendations for future CAP developments.
Rocznik
Tom
Strony
557--576
Opis fizyczny
Bibliogr. 39 poz.
Twórcy
  • West Pomeranian University of Technology in Szczecin
Bibliografia
  • 1. Abidi, M.H., Mohammed, M.K., Alkhalefah, H. (2022). Predictive Maintenance Planning for Industry 4.0 Using Machine Learning for Sustainable Manufacturing. Sustainability, 14, 3387.
  • 2. Alsina, E.F., Chica, M., Trawiński, K., Regattieri, A. (2018). On the use of machine learning methods to predict component reliability from data-driven industrial case studies. The International Journal of Advanced Manufacturing Technology, 94, 2419-2433.
  • 3. Alvarez Quiñones, L.I., Lozano-Moncada, C.A., Bravo Montenegro, D.A. (2023). Machine learning for predictive maintenance scheduling of distribution transformers. Journal of Quality in Maintenance Engineering, 29(1), 188-202.
  • 4. Amruthnath, N., Gupta, T. (2019). Factor analysis in fault diagnostics using random forest. arXiv preprint arXiv:1904.13366.
  • 5. Antosz, K., Jasiulewicz-Kaczmarek, M., Machado, J., Relich, M. (2023). Application of Principle Component Analysis and logistic regression to support Six Sigma implementation in maintenance. Eksploatacja i Niezawodnosc - Maintenance and Reliability, 25(4).
  • 6. Arena, S., Florian, E., Zennaro, I., Orrù, P.F., Sgarbossa, F. (2022). A novel decision support system for managing predictive maintenance strategies based on machine learning approaches. Safety science, 146, 105529;
  • 7. Bousdekis, A., Lepenioti, K., Apostolou, D., Mentzas, G. (2021). A review of data-driven decision-making methods for industry 4.0 maintenance applications. Electronics, 10(7), 828.
  • 8. Bertolini, M., Mezzogori, D., Neroni, M., Zammori, F. (2021). Machine Learning for industrial applications: A comprehensive literature review. Expert Systems with Applications, 175, 114820
  • 9. Campbell, J.D., Reyes-Picknell, J.V., Kim, H.S. (2015). Uptime: Strategies for Excellence in Maintenance Management. CRC Press.
  • 10. Campos, J.R., Costa, E., Vieira, M. (2019). Improving failure prediction by ensembling the decisions of machine learning models: A case study. IEEE Access, 7, 177661-177674.
  • 11. Carvalho, T.P., Soares, F.A., Vita, R., Francisco, R.D.P., Basto, J.P., Alcalá, S.G. (2019). A systematic literature review of machine learning methods applied to predictive maintenance. Computers & Industrial Engineering, 137, 106024.
  • 12. Chakroun, A., Hani, Y., Elmhamedi, A., Masmoudi, F. (2024). A predictive maintenance model for health assessment of an assembly robot based on machine learning in the context of smart plant. Journal of Intelligent Manufacturing, 1-19.
  • 13. Ciężak, W., Kutyłowska, M. (2023) Application of exponential smoothing method to forecasting daily water consumption in rural areas. Archives of Civil Engineering, 69(3), 445-456.
  • 14. Çınar, Z.M., Abdussalam Nuhu, A., Zeeshan, Q., Korhan, O., Asmael, M., Safaei, B. (2020). Machine learning in predictive maintenance towards sustainable smart manufacturing in industry 4.0. Sustainability, 12(19), 8211.
  • 15. Cline, B., Niculescu, R.S., Huffman, D., Deckel, B. (2017). Predictive maintenance applications for machine learning. Annual reliability and maintainability symposium (RAMS). IEEE, pp. 1-7.
  • 16. Dalzochio, J., Kunst, R., Pignaton, E., Binotto, A., Sanyal, S., Favilla, J., Barbosa, J. (2020). Machine learning and reasoning for predictive maintenance in Industry 4.0: Current status and challenges. Computers in Industry, 123, 103298.
  • 17. Emmanouilidis, C. (2023). Topical collection "applications of machine learning in maintenance engineering and management". Neural Computing and Applications, 35(4), 2945-2946.
  • 18. EN 13306:2017; Maintenance. Maintenance Terminology.
  • 19. Gatta, F., Giampaolo, F., Chiaro, D., Piccialli, F. (2024). Predictive maintenance for offshore oil wells by means of deep learning features extraction. Expert Systems, 41(2), e13128
  • 20. Hallioui, A., Herrou, B., Katina, P.F., Santos, R.S., Egbue, O., Jasiulewicz-Kaczmarek, M., Marques, P.C. (2023). A Review of Sustainable Total Productive Maintenance (STPM). Sustainability, 15(16), 12362.
  • 21. Justus, V., Kanagachidambaresan, G.R. (2024). Machine learning based fault-oriented predictive maintenance in industry 4.0. International Journal of System Assurance Engineering and Management, 15(1), 462-474.
  • 22. Kaparthi, S., Bumblauskas, D. (2020). Designing predictive maintenance systems using decision tree-based machine learning techniques. International Journal of Quality & Reliability Management, 37(4), 659-686.
  • 23. Mahmud, I., Ismail, I., Abdulkarim, A., Shehu, G.S., Olarinoye, G.A., Musa, U. (2024). Selection of an appropriate maintenance strategy using analytical hierarchy process of cement plant. Life Cycle Reliability and Safety Engineering, 13(2), 103-109.
  • 24. Misaii, H., Fouladirad, M., Firoozeh, H. (2022). Data-driven Maintenance Optimization Using Random Forest Algorithm. ENBIS Spring Meeting.
  • 25. Musiał, M., Lichołai, L., Pękala, A. (2023) Analysis of the Thermal Performance of Isothermal Composite Heat Accumulators Containing Organic Phase-Change Material. Energies, 16, 1409.
  • 26. Nguyen, V.T., Do, P., Vosin, A., Iung, B. (2022). Artificial-intelligence-based maintenance decision-making and optimization for multi-state component systems. Reliability Engineering & System Safety, 228, 108757.
  • 27. Pinciroli, L., Baraldi, P., Zio, E. (2023). Maintenance optimization in Industry 4.0. Reliability Engineering & System Safety, 109204.
  • 28. Polenghi, A., Roda, I., Macchi, M., Pozzetti, A. (2023). A methodology to boost datadriven decision-making process for a modern maintenance practice. Production Planning & Control, 34(14), 1333-1349.
  • 29. Quatrini, E., Costantino, F., Di Gravio, G., Patriarca, R. (2020). Machine learning for anomaly detection and process phase classification to improve safety and maintenance activities. Journal of Manufacturing Systems, 56, 117-132.
  • 30. Rebaiaia, M.L., Ait-Kadi, D. (2023). A new integrated strategy for optimising the maintenance cost of complex systems using reliability importance measures. International Journal of Production Research, 1-22.
  • 31. Rojek, I., Jasiulewicz-Kaczmarek, M., Piechowski, M., Mikołajewski, D. (2023). An artificial intelligence approach for improving maintenance to supervise machine failures and support their repair. Applied Sciences, 13(8), 4971.
  • 32. Ruiz-Sarmiento, J.R., Monroy, J., Moreno, F.A., Galindo, C., Bonelo, J.M., GonzalezJimenez, J. (2020). A predictive model for the maintenance of industrial machinery in the context of industry 4.0. Engineering Applications of Artificial Intelligence, 87, 103289.
  • 33. Saihi, A., Ben-Daya, M., As'ad, R. (2023). Underpinning success factors of maintenance digital transformation: A hybrid reactive Delphi approach. International Journal of Production Economics, 255, 108701
  • 34. Sanchez-Londono, D., Barbieri, G., Fumagalli, L. (2023). Smart retrofitting in maintenance: a systematic literature review. Journal of Intelligent Manufacturing, 34(1), 1-19.
  • 35. Scaife, A.D. (2024) Improve predictive maintenance through the application of artificial intelligence: A systematic review. Results in Engineering, 21, 101645.
  • 36. Shaheen, B.W., Németh, I. (2022). Integration of maintenance management system functions with industry 4.0 technologies and features-A review. Processes, 10(11), 2173.
  • 37. Surucu, O., Gadsden, S.A., Yawney, J. (2023). Condition Monitoring using Machine Learning: A Review of Theory, Applications, and Recent Advances. Expert Systems with Applications, 221, 119738.
  • 38. Vanderschueren, T., Boute, R., Verdonck, T., Baesens, B., Verbeke, W. (2023). Optimizing the preventive maintenance frequency with causal machine learning. International Journal of Production Economics, 258, 108798
  • 39. Wang, X., Liu, M., Liu, C., Ling, L., Zhang, X. (2023). Data-driven and Knowledge-based predictive maintenance method for industrial robots for the production stability of intelligent manufacturing. Expert Systems with Applications, 234, 121136.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-4ccae95b-c0ba-43db-917b-3dbeaf2eae3d
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