IDEAS home Printed from
   My bibliography  Save this article

Assess and prognosticate green house gas emissions from agricultural production of EU countries, by implementing, DEA Window analysis and artificial neural networks


  • Vlontzos, G.
  • Pardalos, P.M.


One of the most important policy reforms for the European Union (EU) agriculture was the implementation of the Agenda 2000, which establishes a new framework for subsidies management, decoupled from both crop and animal production for the vast majority of products. One of the main goals of this new policy framework is the improvement of its environmental impact. Additionally, there is a need for the implementation of new efficiency assessment and prognostication tools for the evaluation of EU farming, because the influence of market forces has been increased substantially. Having in mind the efficacy of Data Envelopment Analysis (DEA) methodology, it is used to calculate and quantify the environmental efficiency of EU countries' primary sectors. In this paper, the DEA Window methodology is used to assess GHG emissions efficiency and identify efficiency change of EU countries' primary sectors, under the strong influence of Common Agricultural Policy (CAP), quantifying by this way its positive or negative impact on a national basis, providing at the same time hints for counteractive actions. The main results provide the significant differences among EU countries, with the less developed ones to perform low environmental efficiency rates. Moreover, countries which their output depends to a large extend on arable crops achieve low efficiency rates too. Finally, Artificial Neural Networks (ANNs) are being used as a tool to estimate future performance of EU countries primary sectors on the topic of Greenhouse Gas (GHG) emissions as an undesirable output of agricultural production process. The validation performance characteristics, as well as the linear fit to this output-target relationship, closely intersect the bottom-left and top-right corners of the plot. The combination of these methodologies provides a new methodological approach for CAP evaluation and prognostication, appropriately adjusted to the new market oriented framework for EU agricultural production.

Suggested Citation

  • Vlontzos, G. & Pardalos, P.M., 2017. "Assess and prognosticate green house gas emissions from agricultural production of EU countries, by implementing, DEA Window analysis and artificial neural networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 155-162.
  • Handle: RePEc:eee:rensus:v:76:y:2017:i:c:p:155-162
    DOI: 10.1016/j.rser.2017.03.054

    Download full text from publisher

    File URL:
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    1. Kamel Louhichi & Guillermo Flichman & Jean Boisson, 2010. "Bio-economic modelling of soil erosion externalities and policy options: a Tunisian case study," Journal of Bioeconomics, Springer, vol. 12(2), pages 145-167, July.
    2. Song, Malin & An, Qingxian & Zhang, Wei & Wang, Zeya & Wu, Jie, 2012. "Environmental efficiency evaluation based on data envelopment analysis: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 4465-4469.
    3. Vlontzos, George & Niavis, Spyros & Manos, Basil, 2014. "A DEA approach for estimating the agricultural energy and environmental efficiency of EU countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 91-96.
    4. Heidari, M.D. & Omid, M., 2011. "Energy use patterns and econometric models of major greenhouse vegetable productions in Iran," Energy, Elsevier, vol. 36(1), pages 220-225.
    5. repec:eee:ecomod:v:220:y:2009:i:6:p:888-895 is not listed on IDEAS
    6. José A. Gómez-Limón & Andrés J. Picazo-Tadeo & Ernest Reig-Martínez, 2011. "Eco-efficiency Assessment of Olive Farms in Andalusia," Working Papers 1105, Department of Applied Economics II, Universidad de Valencia.
    7. Picazo-Tadeo, Andrés J. & Beltrán-Esteve, Mercedes & Gómez-Limón, José A., 2012. "Assessing eco-efficiency with directional distance functions," European Journal of Operational Research, Elsevier, vol. 220(3), pages 798-809.
    8. Smith, Peter & Mayston, David, 1987. "Measuring efficiency in the public sector," Omega, Elsevier, vol. 15(3), pages 181-189.
    9. Amores, Antonio F. & Contreras, Ignacio, 2009. "New approach for the assignment of new European agricultural subsidies using scores from data envelopment analysis: Application to olive-growing farms in Andalusia (Spain)," European Journal of Operational Research, Elsevier, vol. 193(3), pages 718-729, March.
    10. C. F. Bach & S. E. Frandsen & H. G. Jensen, 2000. "Agricultural and Economy-Wide Effects of European Enlargement: Modelling the Common Agricultural Policy," Journal of Agricultural Economics, Wiley Blackwell, vol. 51(2), pages 162-180.
    11. Khoshnevisan, Benyamin & Rafiee, Shahin & Omid, Mahmoud & Mousazadeh, Hossein, 2013. "Reduction of CO2 emission by improving energy use efficiency of greenhouse cucumber production using DEA approach," Energy, Elsevier, vol. 55(C), pages 676-682.
    12. Vlontzos, George & Theodoridis, Alexandros, 2013. "Efficiency and Productivity Change in the Greek Dairy Industry," Agricultural Economics Review, Greek Association of Agricultural Economists, vol. 0(Issue 2), pages 1-15.
    13. Boussofiane, A. & Dyson, R. G. & Thanassoulis, E., 1991. "Applied data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 52(1), pages 1-15, May.
    14. Khoshnevisan, Benyamin & Rafiee, Shahin & Omid, Mahmoud & Mousazadeh, Hossein, 2013. "Applying data envelopment analysis approach to improve energy efficiency and reduce GHG (greenhouse gas) emission of wheat production," Energy, Elsevier, vol. 58(C), pages 588-593.
    15. Sözen, Adnan & Alp, Ihsan & Özdemir, Adnan, 2010. "Assessment of operational and environmental performance of the thermal power plants in Turkey by using data envelopment analysis," Energy Policy, Elsevier, vol. 38(10), pages 6194-6203, October.
    16. Park, S.J. & Hwang, C.S. & Vlek, P.L.G., 2005. "Comparison of adaptive techniques to predict crop yield response under varying soil and land management conditions," Agricultural Systems, Elsevier, vol. 85(1), pages 59-81, July.
    17. Hansson, Helena, 2007. "Strategy factors as drivers and restraints on dairy farm performance: Evidence from Sweden," Agricultural Systems, Elsevier, vol. 94(3), pages 726-737, June.
    18. Arabi, Behrouz & Munisamy, Susila & Emrouznejad, Ali & Shadman, Foroogh, 2014. "Power industry restructuring and eco-efficiency changes: A new slacks-based model in Malmquist–Luenberger Index measurement," Energy Policy, Elsevier, vol. 68(C), pages 132-145.
    19. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    20. Kaul, Monisha & Hill, Robert L. & Walthall, Charles, 2005. "Artificial neural networks for corn and soybean yield prediction," Agricultural Systems, Elsevier, vol. 85(1), pages 1-18, July.
    21. Elvira Silva & Spiro Stefanou, 2003. "Nonparametric Dynamic Production Analysis and the Theory of Cost," Journal of Productivity Analysis, Springer, vol. 19(1), pages 5-32, January.
    22. George Vlontzos & Garyfallos Arabatzis & Basil Manos, 2014. "Investigation of the relative efficiency of LEADER+ in rural areas of Northern Greece," International Journal of Green Economics, Inderscience Enterprises Ltd, vol. 8(1), pages 37-48.
    23. Khoshroo, Alireza & Mulwa, Richard & Emrouznejad, Ali & Arabi, Behrouz, 2013. "A non-parametric Data Envelopment Analysis approach for improving energy efficiency of grape production," Energy, Elsevier, vol. 63(C), pages 189-194.
    24. Pahlavan, Reza & Omid, Mahmoud & Akram, Asadollah, 2012. "Energy input–output analysis and application of artificial neural networks for predicting greenhouse basil production," Energy, Elsevier, vol. 37(1), pages 171-176.
    25. Hertel, Thomas, 1997. "Global Trade Analysis: Modeling and applications," GTAP Books, Center for Global Trade Analysis, Department of Agricultural Economics, Purdue University, number 7685.
    26. Emmanuel Thanassoulis, 1999. "Data Envelopment Analysis and Its Use in Banking," Interfaces, INFORMS, vol. 29(3), pages 1-13, June.
    27. Safa, Majeed & Samarasinghe, Sandhya, 2013. "Modelling fuel consumption in wheat production using artificial neural networks," Energy, Elsevier, vol. 49(C), pages 337-343.
    28. Khoshnevisan, Benyamin & Rafiee, Shahin & Omid, Mahmoud & Yousefi, Marziye & Movahedi, Mehran, 2013. "Modeling of energy consumption and GHG (greenhouse gas) emissions in wheat production in Esfahan province of Iran using artificial neural networks," Energy, Elsevier, vol. 52(C), pages 333-338.
    29. Lozano, S. & Villa, G. & Brännlund, R., 2009. "Centralised reallocation of emission permits using DEA," European Journal of Operational Research, Elsevier, vol. 193(3), pages 752-760, March.
    30. Reig-Martinez, Ernest & Picazo-Tadeo, Andres J., 2004. "Analysing farming systems with Data Envelopment Analysis: citrus farming in Spain," Agricultural Systems, Elsevier, vol. 82(1), pages 17-30, October.
    31. Karkazis, John & Thanassoulis, Emmanuel, 1998. "Assessing the effectiveness of regional development policies in Northern Greece using data envelopment analysis," Socio-Economic Planning Sciences, Elsevier, vol. 32(2), pages 123-137, June.
    32. Chen, Serena H. & Jakeman, Anthony J. & Norton, John P., 2008. "Artificial Intelligence techniques: An introduction to their use for modelling environmental systems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 78(2), pages 379-400.
    33. Cook, Wade D. & Seiford, Larry M., 2009. "Data envelopment analysis (DEA) - Thirty years on," European Journal of Operational Research, Elsevier, vol. 192(1), pages 1-17, January.
    34. Pahlavan, Reza & Omid, Mahmoud & Akram, Asadollah, 2011. "Energy use efficiency in greenhouse tomato production in Iran," Energy, Elsevier, vol. 36(12), pages 6714-6719.
    35. Seiford, Lawrence M. & Zhu, Joe, 2002. "Modeling undesirable factors in efficiency evaluation," European Journal of Operational Research, Elsevier, vol. 142(1), pages 16-20, October.
    36. Mustafa Dinc & Kingsley E. Haynes, 1999. "Sources of regional inefficiency An integrated shift-share, data envelopment analysis and input-output approach," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 33(4), pages 469-489.
    37. Cullinane, Kevin & Wang, Teng-Fei & Song, Dong-Wook & Ji, Ping, 2006. "The technical efficiency of container ports: Comparing data envelopment analysis and stochastic frontier analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(4), pages 354-374, May.
    38. Safa, M. & Samarasinghe, S., 2011. "Determination and modelling of energy consumption in wheat production using neural networks: “A case study in Canterbury province, New Zealand”," Energy, Elsevier, vol. 36(8), pages 5140-5147.
    39. Vennesland, Birger, 2005. "Measuring rural economic development in Norway using data envelopment analysis," Forest Policy and Economics, Elsevier, vol. 7(1), pages 109-119, January.
    Full references (including those not matched with items on IDEAS)


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. repec:gam:jsusta:v:9:y:2017:i:5:p:842-:d:98960 is not listed on IDEAS


    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:eee:rensus:v:76:y:2017:i:c:p:155-162. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.