IDEAS home Printed from
MyIDEAS: Log in (now much improved!) to save this paper

Comparing Environmental Impact of Alternative CAP Scenarios Estimated Through an Artificial Neural Network

Listed author(s):
  • Andrea BONFIGLIO


Registered author(s):

    The paper aims to assess environmental impact produced by alternative Common Agricultural Policy (CAP) scenarios in the Italian Marche region for the period 2000-2002. Scenarios concern alternative hypotheses about direct payments for arable crops related to Agenda 2000. For this aim, a Multilayer Feedforward Neural Network model (MFNN) was applied. Different from traditional models, MFNN is able to analyze complex patterns quickly and with a high degree of accuracy. Moreover, MFNN makes assumptions about neither the underlying population nor the existence of optimising behaviour and uses the data to develop an internal representation of the complexity characterising the system analysed. The results indicate that direct payments produced positive environmental effects compared to the hypothesis of absence of direct payments. Moreover, they show that it would have been even better, from an environmental point of view, if Agenda 2000 had been more radical in comparison to the 1992 Mac Sharry reform, by introducing decoupled direct payments.

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

    File URL:
    File Function: First version, 2006
    Download Restriction: no

    Paper provided by Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali in its series Working Papers with number 269.

    in new window

    Length: 24
    Date of creation: Oct 2006
    Handle: RePEc:anc:wpaper:269
    Contact details of provider: Postal:
    Piazzale Martelli, 8, 60121 Ancona

    Phone: +39 071 220 7100
    Fax: +39 071 220 7102
    Web page:

    More information through EDIRC

    References listed on IDEAS
    Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

    in new window

    1. Nowrouz Kohzadi & Milton S. Boyd & Iebeling Kaastra & Bahman S. Kermanshahi & David Scuse, 1995. "Neural Networks for Forecasting: An Introduction," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 43(3), pages 463-474, November.
    Full references (including those not matched with items on IDEAS)

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    When requesting a correction, please mention this item's handle: RePEc:anc:wpaper:269. 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: (Maurizio Mariotti)

    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 references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link 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 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.

    This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.