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

G. Vlontzos and P.M. Pardalos

Renewable and Sustainable Energy Reviews, 2017, vol. 76, issue C, 155-162

Abstract: 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.

Keywords: DEA Window analysis; Artificial Neural Networks; Environmental efficiency; Greenhouse gas emissions (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (28)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1364032117303489
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:rensus:v:76:y:2017:i:c:p:155-162

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/bibliographic
http://www.elsevier. ... 600126/bibliographic

DOI: 10.1016/j.rser.2017.03.054

Access Statistics for this article

Renewable and Sustainable Energy Reviews is currently edited by L. Kazmerski

More articles in Renewable and Sustainable Energy Reviews from Elsevier
Bibliographic data for series maintained by Catherine Liu ().