Assess and Prognosticate Operational and Environmental Efficiency of Primary Sectors of EU Countries: Implementation of DEA Window Analysis and ANNs

Assess and Prognosticate Operational and Environmental Efficiency of Primary Sectors of EU Countries: Implementation of DEA Window Analysis and ANNs

George Vlontzos (University of Thessaly, Greece) and Panos M. Pardalos (University of Florida, USA)
Copyright: © 2017 |Pages: 19
DOI: 10.4018/978-1-5225-2107-5.ch001
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Efficiency assessment in agriculture is a research field were quite important methodologies have been implemented. Data Envelopment Analysis (DEA) in one of the most recognized approaches due to the considerable advantages of it. In this paper the implementation of DEA Window analysis assesses efficiency scores of the primary sectors of EU member states on both operational and environmental level, verifying considerable efficiency differences among them and a continuous improvement after the application of the latest Common Agricultural Policy (CAP) reform. Regarding prognostication of crop and animal output, as well as Green House Gas (GHG) emissions, the application of Artificial Neural Networks (ANNs) is being proposed, succeeding satisfactory quality characteristics for the models being proposed for operational and environmental predictions in EU agriculture.
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It is a continuous goal of the European Union (EU) Common Agricultural Policy (CAP) to improve both operational and environmental efficiency of agricultural holdings, aiming by this way to increase the competitiveness of EU primary sectors as a whole in a globalised production and trading framework. The quantification of this approach is being expressed by the 20-20-20 strategy which focuses on increasing the energy efficiency by 20%, reducing the CO2 emissions by 20% and produce 20% of overall energy consumed by renewable energy resources (European Commission, 2011). One of the most important policy reforms for the EU agriculture was the implementation of the Agenda 2000, with the establishment of a totally new framework for subsidies management, decoupled from both crop and animal production. Since the year 2005 the new subsidy scheme has come into force, providing by this way the ability to the EU to fully comply with the last World Trade Organization (WTO) agreement of the Uruguay Round (European Commission, 2013). Under this new framework, the subsidy scheme has a pure supportive role on the producers’ income, increasing by this way the impact of their managerial decisions on the improvement of efficiency of their holdings.

Up to now, the implementation of Data Envelopment Analysis (DEA) has contributed substantially towards this goal.The non-parametric approach of this methodology, in accordance with the absence of a priori assumptions formulates a framework where it is easily applicable. Another quite important advantage of it is the ability to use multiple inputs and outputs for efficiency assessment, increasing by this way the objectivity of the results being obtained when handling real life tasks. Therefore, it has been used for efficiency assessment of very important sectors of the economy, like banking and health care. Nevertheless, it has also been used in various cases in agriculture, as well as in the food industry, trying to evaluate the efficiency rates of inputs used, as well as the outputs achieved. For almost every sector of the economy there is a variation of efficiency goals, beyond the typical economic ones. Consumers demand for the adoption of environmentally friendly production methods, in addition with the need for a continuous increase of energy efficiency, outlined new perspectives for the use of DEA with fairly successive research results. The European primary sector, especially after the implementation of the Agenda 2000, has put a lot of effort to meet these goals, by applying a series of policies focused on improving the environmentally friendly profile of production methods, as well as increasing their energy efficiency. Due to the fact that it is important for policy makers to detect efficiency trends overtime, the DEA Window analysis is a suitable tool for such assessments. It has been used for rather diversified economic activities, verifying by this way the validity of the method and its acceptance as a research tool by researchers (Yang and Chang, 2009; Pjevcevic et al, 2012). In this paper it is used to assess efficiency and identify efficiency change of EU countries primary sectors in operational, and environmental terms, as the outcome of significantly diversified agricultural policies, quantifying by this way their positive or negative impact, providing at the same time hints for counteractive actions.

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