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This paper focuses on whether the on-going dramatic decrease in alcohol consumption in Italy, especially of wine, during 1961-2008, was associated with which parallel sociodemographic and economic changes and with alcohol control policies. The study, using both time series (TS) and artificial neural network (ANN)-based analyses documents that its selected sociodemographic and economic factors, and particularly urbanization, had a definite connection with wine consumption decrease, spirits decrease, and the increase in beer consumption over time. On the other hand, control policies showed no effect on the decline in alcohol consumption, since no alcohol control policy existed in Italy between 1960 and 1987. A few policies introduced since 1988 (BAC and sale restrictions during mass events) may have contributed to reducing or to maintaining the on-going reduction. Study limitations are noted and future needed research is suggested. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The present paper discusses a novel methodology based on neural network to determine agriculture emission model simulations. Methane and nitrous oxide are the key pollutions among greenhouse gases being a major contribution to climate changes because of their high potential global impact. Using statistical clustering (k-means and Ward’s method), five meaningful clusters of countries with similar level of greenhouse gases emission were identified. Neural modeling using multi-layer perceptron networks was performed for countries placed in particular groups. The parameters that characterize the quality of a network are the predictive errors (mainly validation and test) and they are high (0.97-0.99). The use of sensitivity analysis allowed for identifying the variables that have a significant influence on the greenhouse gases emissions. The sensitivity analysis of the designed artificial neural network models shows a few dominant variables, affecting emissions with varied intensity: cattle and buffaloes, sheep and goat populations, afforestation as well as electricity consumption. The observed values were compared with those predicted by the models. The forecasted course of changes in the variable test is identical with the real data, which proves that the model highly matches to the observed data. [ABSTRACT FROM AUTHOR]

The study identifies changes in selected ('unplanned') socio-demographic and economic factors as well as in (planned) political measures that are most strongly correlated with changes in alcohol consumption and alcohol consumption-related harm between 1961 and 2006 in Austria. During the period of investigation consumption increased until the early 1970s, dropped during the next decade and have leveled off since. Increasing urbanization, female employment and average age of mothers at their child births are associated with the best time series model for the interpretation of consumption changes. The results regarding alcohol control policies and their impact on consumption were paradoxical. Study limitations were noted pointing up the necessity to improve indicators and concepts. [ABSTRACT FROM AUTHOR]