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This research provides an analytical and predictive framework, based on state-of-the-art machine-learning (ML) algorithms (random forest (RF) and generalized additive models (GAM)), that can be used to assess and improve the Common Agricultural Policy (CAP) impact/performance over the agricultural and rural environments, easing the identification of proper instruments that can be used by EU policy makers in CAP’s financial management. The applied methodology consists of elaborating a custom-developed analytical framework based on a dataset containing 22 relevant indicators, considering four main dimensions that describe the intricacies of the EU agricultural and rural environment, in the CAP context: rural, emissions, macroeconomic, and financial. The results highlight that an increase of the agricultural research and development funding, as well as the agriculture employment rate, negatively influence the degree of rural poverty. The rural GDP per capita is influenced by the size of the employment rate in agriculture. It seems that environmental sustainability, identified by both fertilizers used and emissions from agriculture parameters, significantly influences the GDP per capita. In predicting emissions in agriculture, the direct payment, degree of rural poverty, fertilizer use, employment in agriculture, and agriculture labor productivity are the main independent parameters with the highest future importance. It was found that when predicting direct payments, the rural employment rate, employment in agriculture, and gross value added must be considered the most. The agricultural, entrepreneurial income prediction is mainly influenced by the total factor productivity, while agricultural research and development investments depend on gross value added, direct payments, and gross value added in the agricultural sector. Future research, related to prediction models based on CAP indicators, should also consider the marketing dimension. It is recommended for direct payments to be used to invest in upgrading the fertilizers technologies, since environmental sustainability will influence economic growth.

Introduction: Organizations currently emphasize green marketing strategies by implementing marketing practices, aiming to design, produce, promote and sell green products. Thus, both consumers and producers have turned their attention to the category of environmental friendly products, taking into account that the concept of green marketing is now being given greater importance. Responsible organizations have begun to adapt their strategies in production, promotion and interaction activities with consumers or potential consumers of organic products in the context we are going through, when environmental protection becomes an imperative. Analysis of the sustainable behavior of Generation Z is a determining factor from the perspective of the task that this generation will naturally take on, in terms of environmental responsibility.Methods: The research aims to determine the profile of the Generation Z consumer, in order to adapt the strategic actions of the government or organizations to direct and educate as objectively and efficiently as possible towards adopting the principles of ecological, sustainable and responsible consumption. Based on the data collected through a survey, we analyzed the sustainable behavior of Generation Z consumers studying at Romanian universities where there are specializations in this field. The research is quantitative, using structural equation modelling with partial least squares (PLSSEM) to test the hypotheses regarding the relationship between the determining factors and the sustainable behavior of Generation Z consumers.Results and Discussion: The results show that there is a positive relationship between both the sustainable behavior of Generation Z consumers and the satisfaction it conveys to them, as well as their environmental protection activities. However, there is no relationship between the sustainable behavior of Generation Z consumers and the green marketing practices of the organizations, environmental issues and their identification with the environmentally responsible consumer.

This paper provides a conceptual architecture for a cloud based platform design, that implements continuously data storage and analysis services for large maritime ships, with the purpose to provide valuable insights for maritime transportation business. We do this by first identifying the need on the shipping market for such kind of systems and also the significance and impact of different factors related to shipping business processes. The architecture presented throughout this paper will be defined around some of the most currently used ICT technologies, like Amazon Cloud Services, Sql Server Databases, .NET Platform, Matlab 2016 or javascript visualization libraries. The proposed system makes possible for a maritime company to gain more knowledge for optimizing the efficiency of its operations, to increase its financial benefits and its competitive advantage. The platform architecture was designed to make possible the storage and manipulation of very large datasets, also allowing the possibility of using different data mining techniques for inferring knowledge or to validate already existent models. Ultimately, the developed methodology and the presented outcomes demonstrate a vast potential of creating better technological management systems for the shipping industry, starting from the challenges but also from the huge opportunities this sector can offer.

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