Faculty of European Studies and Regional Development

New Visegrad fund project from ESG field approved

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Green evaluation of food industries in V4 countries from EU Taxonomy perspective

Project Summary:

In line with the aims of the EU green transition and sustainability process, the consortium led by AKI (Institute of Agricultural Economics Nonprofit Kft., Hungary) will conduct scientific research in V4 food industries regarding sustainability reporting practices from the EU Taxonomy point of view by content and quality analysis of online reports of large companies using four-point scale assessment by the relevant literature, EU Directives and EU Taxonomy.

The project's main aim is to provide comprehensive knowledge about the current situation and future opportunities of food industries' sustainable goals and activities in V4 countries to promote environmental-friendly solutions. The typical target is to find the most effective solutions for the sustainable improvement of food industries in our region and to achieve determining behaviour-changing effects The project aspires to have a lasting impact by influencing the behaviour of companies and stakeholders in the food industry. Moreover, the project's success would likely contribute to advancing sustainable practices in the region.

SUA is currently involved in several high-profile international projects related to food science with an extensive reach. Most notably, COMFOCUS (Horizont 2020), EIT Food Hub (EIT), Food RIS Consumer Engagement Labs (EIT) and 3TforUni (Erasmus+KA2 Strategic partnerships) can be noted. SUA's specialized labs conduct empirical and modelling research in the field of business and agricultural and food economics. In this field, the SUA currently cooperates with several prominent representatives on behalf of the food sector, like Tesco, a.s.; Kaufland, s.r.o.; or Lidl, s.r.o. The cooperation with these partners is multiform. The SUA is a fit and experienced partner in providing research and analysis, including exploratory data analysis and data mining.





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