European Structural and Investment Funds 2021–2027: Prediction Analysis Based on Machine Learning Models

Abstract

ABSTRACTThis research presents several machine learning algorithms and prediction models to anticipate the European Structural and Investment Funds (ESIF) application in different European Union (EU) countries. These analyses start with data training from 2014 to 2020 ESIF, to test and predict the application of the future ESI Funds for 2021–2027. We deliver an analysis focused on the priorities of each fund, highlighting the differences between the programs in different time periods. In the framework of the European Regional Development Fund (ERDF), we will specifically address the assessment of the following themes: support innovation of small and medium-sized businesses, to greener, low-carbon, and resilient projects with enhanced mobility. In what concerns the European Social Fund (ESF), we will evaluate projects that promote and increase the EU’s employment, social, education, and skills policies, including structural reforms in these areas. Regarding the cohesion funds (CF), we will be targeting the improvements between the two ESIFs, looking at projects in the field of environment and trans-European networks in the area of transport infrastructure (TEN-T). In summary, we will be looking at the future of ESIF through the glasses of artificial intelligence.