The European Regional Development Fund devoted around 66 billion Euros to the financial support of innovation and productivity in European enterprises over the 2014–2020 programming period. In this framework, we assessed the implementation of the Operational Programmes dedicated to fostering research and innovation, particularly in small and medium-sized enterprises. With this aim, we used a network slack-based data envelopment analysis model paired with cluster analysis that encompasses a multitude of performance framework indicators to assess 53 Operational Programmes from 19 countries. Our findings suggest that compared to transition and less developed regions, more developed regions present a higher room for improvement. Also, less developed regions present a better performance when they employ their funding against more developed regions, suggesting that further funding should be channelled for leveraging research and innovation in the former regions. Finally, Operational Programme managers should focus on solving the problems both inherent to the poor outcomes in terms of enhancing the number of researchers working in improved research infrastructures and promoting the technology transfer between research institutions and enterprises.

Funds from the European Union that are devoted to fostering a low-carbon economy are aimed at assisting Member States and regions in implementing the required investments in energy efficiency, renewable energy, and smart distribution electricity grids, and for research and innovation in these areas. In this context, we assessed the implementation of these funds in small and medium-sized enterprises across different beneficiary countries and regions of the European Union. Therefore, this study uses a non-radial slack-based data envelopment analysis model coupled with cluster analysis that covers multiple aspects of evaluation, including two inputs and two outputs, to assess 102 programs from 22 countries. Overall, we were able to ascertain that there are 25 efficient operational programs that remain robustly efficient, whereas 51 remain robustly inefficient for data perturbations of 5 and 10%. Under the current output level, there was almost no input surplus. Therefore, to promote a low-carbon economy, operational program managers should concentrate on solving the problems behind the poor results achieved, both in terms of greenhouse gas emissions reduction and the pace of the programs’ implementation.