Linking the ‘Recovery and Resilience Plan’ and Smart Specialisation. The Greek Case

Publication article | | TOLIAS Yannis

This work analyses the alignment of investments in the Greek Recovery and Resilience Plan (‘the Greek RRP’) with the Smart Specialisation Strategies priorities (2021-2027) of the country and its regions to identify potential synergies and complementarities between funding instruments. The structure and methodology follows the approach that was applied in the Portuguese case by Marques Santos (2021). This methodology uses the information available in the Plan and its annexes and establishes the steps for carrying out a detailed analysis to identify and to classify the investments and actions able to enhance Research and Development and Innovation (R&I) and regional innovation ecosystems. The analysis indicates that up to €0.91billion of the Greek RRP (5.1% of the non-repayable EU support) directly supports the Smart Specialisation processes in Greece by providing for new or upgraded research infrastructures, mobility of researchers, and basic research in public-sector research organisations (universities and research centres), without explicitly earmarking funding for the smart specialisation priorities at the national or at the regional level. Moreover, an additional amount of €1.14billion (6.4% of the non-repayable EU support) contributes in an indirect manner to agri-food, tourism/creative industries and reskilling/upskilling, which are all smart specialisation priorities both at the national and at the regional level. Concluding, the Greek RRP mobilises a considerable amount of public funds and stimulates additional investments in a very short timeframe. The main challenge will be valorising these investments with appropriate policy measures using the approximately €5billion that are earmarked for policy objective 1 in the national and the regional operational programmes of this programming period, which by design are fully aligned with the national S3.

Series

JRC Working Papers on Territorial Modelling and Analysis No 09/2022

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