Assessing the relevance of R&D funding towards societal goals: Insights from new data sources and AI-assisted methods

Leonidas Aristodemou, Silvia Appelt, Brigitte van Beuzekom and Fernando Galindo-Rueda

No 2025/25, OECD Science, Technology and Industry Working Papers from OECD Publishing

Abstract: This paper presents and demonstrates a novel approach for assessing the relevance of public R&D funding towards societal goals. The approach relies on the development of a new machine learning classification model for R&D activity descriptions, trained on researchers’ self-assessments about the relevance of their R&D activity towards Sustainable Development Goals (SDGs). Applied to R&D project descriptions in the OECD Fundstat database, the model shows how funding, including for basic research, contributes towards societal goals. The analysis allows to compare funding portfolios and provides evidence of the interdependencies between societal goals served by R&D funding and its potential to contribute to multiple goals.

JEL-codes: C38 C45 C55 H59 O32 O38 Q01 Q55 (search for similar items in EconPapers)
Date: 2025-11-26
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Persistent link: https://EconPapers.repec.org/RePEc:oec:stiaaa:2025/25-en

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