Artificial intelligence reveals unbalanced sustainability domains in funded researchShow others and affiliations
2025 (English)In: Results in Engineering (RINENG), ISSN 2590-1230, Vol. 25, article id 104367Article in journal (Refereed) Published
Abstract [en]
To meet the 2030 Agenda for Sustainable Development, all Sustainable Development Goals (SDGs) must receive adequate and balanced funding. This study applies artificial intelligence to analyze research proposals accepted between 2015 and 2023 in the European Union and the United States, focusing on datasets from the European Research Council and the National Science Foundation, respectively. Despite the growing application of Artificial Intelligence (AI) in various domains, there remains a lack of comprehensive analysis that applies AI to examine funding allocation across SDGs and gender disparities in scientific research. This study addresses this unmet need by using AI to uncover imbalances in funding distribution, offering insights into current funding instruments. We reveal critical coverage disparities across SDGs, with both funding instruments prioritizing SDG 9 (Industry, Innovation, and Infrastructure), highlighting a potential overemphasis on this goal. Additionally, we document pronounced gender imbalances among principal investigators across nearly all SDGs, except for SDG 5 (Gender Equality), in which female researchers are comparatively better represented. Our results indicate an urgent need for more inclusive and balanced approaches to achieve sustainable development, starting with allocation of research funding. By providing a nuanced understanding of funding dynamics and advocating strategic reallocations, this study offers actionable policy design and planning insights to foster a more equitable and comprehensive support system for sustainability-focused research endeavours.
Place, publisher, year, edition, pages
Elsevier BV , 2025. Vol. 25, article id 104367
Keywords [en]
AI, ChatGPT, ERC, Funding Research, NSF, Scientific Funding, SDGs, Sustainability
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-360576DOI: 10.1016/j.rineng.2025.104367Scopus ID: 2-s2.0-85217788764OAI: oai:DiVA.org:kth-360576DiVA, id: diva2:1940642
Note
QC 20250227
2025-02-262025-02-262025-02-27Bibliographically approved