  The 2030 Agenda of the United Nations (UN) revolves around the Sustainable
Development Goals (SDGs). A critical step towards that objective is identifying
whether scientific production aligns with the SDGs' achievement. To assess
this, funders and research managers need to manually estimate the impact of
their funding agenda on the SDGs, focusing on accuracy, scalability, and
objectiveness. With this objective in mind, in this work, we develop ASDG, an
easy-to-use artificial-intelligence (AI)-based model for automatically
identifying the potential impact of scientific papers on the UN SDGs. As a
demonstrator of ASDG, we analyze the alignment of recent aerospace publications
with the SDGs. The Aerospace data set analyzed in this paper consists of
approximately 820,000 papers published in English from 2011 to 2020 and indexed
in the Scopus database. The most-contributed SDGs are 7 (on clean energy), 9
(on industry), 11 (on sustainable cities) and 13 (on climate action). The
establishment of the SDGs by the UN in the middle of the 2010 decade did not
significantly affect the data. However, we find clear discrepancies among
countries, likely indicative of different priorities. Also, different trends
can be seen in the most and least cited papers, with clear differences in some
SDGs. Finally, the number of abstracts the code cannot identify is decreasing
with time, possibly showing the scientific community's awareness of SDG.
