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.
翻译:2030年联合国议程围绕可持续发展目标(SDGs),实现这一目标的一个关键步骤是确定科学生产是否符合SDGs的成就。评估时,供资者和研究管理人员需要手动估计其供资议程对可持续发展目标的影响,重点是准确性、可缩放性和客观性。铭记这一目标,我们在这项工作中开发了ASDG,这是自动确定科学文件对联合国可持续发展目标潜在影响的容易使用的人工智能(AI)模型。作为ASDG的示范方,我们分析最近航空航天出版物与SDGs的匹配情况。本文分析的航空航天数据集包括2011年至2020年以英文出版的约820 000篇论文,并编入Scopus数据库。贡献最多的SDG是7篇(关于清洁能源)、9篇(关于工业)、11篇(关于可持续城市)和13篇(关于气候行动)。2010年中期联合国建立SDGs,可能不会显著地影响科学数据的变化。最后,我们发现,在所引用的SDG中,各国之间可能存在的明显差异,在2010年中期,也不可能在最明显地显示科学数据中显示最明显的差异。