论文标题

可持续发展目标和航空工程:通过人工智能的关键说明

The Sustainable Development Goals and Aerospace Engineering: A critical note through Artificial Intelligence

论文作者

Sánchez-Roncero, Alejandro, Garibo-i-Ortsa, Òscar, Conejero, J. Alberto, Eivazi, Hamidreza, Mallor, Fermín, Rosenberg, Emelie, Fuso-Nerini, Francesco, García-Martínez, Javier, Vinuesa, Ricardo, Hoyas, Sergio

论文摘要

2030年联合国议程(联合国)围绕可持续发展目标(SDG)展开。朝着该目标迈出的关键一步是确定科学生产是否与可持续发展目标的成就保持一致。为了评估这一点,资助者和研究经理需要手动估计其资金议程对可持续发展目标的影响,重点关注准确性,可扩展性和客观性。考虑到这一目标,在这项工作中,我们开发了ASDG,这是一种易于使用的人工智能(AI)模型,用于自动识别科学论文对联合国可持续发展目标的潜在影响。作为ASDG的演示者,我们分析了最近航空航天出版物与可持续发展目标的一致性。本文分析的航空航天数据集由2011年至2020年用英文发表的约820,000篇论文组成,并在Scopus数据库中进行了索引。最合并的可持续发展目标是7(在清洁能源上),9(在行业上),11(在可持续城市)和13(关于气候行动)。联合国在2010年中期建立可持续发展目标并没有显着影响数据。但是,我们发现国家之间存在明显的差异,这可能表明了不同的优先事项。同样,在最多和最少的论文中可以看到不同的趋势,在某些可持续发展目标中存在明显的差异。最后,该代码无法识别的摘要数量随着时间而减少,可能显示了科学界对可持续发展目标的认识。

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.

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