.png){kind=icon}
返回
Monitoring global development aid with machine learning
.png){kind=icon}
OA
AI
DOI：10.1038/s41893-022-00874-z.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Monitoring global development aid provides important evidence for policymakers financing the Sustainable Development Goals (SDGs). To overcome the limitations of existing monitoring, we develop a machine learning framework that enables a comprehensive and granular categorization of development aid activities based on their textual descriptions. Specifically, we cluster the descriptions of similar to 3.2 million aid activities conducted between 2000 and 2019 totalling US$2.8 trillion. As a result, we generated 173 activity clusters representing the topics of underlying aid activities. Among them, 70 activity clusters cover topics that have not yet been analysed empirically (for example, greenhouse gas emissions reduction and maternal health care). On the basis of our activity clusters, global development aid can be monitored for new topics and at new levels of granularity, allowing the identification of unexplored spatio-temporal disparities. Our framework can be adopted by development finance and policy institutions to promote evidence-based decisions targeting the SDGs.
期刊
IF:
27.1
论文数:
1.9K
被引数:
2.5W
机构
引用论文
The trouble with the MDGs: Confronting expectations of aid and development success千年发展目标的麻烦: 面对对援助和发展成功的期望.png)
WORLD DEVELOPMENT
OAAI
WORLD DEVELOPMENTIF4.8
OAAI分享收藏
查看更多内容