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DOI10.1038/s41893-022-00874-z
Monitoring global development aid with machine learning
Toetzke, Malte; Banholzer, Nicolas; Feuerriegel, Stefan
发表日期2022
ISSN2398-9629
起始页码533
结束页码541
卷号5期号:6页码:9
英文摘要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.
学科领域Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies
语种英语
WOS研究方向Science & Technology - Other Topics ; Environmental Sciences & Ecology
WOS记录号WOS:000782220400001
来源期刊NATURE SUSTAINABILITY
文献类型期刊论文
条目标识符http://gcip.llas.ac.cn/handle/2XKMVOVA/272772
作者单位Swiss Federal Institutes of Technology Domain; ETH Zurich; Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Munich
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Toetzke, Malte,Banholzer, Nicolas,Feuerriegel, Stefan. Monitoring global development aid with machine learning[J],2022,5(6):9.
APA Toetzke, Malte,Banholzer, Nicolas,&Feuerriegel, Stefan.(2022).Monitoring global development aid with machine learning.NATURE SUSTAINABILITY,5(6),9.
MLA Toetzke, Malte,et al."Monitoring global development aid with machine learning".NATURE SUSTAINABILITY 5.6(2022):9.
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