Consistent and replicable estimation of bilateral climate finance
| Item Type | Journal paper |
| Abstract | International climate finance is key to achieving the goals of the Paris Agreement. Here we develop a machine learning classifier to identify international climate finance from 2.7 million official development assistance projects between 2000 and 2019, resulting in a consistent and replicable inventory of 82,023 bilateral climate finance projects (US$80 billion). Our findings reinforce concerns that the actual numbers may be much lower than current estimates made with Rio markers. |
| Authors | Toetzke, Malte; Stünzi, Anna & Egli, Florian |
| Journal or Publication Title | Nature Climate Change |
| Language | English |
| Subjects | social sciences political science other research area Responsibility and Sustainability e.g. SDGs |
| HSG Classification | contribution to scientific community |
| Refereed | Yes |
| Date | 22 September 2022 |
| Number | 12 |
| Page Range | 897-900 |
| Publisher DOI | https://doi.org/10.1038/s41558-022-01482-7 |
| Depositing User | Dr. Anna Stünzi |
| Date Deposited | 02 Dec 2022 15:37 |
| Last Modified | 02 Dec 2022 15:37 |
| URI: | https://www.alexandria.unisg.ch/publications/268238 |
DownloadFull text not available from this repository.CitationToetzke, Malte; Stünzi, Anna & Egli, Florian (2022) Consistent and replicable estimation of bilateral climate finance. Nature Climate Change, (12). 897-900. Statisticshttps://www.alexandria.unisg.ch/id/eprint/268238
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