Analyzing global utilization and missed opportunities in debt-for-nature swaps with generative AI

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Tkachenko, N
Frieder, S
Griffiths, RR
Nedopil, C
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2024
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Abstract

We deploy a prompt-augmented GPT-4 model to distill comprehensive datasets on the global application of debt-for-nature swaps (DNS), a pivotal financial tool for environmental conservation. Our analysis includes 195 nations and identifies 21 countries that have not yet used DNS before as prime candidates for DNS. A significant proportion demonstrates consistent commitments to conservation finance (0.86 accuracy as compared to historical swaps records). Conversely, 35 countries previously active in DNS before 2010 have since been identified as unsuitable. Notably, Argentina, grappling with soaring inflation and a substantial sovereign debt crisis, and Poland, which has achieved economic stability and gained access to alternative EU conservation funds, exemplify the shifting suitability landscape. The study's outcomes illuminate the fragility of DNS as a conservation strategy amid economic and political volatility.

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Frontiers in Artificial Intelligence

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7

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© 2024 Tkachenko, Frieder, Griffiths and Nedopil. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use,distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Control engineering, mechatronics and robotics

Artificial intelligence

Machine learning

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Tkachenko, N; Frieder, S; Griffiths, RR; Nedopil, C, Analyzing global utilization and missed opportunities in debt-for-nature swaps with generative AI, Frontiers in Artificial Intelligence, 2024, 7

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