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

Nataliya Tkachenko; Simon Frieder; Ryan-Rhys Griffiths; Christoph Nedopil

doi:10.3389/frai.2024.1167137

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

Front Artif Intell. 2024 Feb 5:7:1167137. doi: 10.3389/frai.2024.1167137. eCollection 2024.

Authors

Nataliya Tkachenko^{1

2

3}, Simon Frieder⁴, Ryan-Rhys Griffiths⁵, Christoph Nedopil^{6

7

8

9}

Affiliations

¹ Smith School of Enterprise and the Environment, School of Geography and the Environment, University of Oxford, Oxford, United Kingdom.
² UK Centre for Greening Finance and Investment, University of Oxford, Oxford, United Kingdom.
³ The Alan Turing Institute, Finance and Economics, London, United Kingdom.
⁴ Department of Computer Science, University of Oxford, Oxford, United Kingdom.
⁵ Department of Physics, University of Cambridge, Cambridge, United Kingdom.
⁶ Griffith Asia Institute, Griffith University, Brisbane, QLD, Australia.
⁷ Sim Kee Boon Institute for Financial Economics, Singapore Management University, Singapore, Singapore.
⁸ Fanhai International School of Finance, Fudan University, Shanghai, China.
⁹ Belt and Road Initiative Green Coalition, Ministry of Ecology and Environment of People's Republic of China, Beijing, China.

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.

Keywords: DNS; GPT-4; adaptation finance; generative AI; nature finance; nature-based solutions; retrieval augmented generation; sustainable credit finance.

Grants and funding

The funding for this study has been kindly provided by the UK Centre for Greening Finance and Investment, University of Oxford (UK). The authors are very grateful to the Centre's Director Dr. Ben Caldecott for all of his support.