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

• Main Authors: Tkachenko, Nataliya, Frieder, Simon, Griffiths, Ryan-Rhys, Nedopil, Christoph
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: DNS; retrieval augmented generation; nature-based solutions; sustainable credit finance; nature finance; adaptation finance; generative AI; GPT-4; Business Law, Public Responsibility, and Ethics; Finance and Financial Management
• Online Access: https://ink.library.smu.edu.sg/skbi/39; https://ink.library.smu.edu.sg/context/skbi/article/1038/viewcontent/frai_07_1167137.pdf
• Institution: Singapore Management University

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.