Every year, governments borrow trillions of dollars to function. The interest rate they pay depends almost entirely on their credit rating a number assigned by a handful of private companies that assess how likely a country is to default. Those ratings shape public investment in hospitals, schools, and infrastructure worldwide. And right now, they are missing something large.
A study published this week in Nature Ecology & Evolution finds that the collapse of fisheries, forests, and wild pollinators already underway in many parts of the world could add US$162 billion to the annual debt bills of just 23 countries. China alone could face an extra US$70 billion a year in interest payments. India, US$49 billion equivalent to 2.4% of the median citizen’s disposable income, every year, just to cover the additional risk premium on money the government has already borrowed.
None of this is currently factored into any major sovereign credit rating. The researchers argue that means financial markets are mispricing more than US$83 trillion in assets.
B. Sovereign credit rating changes under both scenarios. *Madagascar’s rating is model-estimated, as S&P issued no rating for it during 2015–2020.
Why does this happen? Ecosystem services the productive contributions of nature to agriculture, forestry and fishing feed directly into GDP. GDP per capita turns out to be the single most powerful predictor in the researchers’ credit rating model. So when nature degrades and economic output falls, governments look riskier to lenders, who demand higher interest rates. The relationship isn’t linear, which matters a lot in practice. Countries whose GDP levels happen to sit near a threshold in the rating scale get hit disproportionately hard: a relatively modest GDP loss tips them down a notch, which triggers a jump in borrowing costs far larger than the underlying economic damage would suggest. That’s why China and Malaysia, with GDP losses of around 3–4% in the severe scenario, end up with larger rating downgrades than some countries with bigger absolute economic hits.
The really troubling part for lower-income countries is what sits at the far end of this logic. Angola, the Democratic Republic of Congo and Madagascar fall entirely off the ratings scale under the severe scenario meaning the model can no longer assign them a rating at all. In practical terms, the researchers say, that would likely mean sovereign default.
To do all of this, the team built a machine learning model a random forest classifier trained on data from 113 countries between 2015 and 2020. They fed six macroeconomic variables into it (things like GDP per capita, debt-to-GDP ratios, and current account balance) and taught it to predict S&P sovereign credit ratings. Then they plugged in outputs from a separate, previously published ecological-economic model called GTAP-InVEST, which estimates how GDP changes when specific ecosystem services decline. This let them trace a path from nature loss, through economic damage, through the rating model, to borrowing costs. One of the co-authors is a former Chief Sovereign Rating Officer at S&P, which informed how they structured the exercise to mirror how rating committees actually work.
They ran two scenarios. The milder one assumes current rates of ecosystem degradation continue through 2030 even then, Indonesia, Bangladesh, India and China each get downgraded at least one notch. The severe one is the partial ecosystem collapse described above, which functions as a stress test rather than a forecast.
Now for where it falls short, and the authors are reasonably candid about this. The study covers 23 countries, limited by the availability of GTAP-InVEST data at national resolution, so the global picture is incomplete. More importantly, these are simulations, not predictions. The model cannot tell you the probability that any of these scenarios actually happens it can only show you what the financial consequences would be if they did. The severe scenario is ecologically defensible (pollinator and fisheries collapses of this magnitude have historical precedents, the authors note), but whether and when they occur is genuinely uncertain, and tropical forest tipping points in particular remain scientifically contested.
The study is also limited to three ecosystem services specifically because those are the only ones for which globally consistent spatial data exist and can be connected to economic sectors in the GTAP-InVEST model. Water regulation, flood protection, soil retention — none of those are included. The authors say the results should therefore be read as lower bounds. The underlying ecological-economic model is also a comparative static tool, not a dynamic one: it can compare a baseline world against a degraded one, but it can’t model how feedbacks evolve over time or how financial markets themselves might respond to deteriorating ecosystems before a formal downgrade occurs.
The practical argument the paper is pushing is fairly straightforward: credit rating agencies are mispricing US$83 trillion in sovereign debt by ignoring risks they could, in principle, incorporate. The methodology the researchers built is a proof of concept that you can integrate ecological science into standard financial risk tools without exotic new metrics. Whether the agencies will act on this is a different question entirely but the paper at least demonstrates that “we don’t know how to model it” is no longer a sufficient excuse.
Reference:
Agarwala, M., Burke, M., Klusak, P., Kraemer, M., Volz, U., & Sovacool, B. K. (2026). Biodiversity loss will decrease the future creditworthiness of nations. (Nature Ecology & Evolution.)
