Blended finance uses public or philanthropic capital to absorb losses first, so private investors sit in a safer, senior slice of the same portfolio. This lets a small amount of concessional capital "mobilize" a much larger amount of private capital toward climate projects that would otherwise be too risky.
The portfolio holds N = 30 projects of equal size. Each defaults independently with probability p, so the number of defaults k follows a binomial distribution:
P(k defaults) = C(N,k) · p^k · (1-p)^(N-k)
Portfolio loss L(k) = k · (100/N) · LGD (% of committed capital)
Losses then cascade up the stack — a waterfall — absorbed bottom-up:
First-loss absorbs min(L, Lf)
Mezzanine absorbs min(max(L − Lf, 0), Lm)
Senior(private) hit min(max(L − Lf − Lm, 0), 100 − Lf − Lm)
Because private capital is only hit after both junior layers are exhausted, its expected loss is far below the portfolio average — which is exactly what lets it accept a market-rate return. The catalytic ratio = Senior size ÷ First-loss size measures how many dollars of private capital one dollar of public first-loss capital mobilizes.
- First-loss / mezzanine sliders — resize the junior tranches; the remainder is automatically the private senior tranche.
- Default probability / LGD sliders — set the underlying project risk that feeds the binomial model.
- Simulate Year — draws one random realization of defaults, lights up the defaulted project cubes, and animates the loss level rising through the capital stack.
Real-world relevance: this is the structuring logic behind vehicles like the Green Climate Fund's co-financing facilities and MDB-backed climate blended-finance funds — concessional first-loss capital from public sources catalyzing private institutional investment into adaptation and mitigation projects.