Agroforestry Carbon Credit Market

Model tree-planting density, carbon-credit pricing and farmer income in an agroforestry carbon market program.

Why this matters

Model tree-planting density, carbon-credit pricing and farmer income in an agroforestry carbon market program.

This model is adapted from an internal scenario-planning tool, distilled here into three linked calculations that mirror how real operators, engineers and analysts reason about the system.

How the model works

Tree density (trees/ha) — Annual CO₂ sequestration across 120 ha at 6.5 t/ha/yr biomass growth, converted at 0.45 carbon fraction × 3.67 CO₂ ratio.
Credit price ($/t CO₂) — Net profit after $14/ha verification cost on 120 ha, once credits clear a 420 t CO₂/yr baseline.
Farmers enrolled (ratio) — Community resilience benefit index combining enrolled farmers with a fixed 0.68 resilience score.

Reading the results

Each control drives one of three underlying formulas taken from the source engineering model. Moving a slider recomputes its metric instantly and updates the 3D bar in the simulation — taller, brighter bars mean the system is closer to its optimum operating envelope. Try pushing each parameter to its extreme to see where the model breaks down or saturates.

Frequently Asked Questions

What is agroforestry carbon credit market used for?

Model tree-planting density, carbon-credit pricing and farmer income in an agroforestry carbon market program.

Is this a real-world engineering model or a toy?

The underlying formulas are simplified versions of real planning heuristics used in this domain — accurate enough to show the right trends and trade-offs, but not a substitute for full engineering simulation software.

Can I use my own numbers?

Yes — every slider in the simulation maps directly onto one of the model's input variables, so you can explore scenarios well outside the defaults shown here.

Why does the bar height saturate at the extremes?

Each metric is normalised to a 0–1 range against a realistic reference ceiling from the source model, so very large inputs will visually cap out even though the underlying number keeps growing.