Renewable Capital Race — Learning-Curve Investment Simulator

Policy Levers

Carbon price $60/t

Feed-in tariff $20/MWh

Learning-rate sensitivity 1.0×

Simulation speed 2 yr/s

Market Snapshot

Simulated year

2026

Solar capex

$0.85M/MW

Wind capex

$1.30M/MW

Storage capex

$0.28M/MWh

CO₂ avoided

0 Mt

This tick's capital share

Solar —

Wind —

Storage —

How it works

Each technology's cost follows Wright's Law (the empirical rule behind real solar/battery price declines — a.k.a. "Swanson's Law"): cost falls by a fixed fraction every time cumulative installed capacity doubles.

capex(t) = capex₀ · (cumulative / cumulative₀) ^ (−b)
b = −log₂(1 − learningRate)

A carbon price and feed-in tariff pay solar and wind a credit over their ~20-year lifetime, which lowers their effective capital cost relative to storage (which earns neither):

credit  = (carbonPrice·emissionFactor + tariff) · CF · 8760h · 20yr
effCost = capex − credit   (floored at 15% of capex)

Each simulated tick, a shared global capital pool (modeled at $400B/yr) splits across the three technologies in inverse proportion to effective cost, raised to a power that sharpens the competition:

weight_i = (1 / effCost_i)³
share_i  = weight_i / Σ weight
ΔcapacityI = (pool · share_i) / capex_i

The glowing particles streaming from the central pool to each tower are that capital allocation, spawned at a rate proportional to share_i. Tower height tracks log₂(cumulative capacity), so each full tower-height step is one doubling — directly visualizing the learning curve. Raise the carbon price or the tariff and watch solar/wind pull effective cost down and out-compete storage for the next dollar; raise "learning-rate sensitivity" to see how much of the whole race is decided by how fast each technology gets cheaper as it scales.

2026

Solar PV Wind Battery storage

⚠ Couldn't load the 3D engineThree.js failed to load from the CDN. Check your connection and reload.