Climate Change

The number that shaped a generation of policy

In 2018, William Nordhaus won the Nobel Memorial Prize for a model — DICE — that claimed to compute the “optimal” response to global warming. Its verdict: letting the planet warm by 3–4°C was economically reasonable, because the damage would cost only a few percent of GDP.

That number is not science. It comes from “damage functions” fitted to almost no data and then extrapolated to temperatures no human civilisation has ever experienced. To reach it, the models assume that the large majority of economic activity — roughly 87%, everything that happens indoors — is simply unaffected by climate. As Steve Keen documents in The appallingly bad neoclassical economics of climate change (2021), the profession’s headline estimates aren’t a cautious reading of the evidence; they’re an artefact of assumptions no physical scientist would accept.

Why the mainstream can’t see it

The deeper failure is structural. Neoclassical climate economics treats the economy as a system in equilibrium, nudged by a small, smooth cost. But the economy is not in equilibrium and never has been — it’s a dynamic, energy-driven system embedded in the biosphere, full of feedback loops and tipping points. A model built to find a stable optimum is the wrong instrument for a system whose defining feature is instability.

That’s the tradition this platform comes from. System dynamics — the feedback modelling Jay Forrester carried from control engineering into economics — and stock-flow-consistent monetary modelling let you build the economy as it actually behaves: flows that must balance, debts that compound, energy that constrains, thresholds that break. Minsky and RavelSim are those instruments, made usable.

What we’re building here

Climate is our first front: open, reproducible models of the climate-economy that take the physics seriously — that price in tipping points and energy, and make the financial system’s blindness to physical limits visible instead of assumed away. Competent modellers lead; aspiring ones learn by doing.

If you’ve ever suspected that “4% of GDP” was too neat to be true — you were right. Come model what actually matters.