About a year ago, a group of scholars at Oxford University's Institute for New Economic Thinking released a working paper that made a considerable splash in the world of energy nerds. It has now been peer-reviewed and published in the journal Joule. It is called “Empirically grounded technology forecasts and the energy transition,” which, I think you'll agree, is a title that really gets the blood pumping.

At the heart of the paper is a new way of forecasting technology costs that is more grounded in history and empirical data than the integrated assessment models (IAMs) used by organizations like the IPCC and the IEA. Those models have notoriously overestimated the future costs of clean energy technologies, and consequently counseled insufficient climate action, for decades now.

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The Oxford scholars take a different approach, centered on technology "learning curves” (sometimes called “experience curves”). They begin by noting:

The prices of fossil fuels such as coal, oil, and gas are volatile, but after adjusting for inflation, prices now are very similar to what they were 140 years ago, and there is no obvious long-range trend. In contrast, for several decades the costs of solar photovoltaics (PV), wind, and batteries have dropped (roughly) exponentially at a rate near 10% per year.

Those key clean-energy technologies are on learning curves. For technologies on a learning curve, “costs drop as a power law of cumulative production.” Another way of saying that is, for every doubling of cumulative production, per-unit costs fall by X percent.

What that X figure is will vary among different technologies (and for many if not most technologies, there will be no learning curve at all), but the somewhat eerie thing is, for a given technology, X — the rate of learning — tends to persist over time within a relatively narrow band. Learning curves, historically, have been quite predictable and steady.

Learning curves are the subject of a rich and long-standing literature. What’s novel about the Oxford paper is that it develops a new method of forecasting technology costs grounded in established historical learning curves.

The forecasts make probabilistic bets that technologies on learning curves will stay on them. If that's true, then the faster we deploy clean energy technologies, the cheaper they will get. If we deploy them fast enough reach net zero by 2050, as is our stated goal, then they will become very cheap indeed — cheap enough to utterly crush their fossil fuel competition, within the decade. Cheap enough that the most aggressive energy transition scenario won't cost anything — it will save over a trillion dollars relative to baseline.

We've gotten the sign wrong: the transition to clean energy is not a cost, it's a benefit. The implication is that it makes overwhelming sense to rapidly transition to clean energy technologies, without even counting climate and air pollution benefits. That's why the paper made a splash.

To discuss it, I called one of its co-authors, Doyne Farmer. Farmer is a longtime scientist and entrepreneur who has studied complex systems in physics, biology, and economics. His scientific accomplishments are too many to relate, but among them has been much recent work on learning curves and their dynamics.

I called him to talk about how his projections differ from the conventional sort, what other technologies might get on learning curves, and the implications of his forecasts for the broader energy transition.