Whatever Tucker Carlson may lie about to his audience, climate change is real and is caused by humans putting greenhouse gases into the atmosphere. Which, as a consequence, means we have to do two things:
- We have to deal with the consequences (like Ian) through adaptation and resilience, AND
- We have to stop making the problem worse — which means getting off fossil fuels ASAP.
We have more and more data all the time showing what is happening to climate. The economic toll keeps rising as we get more and more climate events made worse by global warming. We have technology that can make it possible to transition away from fossil fuels. And now we have research that suggests the big show-stopper is not as big as we’ve been estimating:
How much this will cost us?
Dave Roberts at Volts points at new ways of modeling transition costs coming out of Oxford that appear to show the current models are not giving us an accurate prediction. It's discussed in a podcast:
Learning curves will lead to extremely cheap clean energy
Doyne Farmer discusses the explosive implications of his new research.
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.
emphasis added
To cut to the chase,
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.
...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.
emphasis added
Here’s the abstract from the working paper that is getting attention:
** 21st Sept 2022 - A published version of this paper is now available here: https://www.cell.com/joule/fulltext/S2542-4351(22)00410-X
Rapidly decarbonising the global energy system is critical for addressing climate change, but concerns about costs have been a barrier to implementation. Most energy-economy models have historically underestimated deployment rates for renewable energy technologies and overestimated their costs. The problems with these models have stimulated calls for better approaches and recent efforts have made progress in this direction. Here we take a new approach based on probabilistic cost forecasting methods that made reliable predictions when they were empirically tested on more than 50 technologies. We use these methods to estimate future energy system costs and find that, compared to continuing with a fossil-fuel-based system, a rapid green energy transition will likely result in overall net savings of many trillions of dollars - even without accounting for climate damages or co-benefits of climate policy. We show that if solar photovoltaics, wind, batteries and hydrogen electrolyzers continue to follow their current exponentially increasing deployment trends for another decade, we achieve a near-net-zero emissions energy system within twenty-five years. In contrast, a slower transition (which involves deployment growth trends that are lower than current rates) is more expensive and a nuclear driven transition is far more expensive. If non-energy sources of carbon emissions such as agriculture are brought under control, our analysis indicates that a rapid green energy transition would likely generate considerable economic savings while also meeting the 1.5 degrees Paris Agreement target.
Please follow this link to view the supplementary information for this paper:
https://www.dropbox.com/s/fwlsys15aa4l5b9/Way_et_al_2021_energy_transition-INET-working-paper-SI.pdf
emphasis added
In short, the more we expand building out the renewable energy technologies discussed here, the better. One of the theories underpinning this is Wright’s Law:
...Moore’s Law focuses on time whereas Wright’s Law focuses instead on the number of units produced. For each doubling of production, the associated cost drops by a certain percentage. In this rule of thumb, it is 20 percent.
...If we have so far produced 1000 units of a product, then the cost per unit will decrease by 20% when production reaches 2000 units. Another 20% reduction happens at 4,000, then another at 8,000, and so on.
If the cost of producing 1000 units is $100, then the cost of unit number 2000 will be $80. For unit number 4000, the cost will be $64, and for unit number 8000 it will be $51.
Three doublings in production, therefore, cut the unit cost almost in half.
Researchers at MIT have compared several of these laws. They went through historical data for 62 different technologies and concluded that Wright’s Law comes closest to the truth, closely followed by Moore’s Law.
emphasis added
Shorter version: Double the production, the costs drop by X percent. The drop in costs reach a point where they begin to more than match the cost of not doing it, considering what will be spent just maintaining the status quo and the consequences of climate.
The podcast with the Doyne Farmer interview is about an hour long. Among other things, it discusses why learning curves are a big factor in this and how they examined that idea to test it. One of the things that makes it interesting is that Farmer is not afraid to say “I don’t know” when asked to explain why this happens — just that they keep seeing it again and again.
He is also not afraid to discuss unknowns and possible limits on this. One of them, for example, is building out the grid. A big one is perhaps the hardest to address: politics. What’s optimum and what’s politically feasible does not necessarily intersect.
The link to Volts shows some of the graphs plotting out the different energy sources, including fossil fuels, to show how costs are charging.
So, if this analysis of the effect of learning curves holds up, we are looking at an energy transition that is far easier to reach than we’ve been expecting. Now if we follow up on this and expand our efforts and it doesn’t produce the predicted cost savings, the worst that happens is that we still end up where we need to be. If they do hold up, we get where we need to be for a lot less money than we fear.
That doesn’t seem like the worst bet to take.