Climate Change Catwalkers

Look up modeling scams and you’ll find a focus on warning young women about unsavory characters who entice them to part with money on the promise of a glittering career on the catwalk. That's one kind of modeling scam. It’s not the one I’m thinking of. I’m thinking of the one which is endemic within the "climate change" industry. It’s the one that guarantees the “right answer” to governments or patrons with money to splurge; and to hustlers with money to grift.

The Sting (the movie) provides a schematic. In this case, the players are evil fossil fuel companies and government saviors. The set-up – the planet's gonna die of CO2. The hook – cheap renewable energy’s the answer. The shut out – cancel dissenters. Finally, the sting – pay up suckers or face climate Armageddon. A movie is one thing, how does the con work in real life?

Complexity is the answer. Buried inside a continual stream of dense and convoluted reports that no one can digest is the outpourings of arcane computer models that no one can access. Viz, the latest report on the future costs of generating electricity in Australia. The 2023-24 "GenCost" report was issued in late December by the Commonwealth Scientific and Industrial Research Organisation (CSIRO). For light holiday reading perhaps. The December GenCost report superseded its predecessor issued only five months before in July 2023. And just in case the GenCost reports didn’t sufficiently bamboozle brains, the Australian Energy Market Operator also issued its draft 2024 Integrated System Plan in mid-December, replete with 8 appendices and other supporting documentation.

We can be confident that any recent year’s production of these plans, reports, and what have you, would have stretched the capacity of the HAL 9000 computer and perhaps those bulky desk-top computers to be seen in circa 2001 movies. All of them are indigestible. And even if you managed to get your head around one, the next is out before you can say Jack Robinson. Ominously too, this avalanche of documentation "reimagining energy" in Australia, of which I have simply scratched the surface, is as a mere footnote compared with similar yarns in North America and Western Europe.

The GenCost report issued by the CSIRO in July 2023 was heavily criticized for not including costs of transmission and storage in calculating the levelized cost of electricity (LCOE), generated by so-called “variable renewables.” More aptly called, intermittent-cum-unreliable renewables. You might think that an organization which produced a study of the costs of wind and solar power without including the costs of essential infrastructure would be undone.

Au contraire! Out comes the CSIRO in December, sans mea culpa, and simply “addresses the concern” by including costs which should never have been excluded in the first place. But, never fear climateers, renewables remained the “lowest cost” source of power. The computer said so.

If only real life were computer modeling.

The inclusion of transmission and storage costs increases the cost of deploying wind and solar by from 40 to 60 percent, according to the CSIRO. However, once fossil fuels were hit with carbon taxes and extra financing costs, the computer still edged them out of the winner’s circle. And, for good measure, the boot was put into nuclear power by incorporating the specific cost blowout which led to the cancelation of the NuScale project in Utah. Nuclear is “the most expensive form of energy,” according to the Australian government. Make it so, is the riding instruction to those wanting government patronage.

The CSIRO and like organizations have a problem. Real-world data (examined here for example) shows electricity costs increasing as the penetration of wind and solar power increases. The answer? Replace real life with computer modeling. Voila! Out comes the desired outcome.

None of it is remotely real. It occupies a computer-generated golden age in which it is preordained that boundless renewables are the cheapest form of energy. Manipulating parameters, inputs and assumptions is the way it’s done. Keep in mind, any model spitting out results counter to the received narrative would crash and burn, along with its keepers. That’s a powerful incentive for the modelers to get it right.