co2-data VS PyPSA

Ich hab das Mal anhand dieses Datensatzes recherchiert https://github.com/owid/co2-data.

Scroll down to "Global atmospheric CO2 concentration" on https://ourworldindata.org/co2-and-other-greenhouse-gas-emissions

All the green technologies that we've developed are to supplement existing oil and coal energy sources, both of which are also increasing: https://ourworldindata.org/co2-and-other-greenhouse-gas-emissions

He then goes on to cite 13 graphs produced by the highly respected Oxford University's "Our World in Data"... conveniently forgetting that precisely that organisation also analysed data on climate change.

https://ourworldindata.org/co2-and-other-greenhouse-gas-emissions (If you scroll down on this one, you will find a graph identical to your glacial graph, with a huge CO2 spike on the right side - climate always lags behind but it's been a 1°C rise so far already. Much faster than previous temperature changes.)

You might make the case that government scientists are all unanimously forging identical data across the globe to protect their jobs and obtain funding, so let's look at the worldwide renowned data analysis of the independent Oxford Martin School of Oxford University (they receive funding for their science and data analysis, regardless of measured outcome) and see what their climate science analysis reveals. Well, it doesn't look too good.

I doubt the WHO is short-sighted and heavily politically leaned, but for the sake of argument, I'll take your word for it. I cannot think of an internationally more respected source on global development data analysis than "Our World in Data" compiled by the Oxford Martin School of Oxford University. They confirm global warming quite effectively. A recent analysis published in the renowned journal Science, also highlights that our 50-year-old climate models were accurate. Unfortunately, no one can predict the stock price of tomorrow beyond 50% accuracy, or else they'd be a billionaire.

This study about the European grid quantifies all this. If you want to go deeper into how they get these numbers, you'll need to read about PyPSA or other grid modelling tools. It's all open source and well documented.

You need to know how energy models work, and I invite you to read about them. Check out PyPSA for instance (paper, Source code)). That would answer both points above and more. For instance, you need to know that they are based on real weather and consumption data, so they account for natural variability. They know the cost of producing synfuels, and they optimize around it to find the cheapest system overall given the input technologies and assumptions. These calculations would be impossible to do by hand.