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Comment by Avamander | Hacker News Reader

It's because they have bad experiences with nuclear and general public doesn't like it either. I suck at German so I can't find the article, but there have been a few cases where the construction of a few reactors has been sub-optimal and thus somewhat dangerous (and too hard to fix). Also some cases of statistically significantly higher chances of leukemia in children near one nuclear plant. But don't quote me on this, GTranslate might have left some critical details about those articles untranslated.

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They have a pretty bad experience with coal, too. Several thousand Germans die each year due to pollution from coal-fired power plants. Yet there's no urgent push to immediately shut down every.

Because that's invisible.

Still, n * 1000 deaths (in n years) may rationally be preferable to m deaths once, even if n * 1000 >> m

Risk assessment is a funny thing. Economists and psychologists have done great experiments along these lines.

I can see it if m is, say, 90% of the entire population. But when m is measured in thousands, how is the vastly larger death toll ever rationally preferable just because it's spread out a bit?

It's not even spread out that much. Worldwide, coal kills more people in two months than nuclear ever has, and that's including nuclear bombs.

> Several thousand Germans die each year due to pollution from coal-fired power plants.

Do you have a source for this?

Sure, here: https://www.euractiv.com/section/health-consumers/news/repor...

Quick summary of figures: in 2013, Germany suffered 1,860 deaths due to coal plants in Germany, 1,770 deaths due to coal plants in other countries, and German coal plants also caused 2,490 deaths in other countries.

There are probably too many significant figures on those numbers, but the general idea of "several thousand" should be more or less right.

I'd be interested to learn more if there are any statistically significant results showing clustering - proving that there is spatiotemporal clustering that is different enough from what might be expected at random is pretty hard. I know that Sellafield has been studied in detail many times, with no definitive evidence yet found.

At least those examples I heard about were easily explained by chance. It was the usual "humans are bad at recognizing randomness".

There are also lots of confounding variables to account for - for example, power plants (of all kinds) commonly have higher amounts of road freight than would otherwise be the case, thus greater air pollution etc. Inhabitants of areas around power plants move in and out of the area. Some people that live in the area work at the plant, whereas others do not. Prevailing weather and topography may affect how any emissions from a plant disperse. Spatial stats is hard!

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