The American nuclear industry illustrates negative learning: the costs of plants have increased over time.
But this is not nuclear's fault. Almost everywhere else, the learning rate is positive: costs decline as the industry gains experience building!
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Consider France:
The U.S. has really only been experiencing cost overruns since the Three Mile Island incident, and the reason has to do with the industry becoming overregulated as a result of the public outcry that ensued.
In general, nuclear cost overruns are driven by indirect costs, like having to hire more safety professionals due to added regulatory burdens.
Those explain 72% of the price hike in the U.S., 1976-87:
In a more recent OECD report on nuclear from 2020, it was noted that "indirect cost[s] are the main driver of these cost overruns" and 80% of those indirect costs are attributable to largely unnecessary labor.
The regulatory costs levied against nuclear are so extreme that they can make components cost 50 times what they should, like in the case of 75 mm stainless steel gate valves.
The main factor differentiating nuclear and industrial grade? Unnecessary quality certification.
The question is less "Why is nuclear expensive?" and more "Why is nuclear overregulated?"
And the reason isn't clear-cut. It's obvious it's not so simple as saying "ALARA!", since many countries manage positive learning despite sticking to the same philosophy.
It's more likely a combination of factors involving activism
Thanks to activism, the U.S. nuclear fleet won't achieve French emission levels because, under the Carter administration, activists managed to get reprocessing banned, tarring nuclear's reputation via the 'waste' issue
In any case, nuclear remains a viable option for cleanly powering the future, and continued research into it is necessary for taking us into the stars.
Moreover, for consumers, it remains beneficial ($!) so long as intermittent forms of generation are, well, intermittent.
There's more that can be said, but I'll cut it off there
Sources:
To read way more on this, check out this IFP piece:
A lot of the major pieces of civil rights legislation were passed by White elites who were upset at the violence generated by the Great Migration and the riots.
Because of his association with this violence, most people at the time came to dislike MLK.
It's only *after* his death, and with his public beatification that he's come to enjoy a good reputation.
This comic from 1967 is a much better summation of how the public viewed him than what people are generally taught today.
And yes, he was viewed better by Blacks than by Whites.
But remember, at the time, Whites were almost nine-tenths of the population.
Near his death, Whites were maybe one-quarter favorable to MLK, and most of that favorability was weak.
The researcher who put together these numbers was investigated and almost charged with a crime for bringing these numbers to light when she hadn't received permission.
Greater Male Variability rarely makes for an adequate explanation of sex differences in performance.
One exception may be the number of papers published by academics.
If you remove the top 7.5% of men, there's no longer a gap!
The disciplines covered here were ones with relatively equal sex ratios: Education, Nursing & Caring Science, Psychology, Public Health, Sociology, and Social Work.
Because these are stats on professors, this means that if there's greater male variability, it's mostly right-tail
Despite this, the very highest-performing women actually outperformed the very highest-performing men on average, albeit slightly.
The percentiles in this image are for the combined group, so these findings coexist for composition reasons.
If men do more of the housework and child care, fertility rates will rise!
Men have been doing increasingly large shares of the housework and child care.
Fertility is lower than ever.
In fact, they're doing more in each generation, but fertility has continued to fall.
The original claim, that men's household work would buoy fertility, was based on cross-sectional data that was inappropriately given a causal interpretation.
The updated cross-sectional data is as useful, and it affords no assurances about the original idea.