Germany has been thrown around a lot as an example of both what to do and what not to do in terms of addressing global warming by embracing green energy technology. It’s possible to look back now and review the numbers, to see what the effect was of its decision to embrace renewable technology and actively shut down their nuclear power plants. The numbers, I think, tell a pretty clear story.

First, some history. Germany has long had an environmentalist anti-nuclear bent, going back to the 1980s. In 2000 the coalition Green Party and Democratic decided to phase out nuclear power in Germany by 2022, making it an even higher priority than phasing out fossil fuels. This policy was reversed by the Christian Democratic Union, extending the timeline until 2034. After the Fukushima accident, however, public opinion shifted and the 2022 timeline was reinstated. This was delayed by a year because of the Russian invasion of Ukraine, but now the last German nuclear power plants have been shuttered. At the same time, Germany invested heavily in wind and solar.

What did this mean for Germany’s energy production and carbon footprint? Did it makes sense to phase out nuclear while building lots of wind and solar? I would argue an emphatic, no. It demonstrates, quite nicely, (I know, I need to be careful of confirmation bias, but hear me out) what I have been saying for years. For now the choice is not really between nuclear and renewables, but nuclear and fossil fuel. A Washington Post article summarizes the relevant numbers. In 2010 Germany’s energy mix was 60% fossil fuel, 23% nuclear, and 17% renewables. In 2022 (before the final shutdowns) it was 51%, 6%, 43% respectively. If in 2000 Germany had decided to prioritize shutting down coal-fired plants and other fossil fuel sources and just kept their existing nuclear power plants open for as long as possible, their mix today would be 32% fossil fuel, 24% nuclear, and 43% renewable.

Either way, they would have 43% renewable. They were building it as fast as they could. The only difference is that today (now that the last nuclear plants have closed) we have something like 57% fossil fuel instead of 32%. It really was the choice between nuclear and fossil fuel. As a result of this policy Germany, despite investing heavily in renewables, has one of the dirtiest energy mixes in Europe, only behind Poland and the Czech Republic. Germany produces 385 gCO2 / kWh. Heavily nuclear France, by comparison, produces 85. This will also delay Germany’s ability to phase out coal, and it will be one of the last European countries to do so.

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Part 4

This will be the final installment of this mini-debate about climate change and health effects, following a typical format of each person getting to make a statement and a response. Scott makes a lot of complaints about tone, format and fairness while simultaneously trying to shield himself from any similar criticism. I am going to ignore this aspect of his response, and also anything dealing with the media. Rather I am going to focus on logic and evidence.

First I have to point out that Scott did not refute in any way my primary criticism of his logic – the notion that if climate change is having any negative effects it should be seen in the raw data. Rather, when one factor is part of a complex set of factors we can use statistical analysis to tease out its effect, even if the net effect of all factors are not in the same direction. This is the fatal flaw in his premise and methods, and he just dismissed my criticism and doubled down on this strategy. Saying he is quoting the IPCC is not a defense, when he is simply misinterpreting what they said. They are talking about “contributions” from climate change. He doesn’t want to talk about logic, and wants to force us to accept this incorrect framing. Nope – the framing is wrong, and therefore all of his arguments are not valid.

But let’s also delve into the specific claims. The first meaty claim is that climate models are running hot (a now long-debunked climate denying trope). Climate models are used for various things, including estimating climate sensitivity (the amount of warming that would result from a doubling of pre-industrial atmospheric CO2 levels). There are other ways to estimate climate sensitivity, including using historical data. Estimates have become increasingly precise, and now the consensus of evidence is that sensitivity is between 1.5 and 4.5C. The “hot model problem” that Scott is referring to is that the latest evaluation of climate models from CMIP6 shows that 10 of the 55 models looked at estimated sensitivity at >4.5 C (up to 5.6 C), i.e. “running hot”. The other 45 models are fine, and agree with historical data.

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Part 3

Hi Steve, and thank you for your timely response, and for even considering hosting this debate. There has been, and continues to be a “blackout” on almost all discussion regarding the science behind climate change. If “The science” is truly “settled”, it is a pretty shaky settlement! Why the blackout then?

I appreciate that you have pointed out some of my quirks that I use in constructing and argument. After all, this is a debate, and of course we come at it with different viewpoints and narratives, which makes debates all the more profoundly important to have. I think we would all agree that listening to other viewpoints other than our own, although may be uncomfortable, nonetheless helps us become more intelligent, enlightened, and wise.

While I do appreciate the pointing out of my biases (which we all have), there was very little substantive rebutting of the information I provided. I did use exact verbiage from the latest IPCC report, so nobody could accuse me of making a strawman argument. If you think I’m setting up a bunch of strawman arguments, I invite you to reach out to the authors of IPCC chapter 11: Human Health: Impacts, Adaptation, and Co-benefits and ask them why they are setting up these supposed confounded strawman arguments for me. If I can’t quote IPCC, the most trusted source of climate change information, then who can anyone trust regarding climate change information? The words I used to base my argument upon are their words, not mine. 

You state: “So Scott’s method is fatally flawed – you cannot just look at raw incidence numbers and declare there is no effect from climate change…But Scott dismisses all of this as “modeling”, which he rejects out of hand, explicitly favoring raw data, which is profoundly naive. He also dismissed data on the risks increased by climate change, again favoring raw data that mixes in many confounding variables.”

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Yesterday’s post was the first in an exchange about the effects of climate change on public health. Today’s post is my response.

Part II

Climate change is a critically important topic for society today, and it’s important that the public have a working knowledge of the facts, causes, effects, and potential interventions regarding climate change, so I am always happy to discuss the topic. Unfortunately, it’s a very complex topic that has been highly politicized and polarized. The science often becomes wrapped up in ideology – the best indication of this is that one’s political affiliation is the strongest predictor of the public’s opinions regarding climate change. The media, as they do in general, is happy to sensationalize the topic and often does not provide good context or background. Scientists have gotten better communicating about climate change, but not enough to override political affiliation.

My sense is the core issue is that the complexity of climate change allows everyone to cherry pick those details that fit their narrative. You can find examples to support whatever narrative you want to believe. You don’t even have to be factually incorrect (although many people certainly are), you just have to be selective in your details and interpretation. Climate change is a Rorschach test of subjective validation and confirmation bias.

I say this all because I think Scott’s narrative comes through very clearly. He contacted me asking fervently for a debate on this specific topic, the health effects of climate change. I thought this was a little odd since I have never written or expressed an opinion about this topic before. It seems he assumed what my position was based on other things I have written about climate change – that I think it’s real, it is primarily being caused by humans, and the effects are likely to be bad for the environment and human civilization. This brings up another aspect of the climate change debate, that people generally take sides and think that everyone fits relatively cleanly into the “for or against” side. Once someone thinks they have detected what side you are on, they then ascribe the entire package of views to you.

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Grand conspiracy theories are a curious thing. What would lead someone to readily believe that the world is secretly run by evil supervillains? Belief in conspiracies correlates with feelings of helplessness, which suggests that some people would rather believe that an evil villain is secretly in control than the more prosaic reality that no one is in control and we live in a complex and chaotic universe.

The COVID-19 pandemic provided a natural experiment to see how conspiracy belief reacted and spread. A recent study examines this phenomenon by tracking tweets and other social media posts relating to COVID conspiracy theories. Their primary method was to identify specific content type within the tweets and correlate that with how quickly and how far those tweets were shared across the network.

The researchers identified nine features of COVID-related conspiracy tweets: malicious purposes, secretive action, statement of belief, attempt at authentication (such as linking to a source), directive (asking the reader to take action), rhetorical question, who are the conspirators, methods of secrecy, and conditions under which the conspiracy theory is proposed (I got COVID from that 5G tower near my home). They also propose a breakdown of different types of conspiracies: conspiracy sub-narrative, issue specific, villain-based, and mega-conspiracy.

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After publishing thousands of blog posts I have found that sometimes the most trivial topics garner the most debate, both in amount and intensity. I wouldn’t call it a rule, just a casual observation, likely rife with confirmation bias. But at the very least I am surprised sometimes by how vehemently people will argue about points that are ultimately subjective and of little importance.

Grammatical and semantic arguments tend to fall into that category. My speculation is that this is partly due to the fact that people’s brains literally get wired from exposure to language so that words and phrases just sound right or sound wrong. When someone else says or writes something that just feels wrong it can be extremely irritating. This feeling, in turn, leads us to act as if certain ways of saying things are inherently correct or incorrect, or at least “proper”. As might be expected, the internet is fertile ground for people to vent their grammatical peeves. This has also lead to a backlash against the “grammar nazis”, often in the context that whatever their linguistic peeve, it is not actually “correct” by any objective criteria.

I find this topic fascinating, primarily because it challenges the logic and reasoning we use to determine which linguistic forms are “proper and correct”. Language is endlessly complex and fascinating. It is an organic evolving thing, and we have a history of how it has changed over thousands of years.

The latest example I encountered of the “grammar wars” is the question of when it is proper to use “less” vs “fewer”. It came up because on the latest SGU episode Jay said “less hours of sleep” and I corrected him to say “fewer hours of sleep”. I did this mainly for the entertainment value, but I think the point is valid. My correction sparked some e-mail backlash, often pointing to references arguing that “less” is just fine in such usage. However I found some of the arguments used to be unsatisfying, and even hypocritical. Here is a quick overview of the discussion.

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The mammalian brain is an amazing information processor. Millions of years of evolutionary tinkering has produced network structures that are fast, efficient, and capable of extreme complexity. Neuroscientists are trying to understand that structure as much as possible, which is understandably complicated. But progress is steady.

A recent study illustrates how complex this research can get. The researchers were looking at the geometry of neuron activation in the part of the brain that remembers spatial information – the CA1 region of the hippocampus. This is the part of the brain that has place neurons – those that are activated by being in a specific location. They wanted to know how networks of overlapping place neurons grow as rats explore their environment. What they found was not surprising given prior research, but is extremely interesting.

Psychologically we tend to have a linear bias in how we think of information. This extends to distances as well. It seems that we don’t deal easily (at least not intuitively) with geometric or logarithmic scales. But often information is geometric. When it comes to the brain, information and physical space are related because neural information is stored in the physical connection of neurons to each other. This allows neuroscientists to look at how brain networks “map” physically to their function.

In the present study the neuroscientists looked at the activity in place neurons as rats explored their environment. They found that rats had to spend a minimum amount of time in a location before a place neuron would become “assigned” to that location (become activated by that location). As rats spent more time in a location, gathering more information, the number of place neurons increased. However, this increase was not linear, it was hyperbolic. Hyperbolic refers to negatively curved space, like an hourglass with the starting point at the center.

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There are now approximately 8 billion people on the planet. In addition, there are over 7,100 languages spoken on Earth. One question for anthropologists and linguistic experts is – how closely do genetic relationships match language relationships. Both language and genes are generally inherited from our parents – well, genes absolutely, but language generally. It makes sense that a map of genetic relatedness would closely follow a map of linguistic relatedness. If we zoom out from a single family to a population, the question becomes a bit more complex. Populations can mix genes with other populations. Two populations that derived relatively recently from a common population will likely be genetically similar, and even if their current languages differ, they too likely share a common root and therefore lots of similarities.

What happens, then, when scientists overlay the genetic and linguistic maps of humanity? A recent study does just that. To do this they compiled a massive database, called GeLaTo, or Genes and Languages Together. GeLaTo includes data from “4,000 individuals speaking 295 languages and representing 397 genetic populations.” That is fairly robust, but there is also lots of room for continuing to add information to the database to add more precision and detail to any analysis.

What they found is that the match between genes and language is very good, about 80%. However, that still leaves 20% of identified genetic populations with a language mismatch. How does this happen? It doesn’t take much imagination to think of a scenario where a population takes on the language of another population in their region that is genetically distinct. For example:

Some peoples on the tropical eastern slopes of the Andes speak a Quechua idiom that is typically spoken by groups with a different genetic profile who live at higher altitudes. The Damara people in Namibia, who are genetically related to the Bantu, communicate using a Khoe language that is spoken by genetically distant groups in the same area. And some hunter-gatherers who live in Central Africa speak predominantly Bantu languages without a strong genetic relatedness to the neighboring Bantu populations.

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Archaeologists have uncovered a large cache of over 50 small bronze statues in the ruins of an ancient temple in Tuscany. The find dates from the second century BCE to the first century ACE. It is being reported as the greatest bronze statue find in 50 years, one of the greatest finds ever, and a significant window into that period of history.

The statues themselves range from small representations of specific body parts, to statues representing the gods and up to a meter in length. These statues were deliberately tossed into a thermal spring within the temple, where they sunk to the bottom and were covered in mud. The mud preserved the statues in relatively good condition for the last two thousand years. Many of the statues also have writing on them, in either Roman or Etruscan. Archaeologists believe that these statues were offerings to the gods intended for healings. The body parts represent the ailment that the offerer wishes to be healed. They also found over 5,000 gold, silver, and bronze coins that were tossed into the spring over those three centuries.

Essentially, this thermal spring and temple were the equivalent of a spa for the wealthy. Bathing in hot springs was a common luxury for the wealthy of the time, and this temple was also clearly not a public place. Rather, this was likely a private location for the wealthy and elite. The bronze statues would have been very expensive, only affordable just to be tossed into the waters by the very wealthy.

It’s easy to become smug from our modern perspective about the primitive behavior of making offerings to imaginary gods in hopes of being healed. But I think the opposite reaction is more appropriate. Certainly making such offerings in the genuine hope of being healed is pure superstition, and also completely useless in terms of effecting real change to one’s health. Given the primitive state of medicine at the time, however, it was also pretty harmless (and an archaeological boon, it turns out). Even the wealthy and powerful did not have access to what we could consider basic health care, and so tossing expensive bronze statues was the best they had.

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A recent study asked subjects to give their overall impression of other people based entirely on a photograph of their face. In one group the political ideology of the person in the photograph was disclosed (and was sometimes true and sometime not true), and in another group the political ideology was not disclosed. The question the researchers were asking is whether thinking you know the political ideology of someone in a photo affects your subjective impression of them. Unsurprisingly, it did. Photos that were labeled with the same political ideology (conservative vs liberal) were rated more likable, and this effect was stronger for subjects who have a higher sense of threat from those of the other political ideology.

This question is part of a broader question about the relationship between facial characteristics and personality and our perception of them. We all experience first impressions – we meet someone new and form an overall impression of them. Are they nice, mean, threatening? But if you get to actually know the person you may find that your initial impression had no bearing on reality. The underlying question is interesting. Are there actual facial differences that correlate with any aspect of personality? First, what’s the plausibility of this notion and possible causes, if any?

The most straightforward assumption is that there is a genetic predisposition for some basic behavior, like aggression, and that these same genes (or very nearby genes that are likely to sort together) also determine facial development. This notion is based on a certain amount of biological determinism, which itself is not a popular idea among biologists. The idea is not impossible. There are genetic syndromes that include both personality types and facial features, but these are extreme outliers. For most people the signal to noise ratio is likely too small to be significant.  The research bears this out – attempts at linking facial features with personality or criminality have largely failed, despite their popularity in the late 19th and early 20th centuries.

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