Category: Science
Avian Flu is Bad for Cows
FarmProgress: With a closed herd and all his heifers artificially inseminated — no outside bulls needed — Nathan Brearley was confident his 500-cow dairy farm in Portland, Mich., would be spared from the avian flu strain that’s affecting dairies.
He was wrong. Nearly six months later after an infection on his farm, milk production still hasn’t recovered.
“I was quite surprised. I never saw any other disease this widespread affect the cattle like it did,” Brearley said during a recent webinar on dairy avian flu, put on by the Pennsylvania Center for Dairy Excellence.
…Brearley said the first signs of problems were in April when the SmaxTec boluses in his cows, which keep track of temperature and other health parameters, started sending high-temperature alarms to his phone and computer. Half the herd looked like it was getting sick.
“Looking at data, the average temperature rise was 5.1 degrees above normal,” he said. “Outlying cows were even higher with temperature.”
The cows were lethargic and didn’t move. Water consumption dropped from 40 gallons to 5 gallons a day. He gave his cows aspirin twice a day, increased the amount of water they were getting and gave injections of vitamins for three days.
Five percent of the herd had to be culled.
“They didn’t want to get up, they didn’t want to drink, and they got very dehydrated,” Brearley said, adding that his crew worked around the clock to treat nearly 300 cows twice a day. “There is no time to think about testing when it hits. You have to treat it. You have sick cows, and that’s our job is to take care of them.”
Testing eventually revealed that his cows did indeed contract H5N1. But how they contracted it, he said, is still a mystery.
Brearley said an egg-laying facility a mile and a half away tested positive for H5N1 and had to depopulate millions of birds. The birds were composted in windrows outside the facility, “and I could smell that process.”
The farm averaged 95-100 pounds of milk per head with 4.0% butterfat and strong solids before the outbreak. During the first three weeks of infection, milk production fell to 75 pounds a head and has been slow to recover.
“Honestly, we haven’t recovered since, though my forages have been stable,” Brearley said. “I cannot get back to our baseline again.”
Reproduction was also challenged. Right off the bat, his cows aborted their calves.
And how about this kicker:
He didn’t test his cows until two weeks after the first high temperatures entered his herd, fearing that his milk processor wouldn’t accept his farm’s milk.
Why do I get the feeling that we are sleepwalking?
How has human DNA evolved?
The full title of this paper is “Pervasive findings of directional selection realize the promise of ancient DNA to elucidate human adaptation.” It truly has an all-star cast of authors, including David Reich and Eric S. Lander, and also numerous others at top schools. I did read through this paper, but understood it only in part. In any case, here is the abstract:
We present a method for detecting evidence of natural selection in ancient DNA time-series data that leverages an opportunity not utilized in previous scans: testing for a consistent trend in allele frequency change over time. By applying this to 8433 West Eurasians who lived over the past 14000 years and 6510 contemporary people, we find an order of magnitude more genome-wide significant signals than previous studies: 347 independent loci with >99% probability of selection. Previous work showed that classic hard sweeps driving advantageous mutations to fixation have been rare over the broad span of human evolution, but in the last ten millennia, many hundreds of alleles have been affected by strong directional selection. Discoveries include an increase from ∼0% to ∼20% in 4000 years for the major risk factor for celiac disease at HLA-DQB1; a rise from ∼0% to ∼8% in 6000 years of blood type B; and fluctuating selection at the TYK2 tuberculosis risk allele rising from ∼2% to ∼9% from ∼5500 to ∼3000 years ago before dropping to ∼3%. We identify instances of coordinated selection on alleles affecting the same trait, with the polygenic score today predictive of body fat percentage decreasing by around a standard deviation over ten millennia, consistent with the “Thrifty Gene” hypothesis that a genetic predisposition to store energy during food scarcity became disadvantageous after farming. We also identify selection for combinations of alleles that are today associated with lighter skin color, lower risk for schizophrenia and bipolar disease, slower health decline, and increased measures related to cognitive performance (scores on intelligence tests, household income, and years of schooling). These traits are measured in modern industrialized societies, so what phenotypes were adaptive in the past is unclear. We estimate selection coefficients at 9.9 million variants, enabling study of how Darwinian forces couple to allelic effects and shape the genetic architecture of complex traits.
I can report that nothing in their exposition seemed unreasonable or unsupported to me. But also the paper didn’t much change my worldview? There is the usual Twitter speculation about how this might apply to different groups, but note the data aggregation methods of the paper in fact require that various human groups (Europe only in the dataset) evolved in tandem and in similar ways over time. Without that assumption, the entire piece of work collapses.
Exciting economics is often misguided economics
In my latest Bloomberg column, I weigh in on the issues surrounding the latest David Deming piece in The Atlantic. Here is one excerpt:
…economics is a relatively mature science, and even surprising results are typically consistent with the laws of supply and demand. Innovations tend to be subtle — they could also be described, less generously, as underwhelming — concerning the relative size of effects. So it is hard for radical new ideas to come out of nowhere, and that does lead to some geographic concentration, centered in the highest-reputation schools…
Can economics come up with truly novel remedies or ideas? Probably not. If there is a recession, or say hyperinflation, there is a standard kit of tools involving monetary policy, fiscal policy, deregulation and some other policy changes. Economists can and do argue about the right mix of those policies in a particular case. But there is no “new drug” waiting to be discovered.
And:
As for microeconomics, if there is too much traffic on a highway, congestion pricing usually works. If there isn’t enough housing, deregulating construction or eliminating rent control are worth a try. No brilliant outsider will come along and say, “The way to get more housing is for everyone to drink two shots of vodka,” or some other novel or wild idea.
The point is not that economists have all the answers. It’s that we have a pretty exhaustive list of possible remedies.
And in sum:
The good news is that economists have already achieved a lot. The bad news is that a lot of the remaining work is doomed to be pretty boring and marginal. So one lesson is simply to appreciate the dullness of economics, because exciting economics is often misguided economics.
There is further content at the link.
What Fusion Energy Can Learn From Biotechnology
Fusion energy is currently facing many of the same opportunities and challenges as the biotechnology industry of the 1970s: exciting scientific and engineering breakthroughs that could change the course of human history, with sufficient public and private funding, more effective business models, and appropriate regulatory oversight. A number of lessons can be learned from the last 50 years of biotechnology industry history, which lead to five proposed initiatives for accelerating progress in fusion: the creation of a university intellectual-property consortium; the standardization of fusion energy milestones along with fusion rating agencies to certify their achievement; the development of new financing and business models to fund the various stages of fusion progress; a coordinated plan for two-sided outreach, education, and engagement at all levels from K–12 to policymakers and the general public; and managing fusion initiatives as part of a broader ecosystem. Applying these historical lessons today can accelerate the development of fusion towards the same level of commercial success and human impact that biotech has achieved.
That is from a new paper by Andrew W. Lo and Dennis Whyte.
AI and Biology
I think AI is going to have some if its biggest effects on biology. Biological pathways are among the most complex in all of science. People are good at handling two or maybe three variable problems but just keeping three variables and their interactions in one’s head is difficult. AIs with access to vast databases of genes, proteins, networks and so forth will enable new simulations and learning as has already happened with protein folding.
LLMs are Creative Reasoners
It’s bizarre to me that there are still people claiming that LLMs are not reasoning or are not creative when by any objective measure they are obviously creative reasoners! By objective measure I mean a test that evaluates creativity and reasoning by evaluating outputs not by idle philosophical speculation that rules AIs out by definition. Here’s a good paper, Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers, which illustrates one such test. The authors asked top researchers in the field of natural language processing to propose research ideas which were then presented in a standardized format to a ratings panel of other NLP experts. The AI created ideas were judged more creative than the human ideas.
Now one might argue that the humans weren’t giving their best ideas–some data in the paper suggests they were giving ideas at the median of those for top researchers–and humans might also be looking for ideas that were perhaps easier to get funding precisely because they were less creative but more doable. Either way, however, the AIs are coming up with good ideas that could usefully supplement human generated ideas.
My excellent Conversation with Philip Ball
Here is the audio, video, and transcript. Here is part of the episode summary:
Tyler and Philip discuss how well scientists have stood up to power historically, the problematic pressures scientists feel within academia today, artificial wombs and the fertility crisis, the price of invisibility, the terrifying nature of outer space and Gothic cathedrals, the role Christianity played in the Scientific Revolution, what current myths may stick around forever, whether cells can be thought of as doing computation, the limitations of The Selfish Gene, whether the free energy principle can be usefully applied, the problem of microplastics gathering in testicles and other places, progress in science, his favorite science fiction, how to follow in his footsteps, and more.
Here is one excerpt, namely the opening bit:
TYLER COWEN: Hello, everyone, and welcome back to Conversations with Tyler. Today I’ll be chatting with Philip Ball. I think of Philip this way. We’ve had over 200 guests on Conversations with Tyler, and I think three of them, so far, have shown they are able to answer any question I might plausibly throw their way. Philip, I believe, is number four. He’s a scientist with degrees in chemistry and physics. He’s written about 30 books on different sciences. Both he and I have lost count.
He was an editor at Nature for about 20 years. His books cover such diverse topics as chemistry, physics, the history of experiments, social science, color, the elements, water, water in China, Chartres Cathedral, music, and more. But most notably, he has a new book out this year, a major work called How Life Works: A User’s Guide to the New Biology. Philip, welcome.
PHILIP BALL: Thank you, Tyler. Lovely to be here.
COWEN: What is the situation in history where scientists have most effectively stood up to power, not counting Jewish scientists, say, leaving Nazi Germany or the Soviet Union?
BALL: Gosh, now there’s a question to start with. Where they have most effectively stood up to power — this is a question that I looked at in a book (it must be about 10 years old now) which looked at the response of German physicists during the Nazi era to that regime. I’m afraid my conclusion was, the response was really not very impressive at all.
On the whole, the scientists acquiesced to what the regime wanted them to do. Very few of them were actively sympathetic to the Nazi party, but they mounted no real effective opposition whatsoever. I’m afraid that looking at that as a case study, really, made me realize that it’s actually very hard to find any time in history where scientists have actively mounted an effective opposition to that kind of imposition of some kind of ideology, or political power, or whatever. History doesn’t give us a very encouraging view of that.
That said, I think it’s fair to say, science is doing better these days. I think there’s a recognition that at an institutional level, science needs to be able to mobilize its resources when it’s threatened in this way. I think we’re starting to see that, certainly, with climate change. Scientists have come under fire a huge amount in that arena. I think there’s more institutional understanding of what to do about that. Scientists aren’t being so much left to their own devices to cope as best they can individually.
But I think that there’s this attitude that is still somewhat prevalent within science, that’s a bit like, “We’re above that.” This is exactly what some of the German physicists, particularly Werner Heisenberg, said during the Nazi regime, that science is somehow operating in a purer sphere, and that it’s removed from all the nastiness and the dirtiness that goes on in the political arena.
I think that that attitude hasn’t gone completely, but I think it needs to go. I think scientists need to get real, really, about the fact that they are working within a social and political context that they have to be able to work with, and to be able to — when the occasion demands it — take some control of, and not simply be pushed around by.
That, I think, is something that can only happen when there are institutional structures to allow it to happen, so that scientists are not left to their own individual devices and their own individual sense of morality to do something about it. I’m hoping that science will do better in the future than it’s done in the past.
COWEN: Which do you think are the power structures today that current scientists, say in the Anglo world, are most in thrall to?
Recommended, there are numerous topics of interest. I also asked GPT how much money it could earn if it had the powers of Wells’s Invisible Man.
From Reed and Logchies
Introduction. Your analysis produces a statistically insignificant estimate. Is it because the effect is negligibly different from zero? Or because your research design does not have sufficient power to achieve statistical significance? Alternatively, you read that “The median statistical power [in empirical economics] is 18%, or less” (Ioannidis et al., 2017) and you wonder if the article you are reading also has low statistical power. By the end of this blog, you will be able to easily answer both questions. Without doing any programming.
An Online App. In this post, we show how to calculate statistical power post-estimation for those who are not familiar with R. To do that, we have created a Shiny App that does all the necessarily calculating for the researcher (CLICK HERE).
Here is the link and the full story.
Obama’s space legacy?
Bucking his central planning instincts, Obama embraced a surprisingly laissez-faire approach to space flight that angered political allies and opponents alike.
In doing so, however, he tapped a reservoir of ingenuity and innovation that has ushered in a new age of space flight and exploration…
In her forthcoming book Bureaucrats and Billionaires, former NASA deputy administrator Lori Garver and reporter Michael Sheetz trace the origins of NASA’s commercial crew program, a revolutionary human spaceflight program that joins private aerospace manufacturers such SpaceX and Boeing with NASA’s astronauts.
Garver writes that this hybrid allows space flight “at a fraction of the cost of previous government owned and operated systems.” A decade ago, however, the program faced opposition seemingly from every side.
The saga began early in 2010 when President Obama announced his intention to abort NASA’s Constellation program—NASA’s crew spaceflight program—correctly pointing out it was “over budget, behind schedule, and lacking in innovation.”
The decision angered almost everyone. As Garver and Sheetz write, the program was “extremely popular with Congress, and the contractors who were benefiting from the tax dollars coming their way.” An impressive array of stakeholders from aerospace companies, trade associations, and astronauts to lobbyists, Congressional delegations, and NASA pushed back.
The resistance was immense.
NASA chief Charles Bolden, while choking back tears, compared the decision to “a death in the family.” Pulitzer Prize winning columnist Charles Krauthammer ominously noted the move would give the Russians “a monopoly on rides into space.” Congressman Pete Olson (R-Texas) called the decision “a crippling blow to America’s human spaceflight program.”
Few commentators seemed to even notice the $6 billion in spending over five years to support commercially built spacecraft to launch NASA’s astronauts into outer space…
By pulling the plug on Constellation, Obama had unleashed the power of markets and competition. While many associate competition with dog-eat-dog and survival of the fittest tropes, competition is a healthy and productive force.
Here is the full story, by John Miltimore at FEE (!). Via Matt Yglesias.
Does increasing division of labor lead to greater credentialism?
That is the theme of my latest Bloomberg column, here is one excerpt:
Consider business. For decades now, big businesses have been on the rise in the US, which means employment in large corporations that use a team approach is increasingly likely. One effect of this is that individual outputs are harder to measure. If a product does well, it is often not clear who should get the credit, because the inputs of so many people were involved in creating it.
It is difficult to recalibrate incentives to reflect this changing reality. Often companies respond by enforcing greater credentialism, trying to ensure that everyone is a worthwhile contributor. That could involve looking for an Ivy League education or a standout GitHub profile. Either way, companies are more likely to look for ex ante signals of quality and less likely to take chances on true outsiders, because if the outsider isn’t pulling their weight, it might not be evident for a long time.
And this:
The real losers in the team system are those who do not have the temperament for all the schooling and credential-gathering. Those credentials of course include recommendations from well-known contacts, so networking and socializing have become increasingly important. This is a workable situation for most people but a frustrating arrangement for others.
Some recent evidence indicates this problem is especially serious in the world of science. The number of authors on scientific papers has been rising sharply, a trend I have observed in my own field of economics. It was once rare for the research paper of a fresh job-market candidate to be co-authored; now it is common. The work may be wonderful, but how can you tell how much any one author contributed? In the natural and biological sciences, one paper can have dozens of co-authors.
Again, credentialism will become more important, not less. In relative terms, someone from MIT listed on a multiple-authored paper is more attractive than someone from Iowa State University.
The latter part of the piece also explains why we underinvest in databases, and in turn in LLMs. It is difficult to reward people, under current structures, for contributing to such a broad collective enterprise.
Okie-dokie, solve for the equilibrium
One of the grand challenges of artificial general intelligence is developing agents capable of conducting scientific research and discovering new knowledge. While frontier models have already been used as aids to human scientists, e.g. for brainstorming ideas, writing code, or prediction tasks, they still conduct only a small part of the scientific process. This paper presents the first comprehensive framework for fully automatic scientific discovery, enabling frontier large language models to perform research independently and communicate their findings. We introduce The AI Scientist, which generates novel research ideas, writes code, executes experiments, visualizes results, describes its findings by writing a full scientific paper, and then runs a simulated review process for evaluation. In principle, this process can be repeated to iteratively develop ideas in an open-ended fashion, acting like the human scientific community. We demonstrate its versatility by applying it to three distinct subfields of machine learning: diffusion modeling, transformer-based language modeling, and learning dynamics. Each idea is implemented and developed into a full paper at a cost of less than $15 per paper. To evaluate the generated papers, we design and validate an automated reviewer, which we show achieves near-human performance in evaluating paper scores. The AI Scientist can produce papers that exceed the acceptance threshold at a top machine learning conference as judged by our automated reviewer. This approach signifies the beginning of a new era in scientific discovery in machine learning: bringing the transformative benefits of AI agents to the entire research process of AI itself, and taking us closer to a world where endless affordable creativity and innovation can be unleashed on the world’s most challenging problems. Our code is open-sourced at this https URL
That is from a new paper by Chris Lu, Cong Lu, Robert Tjarko Lange, Jakob Foerster, Jeff Clune, David Ha. Note this is related to some earlier work in economics by Benjamin Manning of MIT (with co-authors).
I’ve said it before, and I’ll say it again. The marginal product of LLMs is when they are interacting with well-prepared, intricately cooperating humans at their peak, not when you pose them random queries for fun.
Beware research in large teams
Teamwork has become more important in recent decades. We show that larger teams generate an unintended side effect: individuals who finish their PhD when the average team in their field is larger have worse career prospects. Our analysis combines data on career outcomes from the Survey of Doctorate Recipients with publication data that measures team size from ISI Web of Science. As average team size in a field increased over time, junior academic scientists became less likely to secure research funding or obtain tenure and were more likely to leave academia relative to their older counterparts. The team size effect can fully account for the observed decline in tenure prospects in academic science. The rise in team size was not associated with the end of mandatory retirement. However, the doubling of the NIH budget was associated with a significant increase in team size. Our results demonstrate that academic science has not adjusted its reward structure, which is largely individual, in response to team science. Failing to address these concerns means a significant loss as junior scientists exit after a costly and specialized education in science.
That is from a new NBER working paper by
My excellent Conversation with Paul Bloom
Here is the audio, video, and transcript. Here is part of the episode summary:
Together Paul and Tyler explore whether psychologists understand day-to-day human behavior any better than normal folk, how babies can tell if you’re a jerk, at what age children have the capacity to believe in God, why the trend in religion is toward monotheism, the morality of getting paid to strangle cats, whether disgust should be built into LLMs, the possibilities of AI therapists, the best test for a theory of mind, why people overestimate Paul’s (and Tyler’s) intelligence, why flattery is undersupplied, why we should train flattery and tax empathy, Carl Jung, Big Five personality theory, Principles of Psychology by William James, the social psychology of the Hebrew Bible, his most successful unusual work habit, what he’ll work on next, and more.
And here is one excerpt:
COWEN: I have some questions about intelligence for you. If we think of large language models, should we let them feel disgust so that they avoid left-wing bias?
BLOOM: [laughs] Why would disgust make them avoid left-wing bias?
COWEN: Maybe we’re not sure it would, but there are various claims in the literature that for people on the right, disgust is a more fundamental emotion, and that a greater capacity to feel disgust encourages people in some ways to be more socially conservative. Debatable, but I don’t think it’s a crazy view. So, if you build LLMs, and you give them, say, a lot of empathy and not much or any disgust, you’re going to get left-leaning LLMs, which you might say, “Well, that was my goal.” But obviously, not everyone will accept that conclusion either.
BLOOM: I wouldn’t want woke LLMs. I think there’s a lot in extreme —
COWEN: You’ve got them, of course.
BLOOM: I’ve got them. I think Gemini is the one, if I wanted to go — the woke LLM of choice. Because I think the doctrine called wokeness leads to a lot of moral problems and makes the world worse in certain ways, but I wouldn’t mind left-wing LLMs.
In fact, I’m not a fan of disgust. You’re right that disgust is often associated with right-wing, but in the very worst instantiation of it. Disgust is what drives hatred towards gay people. It involves hatred of interracial marriage, the exclusion of immigrants, the exclusion of other races. If there’s one emotion I would take away from people, it would be disgust, at least disgust in the moral realm. They could keep their disgust towards rotten food and that sort of thing. That’s the one thing I wouldn’t put into LLMs. I’d rather put anger, pity, gratitude. Disgust is the one thing I’d keep away.
COWEN: So, you wouldn’t just cut back on it at the margin. You would just take disgust out of people if you could?
And:
COWEN: I think at the margin, I’ve moved against empathy more being a podcast host, that I’ll ask a question —
BLOOM: Wait. Why being a podcast host?
COWEN: Well, I’ll ask a question, and a lot of guests think it’s high status simply to signal empathy rather than giving a substantive answer. The signaling-empathy answers I find quite uninteresting, and I think a lot of my listeners do, too. Yet people will just keep on doing this, and I get frustrated. Then I think, “Well, Tyler, you should turn a bit more against empathy for this reason.” And I think that’s correct.
Paul cannot be blamed for doing that, however. So substantive, interesting, and entertaining throughout.
Dark oxygen: jubiliant for others, cry for yourself and your kin
To summarize the new results:
An international team of researchers recently discovered that oxygen is being made by potato-shaped metallic nodules deep under the surface of the Pacific Ocean. In July, their findings, which throw into dispute the concepts of oxygen production, were published in the Nature Geoscience jonal. The discovery could lead to a reconsideration of the origins of complex life on Earth.
The findings from a team of researchers led by Professor Andrew Sweetman at the U.K.’s Scottish Association for Marine Science, show that oxygen is being produced at around 4,000 metres below the surface of the ocean in complete darkness. This contradicts previous scientific assumptions that only living organisms, including plants and algae, can use energy to create oxygen through photosynthesis, using sunlight for the reaction.
As Julian Gough suggests, most life probably is on icy moons. This means a lot more life! Over a time slice, it could mean billions of additional lives out there. Did you pop up the champagne?
The bad news is that the chance that Robin Hanson’s “Great Filter” lies behind us is somewhat smaller. Which boosts the chance that it may lie in our near future. Did you pull out the tissues?
On net, did this news change your mood at all? Why or why not?
The Unseen Fallout: Chernobyl’s Deadly Air Pollution Legacy
A fascinating new paper The Political Economic Determinants of Nuclear Power: Evidence from Chernobyl by Makarin, Qian, and Wang was recently presented at the NBER Pol. Economy conference. The paper is nominally about how fossil fuel companies and coal miners in the US and UK used the Chernobyl disaster to successfully lobby against building more nuclear power plants. The data collection here is impressive but that is just how democracy works. I found the political economy section less interesting than some of the background material.
First, the Chernobyl disaster ended nuclear power plant (NPP) construction in the United States (top-left panel), the country with the most NPPs in the world . Surprisingly, the Three Mile Island accident in 1979 (much less serious than Chernobyl) had very little effect on construction; albeit the 1-2 punch with Chernobyl in 1986 surely didn’t help. The same pattern is very clear across all countries and also all democracies (top-right panel). The bottom two panels show the same data but looking at new plants rather than the cumulative total–there was a sharp break in 1986 with growth quickly converging to zero new plants per year.
Fewer nuclear plants than otherwise would have been the case might have made a disaster less likely but there were countervailing forces:
We document that the decline in new NPPs in democracies after Chernobyl was accompanied by an increase in the average age of the NPPs in use. To satisfy the rise in energy demand, reactors built prior to Chernobyl continued operating past their initially scheduled retirement dates. Using data on NPP incident reports, we show that such plants are more likely to have accidents. The data imply that Chernobyl resulted in the continued operation of older and more dangerous NPPs in the democracies.
Moreover, safety declined because the existing plants got older but in addition “the slowdown of new NPP construction…delayed the adoption of new safer plants.” This is a point about innovation that I have often emphasized (see also here)
The key to innovation is continuous refinement and improvement…. Learning by doing requires doing….Thus, when considering innovation today, it’s essential to think about not only the current state of technology but also about the entire trajectory of development. A treatment that’s marginally better today may be much better tomorrow.
Regulation increased costs substantially:
The U.S. NRC requires six-to-seven-years to approve NPPs. The total construction time afterwards ranges from decades to indefinite. Cost overruns and changing regulatory requirements during the construction process sometime forces construction to be abandoned after significant sunk costs have been made. This often leads investors to abandon construction after already sunk billions of dollars of investment. Worldwide, companies have stopped construction on 90 reactors since the 1980s. 40 of those were in the U.S. alone. For example, in 2017, two South Carolina utilities abandoned two unfinished Westinghouse AP1000 reactors due to significant construction delays and cost overruns. At the time, this left two other U.S. AP1000 reactors under construction in Georgia. The original cost estimate of $14 billion for these two reactors rose to $23 billion. Construction only continued when the U.S. federal government promised financial support. These were the first new reactors in the U.S. in decades. In contrast, recent NPPs in China have taken only four to six years and $2 billion dollars per reactor. When considering the choice of investing in nuclear energy versus fossil fuel energy, note that a typical natural gas plant takes approximately two years to construct (Lovering et al., 2016).
Chernobyl, to be clear, was a very costly disaster
The initial emergency response, together with later decontamination of the environment, required more than 500,000 personnel and an estimated US$68 billion (2019 USD). Between five and seven percent of government spending in Ukraine is still related to Chernobyl. (emphasis added, AT) In Belarus, Chernobyl-related expenses fell from twenty-two percent of the national budget in 1991 to six percent by 2002.
The biggest safety effect of the decline in nuclear power plants was the increase in air pollution. The authors use satellite date on ambient particles to show that when a new nuclear plant comes online pollution in nearby cities declines significantly. Second, they use the decline in pollution to create preliminary estimates of the effect of pollution on health:
According to our calculations, the construction of an additional NPP, by reducing the total suspended particles (TSP) in the ambient environment, could on average save 816,058 additional life years.
According to our baseline estimates (Table 1), over the past 38 years, Chernobyl reduced the total number of NPPs worldwide by 389, which is almost entirely driven by the slowdown of new construction in democracies. Our calculations thus suggest that, globally, more than 318 million expected life years have been lost in democratic countries due to the decline in NPP growth in these countries after Chernobyl.
The authors use the Air Quality Life Index from the University of Chicago which I think is on the high side of estimates. Nevertheless, as you know, I think the new air pollution literature is credible (also here) so I think the bottom line is almost certainly correct. Namely, Chernobyl caused many more deaths by reducing nuclear power plant construction and increasing air pollution than by its direct effects which were small albeit not negligible.