There has been something rather ghoulish about environmental discourse throughout the pandemic. As lockdowns forced people to stay home, city skies cleared. Pollution went down, animals roamed urban streets, and social-media users cheered.
It feels as though people were getting a little too happy about all this. Can you imagine if someone were to say, “Yeah, a lot of people died in the war, but at least there’s way less traffic”?
It makes you wonder whether there’s a part of these people that secretly wishes we’d never return to normal because that would be what’s best for the deified Mother Nature.
Over time, “trust the science” has morphed into “fear the science.” Activists, journalists, and politicians seem to show an interest in news in the scientific community only when it involves something that can induce panic.
This was especially true with the models for COVID hospitalizations. Remember how the predictions led the U.S. Navy to send several thousand-bed hospital ships to New York City and Los Angeles? And that they then hosted only a combined total of 35 patients?
What about when state officials and experts went into a frenzy over ventilator shortages, only for us later to discover that there are no known documented cases of a COVID patient lacking access to a ventilator?
According to Politifact (a notorious agent of the vast right-wing conspiracy), most states had more ventilators than they knew what to do with.
The whole initial justification for the lockdown decrees, to “flatten the curve” in order to prevent an overload of our healthcare system, turned out to be questionable.
Yes, some hospitals were indeed overrun by COVID patients. But there was never a serious danger of shortages, as there was ample backup of supplies. The shortages that did occur were problems of bureaucratic red tape.
Epidemiologists at the CDC notoriously redefined the goal: Even when we had flattened the curve, we were told, for a variety of reasons, that we must indefinitely postpone our return to normalcy.
Today, even for the vaccinated in counties that have single-digit weekly COVID death counts, localities and universities have reimposed testing and mask mandates.
Having been expected to blindly trust the public-health establishment, even when much of their expert advice seems to go against common sense and actual data, we are also asked to place our trust in the climatologists.
The problem is, their track record is no better. Last Monday, the U.N.’s Intergovernmental Panel on Climate Change predicted that entire countries may vanish thanks to rising sea levels within the century.
They may very well be right — I don’t claim to be an expert on greenhouse gases. What I do know, however, is that past divinations have constantly overstated our impending doom. As an instructive exercise, let us explore a few.
In the 1980s, NASA predicted that sea levels would rise about 20 feet by 2080 — quite a considerable increase. But according to NASA and the EPA, in the 37 years since the initial prediction, sea levels have risen less than four inches. It appears we have some catching up to do.
Additionally, there is a good deal of debate on whether or not we have, on net, lost any land at all. National Geographic reports that the Earth’s total land area has actually been increasing, resulting in a land gain of about the size of the Great Lakes over a span of 30 years. Even some Polynesian islands have defied the expectation of land loss.
In 2008, the president of the U.N. General Assembly predicted the displacement of 50 to 200 million environmental refugees by 2010. In reality, the number of people “forcibly displaced” for all reasons worldwide — 43.7 million — was lower than his low-end prediction for just “environmental refugees.” Many ice-melting forecasts have been appallingly wrong, as well.
Most infuriating are the fearmongers’ ever-teleporting goalposts. In 1989, the U.N. warned that the world had until 2000 to reduce emissions to avoid the irreversible devastation of entire nations.
In 2006, James Hansen, the United States’ leading expert on climate change, warned we had until 2016. Then there’s that obnoxious New York digital timer that went up in 2020, saying “the Earth has a deadline” — with about six and a half years remaining.
Notably, the people responsible for the timer neglect to mention that the research they’re basing that claim on says we may have until 2045. I guess they’re saving that detail for the next inevitable readjustment of the doomsday clock.
The stubborn insistence that we have some sort of deadline is an unambiguous effort to spur drastic political action. It’s much harder for politicians to win by running on a platform that targets a problem that won’t become urgent for decades.
The reality is that no one has a darn clue when the most harmful trends of climate change will be unstoppable. Different scientists cling to different models. According to Princeton, it may already be too late.
This is not a matter of suspecting the personal motives of climate alarmists. I trust that most people who devote their lives to humanitarian issues such as climate change probably have the best interest of the public at heart.
There certainly are notable cases of scandal in the world of climate science and, as with COVID, a tendency to crush dissent. While Big Tech censors Rand Paul for saying that cloth masks are useless, the tyrannical engineer Bill Nye demands jail time for climate dissenters.
My issue specifically lies with the clearly defective algorithms that pandemic and climate experts keep using.
Dan McLaughlin provides the following excellent analysis:
Consider the models under closest scrutiny right now: weather models such as hurricane models. These are the best kind of model, in the sense that the raw data is derived from intensive real-time observation and the historical data is derived from a huge number of observations and thus not dependent on a tiny and potentially unrepresentative sample.
Yet, as you watch any storm develop, you see its projected path change, sometimes dramatically. Why? Because the models are highly sensitive to changes in raw data, and because storms are dynamic systems: Their path follows a certain logic but does not track a wholly predictable trajectory. The constant adjustments made to weather models ought to give us a little more humility in dealing with models that suffer from greater flaws in raw data observations, smaller sample sizes in their bases of historical data, or that purport to explain even more complex or dynamic systems — models such as climate modeling.
The response to COVID should be a warning to everyone. Epidemiologists, working with many times more resources, in a field far larger and older than that of climatologists, convinced people in the U.S. and many other open societies that it was necessary to sacrifice an unprecedented degree of freedom.
Similarly, “science-backed” plans such as the Green New Deal seek to solve incredibly complex problems via a complete takeover of the American private sector.
On top of that, even if all of the United States and Europe were to be Thanos-snapped out of existence tomorrow, developing countries are increasing their emissions at a pace fast enough to make the absence of our CO2 meaningless.
Will South Asian and African nations be willing to reduce consumption when, between these two regions, there are about a billion people without access to electricity? When there are more than twice as many without enough power to cook? Experts at the Centre for Policy Research, a leading Indian think tank, are not so optimistic.
The late Freeman Dyson, a brilliant mathematician and physicist, noticed the issues with the climate models over a decade ago. Like many other pragmatists, he did not advocate for massive government programs to reduce carbon dioxide levels.
He argued that the benefits for the global poor of carbon-emitting activities outweigh the harms by increasing life expectancy and access to education.
Read rest at NRO
Climate Doomsayers are more nutty then a Warehouse full of Christmas Fruitcakes
Well said, Aron.
My pet peeve with advocates is the line “If it saves one life, it was worth it” Why? Because they say so, that’s why. Other people’s money (OPM) makes it work for them.