Category Uncategorized

Nov 14 2022

Ukraine, a Ponzi Scheme, and a Top Democrat Donor Raise Serious Questions


Repost

https://redstate.com/bonchie/2022/11/13/ukraine-a-ponzi-scheme-and-a-top-democrat-donor-raise-serious-questions-n658409

Ukraine, a Ponzi Scheme, and a Top Democrat Donor Raise Serious Questions

By Bonchie | 4:00 PM on November 13, 2022

AP Photo/J. Scott Applewhite, Pool

As RedState reported, crypto-exchange FTX collapsed after its much-lauded founder, Sam Bankman-Fried, appeared to make improper transfers of customer money. Somewhere between $1-2 billion of that amount has now gone missing and Bankman-Fried also has disappeared.

What makes this so interesting, though, isn’t just that a lot of really wealthy people got scammed. It’s that Bankman-Fried also happens to be one of the top donors to the Democratic Party. In fact, outside of George Soros, no one has done more to bankroll Democrat efforts since the 2020 election. Joe Biden alone received a whopping $5.2 million.

But here’s where things get even weirder. Apparently, while the United States was bankrolling Ukraine and its war effort, that country’s leaders were investing money into FTX.

It was also revealed that FTX had partnered with Ukraine to process donations to their war efforts within days of Joe Biden pledging billions of American taxpayer dollars to the country. Ukraine invested into FTX as the Biden administration funneled funds to the invaded nation, and FTX then made massive donations to Democrats in the US.

There are so many questions that arise from this. For example, why is Ukraine, which we are all assured is broke and needs US taxpayer money, playing around with a Democrat-linked crypto company? This wasn’t just about accepting donations through the portal. The report specifically says that Ukraine actively invested money in FTX.

While that was happening, FTX’s founder was handing out tens of millions of dollars, from the Bahamas, to help elect Democrats back in the United States. That is one of the shadiest things I’ve ever witnessed in politics.

Yes, the chain of custody regarding the funds involved is tough to know. When and where money was sent is something only an investigation of FTX’s internal operation can ascertain. Still, the appearances here are just horrific. Were Democrats funneling taxpayer money to Ukraine, only for some of it to be sent to FTX so it could be funneled back to Democrat campaigns? That’s a question that must be answered, and any attempt to gloss over it will raise major red flags.

I don’t think I’m going out on a limb by suggesting that if another company had been scamming people while bankrolling the Republican Party, it would be major news. There would be calls for investigations as far as the eye could see to figure out whether Republican politicians were using that company as a passthrough to avoid campaign finance laws. Never mind that simply receiving funds from a Ponzi scheme, even without ill intent, is really bad on its own.

This entire situation stinks to high heaven. It appears that Republicans will end up taking the House of Representatives. When that becomes official, GOP members need to dive headfirst into this and figure out what in the world happened. Because having a Democrat mega-donor get exposed like this while also having Ukraine tied up in the mix is too much to ignore.

Front-page contributor for RedState. Visit my archives for more of my latest articles and help out by following me on Twitter @bonchieredstate.

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Nov 12 2022

The Dirty Secrets inside the Black Box Climate Models


The Dirty Secrets inside the Black Box Climate Models
By Greg Chapman
“The world has less than a decade to change course to avoid irreversible ecological catastrophe, the UN warned today.” The Guardian Nov 28 2007
“It’s tough to make predictions, especially about the future.” Yogi Berra
Introduction
Global extinction due to global warming has been predicted more times than climate activist, Leo DiCaprio, has traveled by private jet.  But where do these predictions come from? If you thought it was just calculated from the simple, well known relationship between CO2 and solar energy spectrum absorption, you would only expect to see about 0.5o C increase from pre-industrial temperatures as a result of CO2 doubling, due to the logarithmic nature of the relationship.
Figure 1: Incremental warming effect of CO2 alone [1]
The runaway 3-6o C and higher temperature increase model predictions depend on coupled feedbacks from many other factors, including water vapour (the most important greenhouse gas), albedo (the proportion of energy reflected from the surface – e.g. more/less ice or clouds, more/less reflection) and aerosols, just to mention a few, which theoretically may amplify the small incremental CO2 heating effect. Because of the complexity of these interrelationships, the only way to make predictions is with climate models because they can’t be directly calculated.
The purpose of this article is to explain to the non-expert, how climate models work, rather than a focus on the issues underlying the actual climate science, since the models are the primary ‘evidence’ used by those claiming a climate crisis. The first problem, of course, is no model forecast is evidence of anything. It’s just a forecast, so it’s important to understand how the forecasts are made, the assumptions behind them and their reliability.
How do Climate Models Work?
In order to represent the earth in a computer model, a grid of cells is constructed from the bottom of the ocean to the top of the atmosphere. Within each cell, the component properties, such as temperature, pressure, solids, liquids and vapour, are uniform.
The size of the cells varies between models and within models. Ideally, they should be as small as possible as properties vary continuously in the real world, but the resolution is constrained by computing power. Typically, the cell area is around 100×100 km2 even though there is considerable atmospheric variation over such distances, requiring each of the physical properties within the cell to be averaged to a single value. This introduces an unavoidable error into the models even before they start to run.
The number of cells in a model varies, but the typical order of magnitude is around 2 million.
Figure 2: Typical grid used in climate models [2]

Once the grid has been constructed, the component properties of each these cells must be determined. There aren’t, of course, 2 million data stations in the atmosphere and ocean. The current number of data points is around 10,000 (ground weather stations, balloons and ocean buoys), plus we have satellite data since 1978, but historically the coverage is poor. As a result, when initialising a climate model starting 150 years ago, there is almost no data available for most of the land surface, poles and oceans, and nothing above the surface or in the ocean depths. This should be understood to be a major concern.
Figure 3: Global weather stations circa 1885 [3]

Once initialised, the model goes through a series of timesteps. At each step, for each cell, the properties of the adjacent cells are compared. If one such cell is at a higher pressure, fluid will flow from that cell to the next. If it is at higher temperature, it warms the next cell (whilst cooling itself). This might cause ice to melt or water to evaporate, but evaporation has a cooling effect. If polar ice melts, there is less energy reflected that causes further heating. Aerosols in the cell can result in heating or cooling and an increase or decrease in precipitation, depending on the type.
Increased precipitation can increase plant growth as does increased CO2. This will change the albedo of the surface as well as the humidity. Higher temperatures cause greater evaporation from oceans which cools the oceans and increases cloud cover. Climate models can’t model clouds due to the low resolution of the grid, and whether clouds increase surface temperature or reduce it, depends on the type of cloud.
It’s complicated! Of course, this all happens in 3 dimensions and to every cell resulting in considerable feedback to be calculated at each timestep.
The timesteps can be as short as half an hour. Remember, the terminator, the point at which day turns into night, travels across the earth’s surface at about 1700 km/hr at the equator, so even half hourly timesteps introduce further error into the calculation, but again, computing power is a constraint.
While the changes in temperatures and pressures between cells are calculated according to the laws of thermodynamics and fluid mechanics, many other changes aren’t calculated. They rely on parameterisation. For example, the albedo forcing varies from icecaps to Amazon jungle to Sahara desert to oceans to cloud cover and all the reflectivity types in between. These properties are just assigned and their impacts on other properties are determined from lookup tables, not calculated. Parameterisation is also used for cloud and aerosol impacts on temperature and precipitation. Any important factor that occurs on a subgrid scale, such as storms and ocean eddy currents must also be parameterised with an averaged impact used for the whole grid cell. Whilst the effects of these factors are based on observations, the parameterisation is far more a qualitative rather than a quantitative process, and often described by modelers themselves as an art, that introduces further error. Direct measurement of these effects and how they are coupled to other factors is extremely difficult and poorly understood.
Within the atmosphere in particular, there can be sharp boundary layers that cause the models to crash. These sharp variations have to be smoothed.
Energy transfers between atmosphere and ocean are also problematic. The most energetic heat transfers occur at subgrid scales that must be averaged over much larger areas.
Cloud formation depends on processes at the millimeter level and are just impossible to model. Clouds can both warm as well as cool. Any warming increases evaporation (that cools the surface) resulting in an increase in cloud particulates. Aerosols also affect cloud formation at a micro level.  All these effects must be averaged in the models.
When the grid approximations are combined with every timestep, further errors are introduced and with half hour timesteps over 150 years, that’s over 2.6 million timesteps! Unfortunately, these errors aren’t self-correcting. Instead this numerical dispersion accumulates over the model run, but there is a technique that climate modelers use to overcome this, which I describe shortly.
Figure 4: How grid cells interact with adjacent cells [4]

Model Initialisation
After the construction of any type of computer model, there is an initalisation process whereby the model is checked to see whether the starting values in each of the cells are physically consistent with one another. For example, if you are modelling a bridge to see whether the design will withstand high winds and earthquakes, you make sure that before you impose any external forces onto the model structure other than gravity, that it meets all the expected stresses and strains of a static structure. Afterall, if the initial conditions of your model are incorrect, how can you rely on it to predict what will happen when external forces are imposed in the model?
Fortunately, for most computer models, the properties of the components are quite well known and the initial condition is static, the only external force being gravity. If your bridge doesn’t stay up on initialisation, there is something seriously wrong with either your model or design!
With climate models, we have two problems with initialisation. Firstly, as previously mentioned, we have very little data for time zero, whenever we chose that to be. Secondly, at time zero, the model is not in a static steady state as is the case for pretty much every other computer model that has been developed. At time zero, there could be a blizzard in Siberia, a typhoon in Japan, monsoons in Mumbai and a heatwave in southern Australia, not to mention the odd volcanic explosion, which could all be gone in a day or so.
There is never a steady state point in time for the climate, so it’s impossible to validate climate models on initialisation.
The best climate modelers can hope for is that their bright shiny new model doesn’t crash in the first few timesteps.
The climate system is chaotic which essentially means any model will be a poor predictor of the future – you can’t even make a model of a lottery ball machine (which is a comparatively a much simpler and smaller interacting system) and use it to predict the outcome of the next draw.
So, if climate models are populated with little more than educated guesses instead of actual observational data at time zero, and errors accumulate with every timestep, how do climate modelers address this problem?
History matching
If the system that’s being computer modelled has been in operation for some time, you can use that data to tune the model and then start the forecast before that period finishes to see how well it matches before making predictions. Unlike other computer modelers, climate modelers call this ‘hindcasting’ because it doesn’t sound like they are manipulating the model parameters to fit the data.
The theory is, that even though climate model construction has many flaws, such as large grid sizes, patchy data of dubious quality in the early years, and poorly understood physical phenomena driving the climate that has been parameterised, that you can tune the model during hindcasting within parameter uncertainties to overcome all these deficiencies.
While it’s true that you can tune the model to get a reasonable match with at least some components of history, the match isn’t unique.
When computer models were first being used last century, the famous mathematician, John Von Neumann, said:
“with four parameters I can fit an elephant, with five I can make him wiggle his trunk”
In climate models there are hundreds of parameters that can be tuned to match history. What this means is there is an almost infinite number of ways to achieve a match. Yes, many of these are non-physical and are discarded, but there is no unique solution as the uncertainty on many of the parameters is large and as long as you tune within the uncertainty limits, innumerable matches can still be found.
An additional flaw in the history matching process is the length of some of the natural cycles. For example, ocean circulation takes place over hundreds of years, and we don’t even have 100 years of data with which to match it.
In addition, it’s difficult to history match to all climate variables. While global average surface temperature is the primary objective of the history matching process, other data, such a tropospheric temperatures, regional temperatures and precipitation, diurnal minimums and maximums are poorly matched.
Even so, can the history matching of the primary variable, average global surface temperature, constrain the accumulating errors that inevitably occur with each model timestep?
Forecasting
Consider a shotgun. When the trigger is pulled, the pellets from the cartridge travel down the barrel, but there is also lateral movement of the pellets. The purpose of the shotgun barrel is to dampen the lateral movements and to narrow the spread when the pellets leave the barrel. It’s well known that shotguns have limited accuracy over long distances and there will be a shot pattern that grows with distance.  The history match period for a climate model is like the barrel of the shotgun. So what happens when the model moves from matching to forecasting mode?
Figure 5: IPCC models in forecast mode for the Mid-Troposphere vs Balloon and Satellite observations [5]
Like the shotgun pellets leaving the barrel, numerical dispersion takes over in the forecasting phase. Each of the 73 models in Figure 5 has been history matched, but outside the constraints of the matching period, they quickly diverge.
Now at most only one of these models can be correct, but more likely, none of them are. If this was a real scientific process, the hottest two thirds of the models would be rejected by the International Panel for Climate Change (IPCC), and further study focused on the models closest to the observations. But they don’t do that for a number of reasons.
Firstly, if they reject most of the models, there would be outrage amongst the climate scientist community, especially from the rejected teams due to their subsequent loss of funding. More importantly, the so called 97% consensus would instantly evaporate.
Secondly, once the hottest models were rejected, the forecast for 2100 would be about 1.5o C increase (due predominately to natural warming) and there would be no panic, and the gravy train would end.
So how should the IPPC reconcile this wide range of forecasts?
Imagine you wanted to know the value of bitcoin 10 years from now so you can make an investment decision today. You could consult an economist, but we all know how useless their predictions are. So instead, you consult an astrologer, but you worry whether you should bet all your money on a single prediction. Just to be safe, you consult 100 astrologers, but they give you a very wide range of predictions. Well, what should you do now? You could do what the IPCC does, and just average all the predictions.
You can’t improve the accuracy of garbage by averaging it.
An Alternative Approach
Climate modelers claim that a history match isn’t possible without including CO2 forcing. This is may be true using the approach described here with its many approximations, and only tuning the model to a single benchmark (surface temperature) and ignoring deviations from others (such as tropospheric temperature), but analytic (as opposed to numeric) models have achieved matches without CO2 forcing. These are models, based purely on historic climate cycles that identify the harmonics using a mathematical technique of signal analysis, which deconstructs long and short term natural cycles of different periods and amplitudes without considering changes in CO2 concentration.
In Figure 6, a comparison is made between the IPCC predictions and a prediction from just one analytic harmonic model that doesn’t depend on CO2 warming. A match to history can be achieved through harmonic analysis and provides a much more conservative prediction that correctly forecasts the current pause in temperature increase, unlike the IPCC models. The purpose of this example isn’t to claim that this model is more accurate, it’s just another model, but to dispel the myth that there is no way history can be explained without anthropogenic CO2 forcing and to show that it’s possible to explain the changes in temperature with natural variation as the predominant driver.
Figure 6: Comparison of the IPCC model predictions with those from a harmonic analytical model [6]

In summary:
Climate models can’t be validated on initiatialisation due to lack of data and a chaotic initial state.
Model resolutions are too low to represent many climate factors.
Many of the forcing factors are parameterised as they can’t be calculated by the models.
Uncertainties in the parameterisation process mean that there is no unique solution to the history matching.
Numerical dispersion beyond the history matching phase results in a large divergence in the models.
The IPCC refuses to discard models that don’t match the observed data in the prediction phase – which is almost all of them.
The question now is, do you have the confidence to invest trillions of dollars and reduce standards of living for billions of people, to stop climate model predicted global warming or should we just adapt to the natural changes as we always have?
Greg Chapman  is a former (non-climate) computer modeler.
Footnotes
[1] https://www.adividedworld.com/scientific-issues/thermodynamic-effects-of-atmospheric-carbon-dioxide-revisited/
[2] https://serc.carleton.edu/eet/envisioningclimatechange/part_2.html
[3] https://climateaudit.org/2008/02/10/historical-station-distribution/
[4]            http://www.atmo.arizona.edu/students/courselinks/fall16/atmo336/lectures/sec6/weather_forecast.html
[5] https://www.drroyspencer.com/2013/06/still-epic-fail-73-climate-models-vs-measurements-running-5-year-means/
Whilst climate models are tuned to surface temperatures, they predict a tropospheric hotspot that doesn’t exist. This on its own should invalidate the models.
[6] https://wattsupwiththat.com/2012/01/09/scaffeta-on-his-latest-paper-harmonic-climate-model-versus-the-ipcc-general-circulation-climate-models/
Nov 9 2022

Maricopa County Arizona Has Election Vote Counting and Tabulation Issues Again


Posted originally on the conservative tree house on November 8, 2022 | Sundance

The counties with ballot counting issues remain consistent over years until someone steps in and fixes the root cause of the problem, democrat election officials.  Nothing destroys election integrity faster than county election problems that repeat in the exact same precincts year after year.

Unfortunately, Maricopa County, Arizona, is one of those regional areas with major election integrity problems each voting cycle, this midterm 2022 election is no different.

According to multiple reports Maricopa County ballot tabulation machines are not working again.  Approximately 20% of the ballot tabulation machines in Maricopa County are not working which is causing delays, frustration and voter concern over the integrity of the election.  Voters have been told to leave their ballots in a box for tabulation later at a central location.  Many voters are not willing to ‘trust’ the process.

ARIZONA – Vote-counting machines weren’t working in about 20% of polling sites in Maricopa County, Arizona, as Election Day voting in the midterms began, county officials said.

The Maricopa County Recorder’s Officer said technicians were called to fix the tabulator machines that weren’t working, Fox10’s TV station in Phoenix reported. It’s not clear how many of the machines were malfunctioning in the state’s most populous county.

“About 20% of the locations out there where there’s an issue with the tabulator … they try and run (completed ballots) through the tabulator, and they’re not going through,” Maricopa County Board of Supervisors chairman Bill Gates said in a video posted on Facebook. Long lines of voters were appearing throughout the county as officials tried to reassure people that all votes would be counted. (read more)

STAY IN LINE and VOTE!

Nov 3 2022

McCaughey: Zeldin Surge Feels Like ’94 Win


Newsmax TV Published originally on Rumble on November 2, 2022

Former New York Lt. Gov. Betsy McCaughey joins us to discuss the potential for a red wave surge into heavily-controlled blue areas.

Oct 11 2022

Biden Announces U.S. Air Defense System for Ukraine Following Retaliatory Russian Missile Attacks


Posted originally on the conservative tree house on October 10, 2022 | Sundance

In a statement today from the White House, Joe Biden has pledged “to continue providing Ukraine with the support needed to defend itself, including advanced air defense systems.” [link]  The statement comes as a result of a phone call between Biden and Ukraine President Volodymyr Zelenskyy.

Two days ago, a bridge between Russia and Crimea was bombed by Ukraine causing a section of the bridge to collapse.  Yesterday, Vladimir Putin retaliated with missile strikes against several cities in Ukraine and key infrastructure for energy.

The U.S. State Dept and CIA are continuing to lead and coordinate the Ukraine war effort with U.S. personnel in place to organize operations. [link]

Like the bombing of the Nordstream pipeline, it is highly likely the Kerch Strait bridge targeting was planned by Ukraine and the United States.  However, we are not permitted to speak about these coordinated efforts. The bottom line is the U.S. Biden administration going further toward direct engagement with Russia via the proxy state of Ukraine.

KYIV, Ukraine — President Vladimir V. Putin unleashed a far-reaching series of missile strikes against cities across Ukraine on Monday, hitting the heart of Kyiv and other areas far from the front line, in the broadest assault against civilians since the early days of Russia’s invasion.

Mr. Putin said the strikes on almost a dozen cities were retaliation for a blast that destroyed sections of a bridge linking Russia to the Crimean Peninsula, though they also seemed intended to appease hard-liners in Russia who had been openly critical over the prosecution of the war.

Denouncing the bombing of the Kremlin-built bridge, an embarrassing blow, as a “terrorist attack,” Mr. Putin threatened more strikes if Ukraine hit Russian targets again.

“No one should have any doubt about it,” he said. (read more)

BIG PICTURE – The baseline for the global aspect to the Ukraine conflict remains rooted in the economic cleaving underway.  Saudi Arabia has expressed their alignment with OPEC+, including Russia, on a coordinated oil supply.  The NATO alliance broadly wants to pressure their partners away from oil, coal and natural gas.   Thus, today in addition to the phone call with Zelenskyy, Joe Biden called German Chancellor Olaf Scholz:

“President Joseph R. Biden, Jr. spoke today with Chancellor Olaf Scholz of Germany.  The leaders reiterated their condemnation of Russia’s attempted annexation of Ukrainian territory, as well as their ongoing commitment to hold Russia accountable for its brutal actions and provide security and economic assistance to Ukraine. The leaders also discussed recent developments in global energy markets and the importance of securing sustainable and affordable energy supplies.” (link)

The western ideologues, politicians, corporations and banks (yellow on map) are trying to force a new global energy system.  However, there is opposition from multiple nations (grey on map) who see the effort to shift away from oil, coal and natural gas as economic suicide.

We are now starting to see currencies like Brazil and Mexico having greater stability and growth than the value in U.S. dollars.  That said, in the long term the economics of this conflict will ultimately decide the outcome.

The citizens within the western alliance nations are suffering the consequences of the global economic cleaving.  Energy driven inflation, a purposefully created problem, is creating a recession amid the western alliance nations.  The monetary policy of U.S., EU, CA, NZ and AU is currently constructed to lower economic activity to support the reduced amount of energy resources available.

Specifically, because the Ukraine conflict is being used as a justification for political economic and monetary policy, there appears to be no limit in what the U.S. will do to widen the ideological war against Russia.  Unfortunately, the era of great pretending forbids anyone from talking openly about the true root of the issue.

It seems clear now that NATO, led by the USA, is willing to escalate a European war if that’s what it takes to protect the climate change goals.

Without Russia as the bad guy, the Build Back Better agenda becomes naked to the world.  The Ukraine conflict provides a visible shield to prevent any larger discussion.  Reference the attempt by Elon Musk to mitigate the Ukraine conflict by talking about a peace deal with Russia keeping the Russian speaking eastern Ukraine.  In the week since that proposal became international fodder for ridicule, the conflict in Ukraine has intensified.  This is not coincidental.

We are pretending our way into a war.

Sep 19 2022

Trump Reveals His Key Whitness


The Charlie Kirk Show Published originally on Rumble on September 17, 2022

Charlie Kirk always has a good take on what is going on

Aug 28 2022

We’re Controlling You Even MORE Now!


Awaken With JP Published originally on Rumble on August 27, 2022

Welcome to tonight’s broadcast, we can’t believe you’re dumb enough to keep watching, but here we go!

Aug 19 2022

Pelosi – Taiwan – China


Armstrong Economics Blog/China Re-Posted Aug 19, 2022 by Martin Armstrong

QUESTION: With the bank runs in China, do you still think China will surpass the US economy? What about Pelosi’s trip? China’s response was more shock and see than anyone expected.

GD

ANSWER: Nothing has changed. But you have to understand that the decline in the US economy also benefits China’s rise. The Democrats are attacking corporations and hiring 87,000 IRS agents to harass the people. There comes a point where it no longer pays to work. They do not understand that. I used to manage money in the USA and quit in 1985. I handed back all the money and told clients I was retiring. The auditors were crazy. They view NOBODY as honest, so they will not leave until they can charge you with some infraction. Mine was the tabs on our files were “pink” not “red” so they were going to write me up for improper record keeping. I am not one to cower. That was it. I said, fine. I would close the business. IRS agents are no different, but now they will be armed. Later, the government had the audacity to say I was prejudiced against Americans and refused to manage their money. I warned Congress that they were forcing everyone offshore and thus began the hedge fund business. We are at such a crossroads, and the future does not look bright.

As far as Nancy Pelosi’s recent trip to Taipei, it demonstrated the complete lack of international expertise that has engulfed Washington. There appears to be a complete lack of intelligence, and that is by no means confined to just Democrats. Anyone who supported her trip is totally incompetent in international politics.  The trip was supposed to demonstrate US confidence in Taiwan’s leadership, but instead, it provoked a reaction from China that undermined the entire region. Beijing has apparently emerged with much more confidence than ever that it could retake Taiwan by force if necessary.

There were some people in the White House and the US Defense Department who told her to postpone her trip. Pelosi had announced her trip on her own and then faced political pressure not to back down once her plans became public. On  July 31, 2022, Xi Jinping, general secretary of the Communist Party of China (CPC) Central Committee, attended a reception to celebrate the 95th founding anniversary of the People’s Liberation Army (PLA), which fell on August 1. Her sheer stupidity was a slap in the face to Jinping, showing her total ignorance of international politics.

The Biden Administration has been forced to downplay the trip’s significance. In the process, the Biden Administration was forced to reaffirm its commitment to the United States’ long-standing “One China” policy, which recognizes Beijing as “the sole legal government of China,” while the US just ignores any claims to rule Taiwan. The Biden Administration has been forced to try to tell China that nothing has changed despite the stupidity of Pelosi.

China dramatically put on an expert show of military force that was awesome. Beijing, within hours after Pelosi left Taipei, showed to the world that China’s military spectacle was without precedent in scope and scale. Beijing is merely waiting for the right moment to take Taiwan. Beijing no longer trusts that the American policy of “One China” is in play with all the war drums beating over Taiwan. Biden has stated that the United States has a “commitment” to aid Taiwan in the event of a Chinese invasion. Even NATO has joined in the rhetoric against China. The timing of Pelosi’s trip merely reinforced the support for Jinping in the coming Chinese Communist Party leadership.

This demonstration showed the world just how far China has progressed militarily since the poor performance during the 1996 Taiwan missile crisis. They can take Taiwan with air and sea assets. China conducted, for the first time, simulated attacks on Taiwan in the actual airspace and territorial waters. China is trying to demonstrate that Taiwan should surrender rather than see its people die and its infrastructure destroyed, as is taking place in Ukraine.

In addition, China has confirmed to the entire world that it could, at any time it chooses, severely disrupt or more likely outright block critical global air and sea trade routes. That would cut off all Taiwanese-produced semiconductors while staring them out from imports. Pelosi’s trip has backfired completely, and instead of showing support and confidence in Taiwan, she has illustrated its vulnerability.

Worse still, the Biden Administration’s sanctions on Russia have destroyed globalization. It has also forged a hardline alliance between China, Russia, North Korea, and many more states swinging to their side against what many call the arrogance of the United States. This reminds me of the Peloponnesian War over the arrogance of Athens, and many joined their enemy, Sparta, and Athens lost.