By Andreas Tiedtke

This article first appeared at the Ludwig von Mises Institut, Deutschland. It has been translated with permission from the original German into English, by Andy Duncan. Here is the original article. The detailed references in the article below link directly to the glossary attached to the original piece. 

On the 17th of November in Bonn, the 23rd UN Climate Change Conference (COP 23) finally concluded, with 20,000 participants from all over the world, accompanied by a substantial contingent of journalists. [1] This conference continued the process of the Paris climate agreement, one of the largest state control projects in human history. From 2020 onwards, ‘industrial countries’ are expected to provide an annual sum of $100 billion U.S. dollars to restructure the world’s energy supply. This money will also be used to ‘eliminate any damage caused by climate change’. Much of this cash is expected to flow into private sector investment within developing countries, with only a relatively small proportion going to the public sector for the ‘already necessary remedy of climate-related damage’. [2]

Of course, the politicians of these nominated ‘industrialised countries’ will hardly expect to pay all of this cash out of their own pockets, but will instead mandate their taxpayers to pick up the tab. As far as this ‘private investment’ is concerned, there would be no need for any agreement if these projects were actually profitable within a genuine free market. Instead, the relevant financial incentives are created through regulation and subsidy, which then restricts freedom of choice for private producers and also blunts the free choices of taxpayers. In addition, the International Monetary Fund is calling for a carbon tax [3], even though net taxpayers have already been heavily burdened by energy taxes, vehicle taxes, and other such charges and regulations within the housing construction business.

However, if citizens decide that politicians are unable to provide substantiated evidence to back up a massive encroachment upon assets and freedoms, this could prove catastrophic for politicians, as well as for entire industrial segments that no longer rely upon the freely chosen demands of customers, but instead rely upon taxpayer subsidy and government regulation. These industries range from the manufacture of wind turbines and electric cars through to that of electric lamps and polystyrene thermal insulation panels. And let’s not forget the climate conference industry itself.

It depends upon the scientific method

If anyone actually wants to determine whether there is climate change, we should perhaps establish its causes, or whether its consequences can be prevented or mitigated by human intervention. Various conceptual scientific methods are available to do this. We could choose any one of many different branches of science, whether it be economics, mathematics, logic or even the science of history. Each of these branches possesses its own methodology to gain scientific insight.

Since the subject of our study is climate change, perhaps the first term we ought to examine is the word ‘climate’. The climate [4] is a 30-year average of various weather conditions including temperature, wind direction and speed, humidity, precipitation type and volume, cloud cover, and so on. It is not a computational product generated by applying formulas that specify constant relations, but rather a product of merging measured historical averages. In short, it is a ‘statistic’. This is one of the many things that the Austrian economist Ludwig von Mises (1881 – 1973) wrote, when examining statistical data:

“Experience is always experience of the past. Experience and history are never in the future. This truism would not have to be repeated unless there was the problem of forecasts by statisticians … [5]  Statistics is the description of phenomena that are not characterised by regular uniformity, in numerical terms. As far as there is a recognisable regularity in the sequence of phenomena, it is not necessary to resort to statistics. … Statistics is therefore a specific method of historiography. … It’s about the past, not the future. Like any other past experience, it can occasionally provide important future planning services, but it does not say anything that is directly valid for the future.”

And further:

“There are no such things as statistical laws. People resort to statistical methods precisely because they are unable to recognise a regularity in the chain and sequence of events.” [6]

For example, if a statistic demonstrates that ‘A’ follows from 95% of the cases of ‘B’ and from 5% of the cases of ‘C’, this means that there is no complete knowledge about ‘A’. It should get decomposed into ‘A1’ and ‘A2’. If it could then be established that ‘A1’ always follows ‘B’ and ‘A2’ always follows ‘C’, then perfect knowledge would exist. [7]

Natural science

Statistics are simply incapable of providing scientific proof. Here’s why:

“The world of science is the field in which the human mind is quite capable of discovering constant relationships between elements. [8] In the field of physics and chemistry, there are … constant relationships between magnitudes, and man is capable of recognising these constant relationships with sufficient accuracy in laboratory experiments.” [9]

However, Mises emphasises that even the natural sciences remain unable to deliver ‘exact’ results. Here’s how he explains his thinking:

“Laboratory experiments and observation of external phenomena allow science to measure and to quantify knowledge. Today, no one denies that due to the inadequacy of our senses, measurements are never perfectly accurate within the overall meaning of the term. They provide more or less accurate approximations. Incidentally, the Heisenberg uncertainty principle shows that there are relationships that can no longer be measured at all. There is no quantitative exactitude in our description of natural phenomena. However, the approximations provided by physical and chemical objects are broad enough for practical purposes. The world of technology is a world of approximate measurement and approximate quantitative certainty.” [10]

Although even these hard sciences are unable to provide secure knowledge, they do enjoy a great level of trust. And rightly so. They have produced many practical findings. We should be especially thankful for those laboratory experiments that can demonstrate constant relations, especially if they prove repeatable at will.

Specific understanding

As long as constant relations remain unestablished, as is the case with the natural sciences, interpretations of historical data, or predictions of future events (i.e. the history of the future), should instead apply methods of ‘specific understanding’ :

“[Understanding] is the method that all historians and all other people always apply when it comes to interpreting past events in human history and predicting future events. The discovery and demarcation of understanding was one of the most important contributions of modern epistemology. … The scope of understanding is the mental comprehension of phenomena that cannot be fully elucidated by the means of logic, mathematics, praxeology, and the natural sciences, insofar as they cannot be explained by these sciences. It [understanding] must never contradict the teachings of these other branches of science [logic, science, mathematics and economics].” [11]

Since ‘understanding’ is the interpretation of historical events. It can never produce results that have to be accepted by all people. Even if two people fully agree that any particular set of data is correct, and their thinking fails to contradict the teachings of other sciences, they may still get different results in their ‘understanding’ of the data. They may all agree that factors ‘A’, ‘B’, and ‘C’ all had an impact upon historical event ‘E’. Nevertheless, they may still be at odds about the relevance of these factors to the actual event. As far as ‘understanding’ goes, aiming to attach relevance to each of the different factors is always at the mercy of subjective evaluation. These are not value judgments and they express no particular preference. They are simply relevance judgments. [12] As well as historians, psychologists and ethnologists also use the method of ‘specific understanding’ insofar as they deal with the interpretation of historical data when it comes to human behaviour. [13]

So ‘understanding’ is a method that handles the understanding of complex phenomena and predicts the future within complex systems. However, the data obtained from complex phenomena can neither prove nor disprove a theory. [14] In the natural sciences and within natural history, understanding is out of place. [15] The natural science method is a verification of constantly predictable relations via experimentation.

Is climate forecasting a natural science?

The first question which arises therefore becomes: What should we do with climate research? Should we do it with science? Or should we do it with predictions created using a method such as ‘understanding’? Let’s take a look and see what currently happens within climate research.

The direct effect of CO2 within a closed system, in terms of temperature effects, is known from ‘well-stablished physics’ based upon laboratory results determined over the last century or so. [16] As far as this laboratory knowledge is concerned, we can therefore speak of “hard” science. But the Earth is not a closed system. Firstly, there are people and animals that emit CO2, who we can label as aerobic ‘burning’ respirators, i.e. those who require oxygen [17]. Secondly, there are plants that operate via a countering process of combustion. They use photosynthesis to build up their physical structures from CO2, whilst employing the abundant energy of sunlight. They also emit oxygen as a byproduct.

NASA (the National Aeronautics and Space Administration) estimates that an increase in CO2 within the atmosphere would lead to increased plant growth. [18] Other factors affecting temperature and weather include solar activity, humidity, cloud cover, or precipitation. Thus, whilst it would be scientifically correct, within a laboratory experiment, to say that increased CO2 emission leads to an increase in temperature, a statement about a complex feedback system that constant relations cannot yet describe, is no longer a scientific statement. It is instead a prognosis in which a ‘method of understanding’ must be applied.

If there were constant relations in the climate, which came from the accumulation of measured weather averages, then we could accurately predict the weather over longer periods of time. But each of us knows, even if only from personal experience, that the weather can only be predicted over very short periods of time and not often particularly accurately. What the climate scientists of the IPCC (Intergovernmental Panel on Climate Change) do is to make relevance judgments, as historians do or even everyday people do in any assessment of the future. Since they’re unaware of constant relations, they can only provide relevance judgments. Climate scientists know that all the factors, such as temperature, CO2, cloud formation, solar activity, humidity, and so on, work together to produce the data that they later call ‘climate’ within their statistical frameworks. However, they remain unaware of the underlying constant relationships upon which these factors interact. We can only evaluate their judgements via their models.

Climate research can apply the methods of the natural science, insofar as it is possible to establish constant relations with regard to individual phenomena within laboratory experiments, but not with regard to the overall phenomenon of the Earth’s climate. This is because climate exists as a complex phenomenon containing many complex feedback loops. A climate model can never be verified via laboratory experiment. This uncertainty or deviation from the scientific verification method carried out via experiment, and the consequent reliability of the findings, is more or less frankly admitted:

“The Earth’s system is located in its complexity at the upper end of the spectrum in terms of nonlinearity and degrees of freedom.” [19]

So, it’s unsurprising that climate researchers constantly need to correct their models. In these models, they try to give different weightings to the various climate-relevant factors. These models are then examined to check if they can explain the past’s climate phenomena, or, if enough time has elapsed, to see if they could predict future climate phenomena. So far, this has proved unsuccessful, which the following quote demonstrates:

“We have not been able to observe, within reality, the rapid warming after the year 2000 that we do see in the models.”

This quote comes from the 39th issue of ‘Swiss World Week’, from Oxford University climate scientist Myles Allen, co-author of a study published in the journal ‘Nature Geoscience’.

The computer models will indeed be wrong, because basic climatic relationships within the atmosphere have either remained unknown or misunderstood. The scientists have now concluded that humanity may be able to emit almost four times more CO2 than previously thought, until the earth is supposedly heated by 1.5 degrees. While the climate skeptic party sees this as yet further evidence for climate alarmist failure, the study’s co-author, Pierre Friedlingstein, interprets its results as really good news. Without the slowdown, he says, the project to limit warming to only 1.5 degrees would have proved unrealistic.

However, as we stated earlier, even if climatologists are constantly improving their models, the data gained from complex phenomena can never either prove nor disprove a theory. [20] Even if their models were to get better, confirmed by even more measurable data – in the same way that Copernicus and Keppler applied a little science when they first described the orbit of the Earth, first in a circular form and then within an ellipse – the history of astronomy as a hard science only begins with Newton’s ‘Laws of Motion’. These Newtonian laws then withstood independent verification via rigorous laboratory experimentation. [21]

And so it becomes unsurprising that NASA, via its website, declares that the IPCC scientists remain unsure if human activity has directly led to a rise in global warming over the past 50 years. However, the scientists would claim the ‘probability’ for this at over 95%. [22] From this, it’s believed one can conclude in the future that human activity will be responsible for an increase in temperature, and that this increase – due to human behaviour – is modifiable. Specifically, the quantification of likelihood by the IPCC is described as follows:

“The likelihood of a result evaluates how likely that outcome is. It is given in a linguistically calibrated scale based on quantified probabilities. … This quantification can be based on calculations based on in-situ data, satellite data or simulations, or evaluations of expert interviews. ” [23]

Climate Forecasting is an interpretation of historical data

Now that we know that climate research uses the method of specific understanding, we can realise why no reliable predictions prove possible. And we know that understanding can never contradict the teachings of other knowledge fields  (such as logic, science, mathematics, and economics). Therefore, we can now also check whether the IPCC statement – that humans are responsible for the warming of the last 50 years, with a ‘probability of more than 95%’  – contradicts the teachings within other bona fide areas of science.

What do probability statements tell you?

The figure of 95% is an indication of a probability. Probabilities do provide us with knowledge of logic and mathematics. There are basically two types of probabilities. These are ‘Class Probability’ and ‘Case Probability’.

Class probability

In the case of class probability, we know everything about the behaviour of an entire event class in terms of the object of investigation. However, we know nothing about a specific, single event except that it belongs to that class. [24] We also know about event space, that is, we know about all the events that can happen. However, we’re unable to make a specific prediction about a single event. A well-known example is that of a roll of a dice. We know that the only throws possible go from one to six. However, you’re unable to categorically predict any specific outcome on a single roll. The only thing that you can say is that it will be some integer between one to six.

The probability calculus offers us a way of expressing this form of inadequate knowledge within symbols of mathematical terminology, without unfortunately either deepening or completing our knowledge of the phenomenon. It remains only a translation into the language of mathematics. The probability calculus translates into algebraic language, only that which we already know. [25] A number from one to six will be rolled from a dice throw, but we just don’t know what number it will be. Therefore, Ludwig von Mises stated that the great mathematician Blaise Pascal (1623-1662) would have done a great service to his contemporary, Chevalier de Méré (1607-1684), if he’d sent De Méré home after he’d come to Pascal with a dice game problem. Pascal should have frankly admitted that mathematics could not help De Méré with his dice game problem, even though it was described in mathematical symbols. The underlying problem is understood even before the mathematical symbols come into play. [26]

Not to be confused with class probability is ‘relative frequency’ . Unlike class probability, relative frequency is not the mathematical formulation of incomplete knowledge in terms of a phenomenon which we already theoretically know. Further, it is not the a priori behaviour of a whole class of events where we do not know anything about a single event. The only thing that we do know is that it is part of the class. Relative frequency is instead a record of data and a set of ratios. If you roll a dice a hundred times and then roll it 26 times within six separate trials, in mathematics the probability would fail to match the relative frequency of 26%. It would instead still always equal 1/6 (about 16.7%). [27] The relative abundance comes from the result of recording and evaluating historical data. It thus has nothing to do with class probability.

Individual case probability

In case-by-case probability, we know some of the influencing factors with regard to a specific event in the future. However, there are other factors that we do not know or whose effects we cannot know exactly. [28] A good example is when we want to predict the outcome of a particular football game. ‘Understanding’ is always based upon incomplete knowledge. You, dear readers, may know some of the influencing factors in the sport of football (individual player performance, whether it’s a home or away game, results from past games, and so on). But in the next game, you still cannot possibly know all the factors that will affect its final result or the relevance of any individual player’s ability. In advance of the game, one cannot possibly be sure that they’re completely certain about the relevance of any of the many different outcome drivers involved, or that they will neglect to take into account some of the factors that will indeed play a role. We’re often able to recognise these factors afterwards, with the blessing of hindsight (for instance, the referee had a ‘bad’ day).

Single case probability has nothing in common with class probability, except that both are forms of incomplete knowledge. In a case-by-case probability, a numeric expression is impossible. You’re unable to say that football team ‘A’ will win the game with a 95% probability because it just simply cannot be calculated. If someone says this, then he’s not really calculated the outcome, but instead is only using a metaphor. He might compare the chances of football team ‘A’ winning a particular game with the odds of winning a lottery game when he’s holding 95 of the 100 lots available. If he wants to win the next single draw, he might be of the opinion that a figure of 95% could tell him something substantial about the outcome of that draw. (That this is not the case, I hope does not need to be repeated.) [29]

Incidentally, even within the natural sciences it is forbidden to give a probability about the possible correctness of a hypothesis. Scientific hypotheses are always preliminary statements. [30] All that can be said about them is whether the hypotheses are consistent or inconsistent with both logical principles and experimental results. The hypotheses then prove to be either wrong or non-false (according to the current state of experience).

The 95% opinion of the IPCC

The IPCC’s 95% opinion [31] is not a class probability. As demonstrated above, this is because a mathematical class probability is a theoretical concept in which we’re aware of the behaviour of the whole class of events from the outset. However, we know nothing about the event in question, except that it is part of this class. The class probability is not a prognosis of the relative frequency and is certainly not a prediction of an individual case. With historical data, a probability cannot be formulated. At best, only a relative frequency can be determined.

Admittedly, given an infinite amount of data the relative frequency does eventually converge to the class probability. However, we can only know this probability if we already know the class chance beforehand. That, in turn, only occurs in the case when we already know everything about the previous behaviour of a whole class of events. If we lack this knowledge, the relative frequency can at best indicate a trend, but not a probability . History teaches us, however, that trends that have taken place in the past can change, do change, change often, change almost always, and even change within the immediate future. [32]

The IPCC’s 95% statement is an indication of case-by-case probability based upon measured data. The IPCC is aware of some influencing factors in terms of climate change. However, there are other factors that it does not know about or whose effects it remains unaware of. [33] It has already been shown above, that in the example of a case-by-case probability, the use of numerical expressions is forbidden. [34] So the 95% statement is not a mathematical expression of a class probability, but a weakly intuitive analogy or simply a metaphor. The IPCC claims that it’s as sure about its thesis of man-made climate change as a lottery participant would be who owned 95% of the available lots. But again, as we have already shown above, even in the case of class probability, this figure of 95% cannot tell us anything substantive about any individual case under consideration, except that we’re not completely sure if the predicted outcome is going to come about.

Incidentally, let’s consider the legal term that someone is “almost certainly” convinced of a certain fact. But a probability bordering on certainty means that there can no longer be any “reasonable doubt”. However, if someone has purchased 95 out of 100 lots, then there still remains a reasonable doubt as to whether the winning lot is included within their held collection. To stay within the frame of the metaphor, if judges were satisfied with a 95% certainty, one in 20 convicts would be wrongfully imprisoned. Would you also describe an airline as “safe” in which a plane crash awaited one in 20 of its scheduled flights? [35]

As a result of our preceding investigation, we can say that climatologists – with regard to climate history, prognoses and models – do not use the methods of natural science but the method of understanding complex phenomena. [36] They cannot arrive at a safe prediction . And we know that these scientists, with their 95% certainty are blatantly contradicting the laws of thought. Their application of a mathematical expression is wrong, because it is not a case of class probability. In the realm of understanding, the statement remains false, because a case-by-case probability cannot logically allow any numerical expressions; it’s just impossible to calculate one.

Climate fighters and climate sinners are not scientific terms – they serve the struggle of political opinion

By assigning climate science to understanding, it’s now becoming clear why such a heated dispute prevails over man-made climate change. It uses unscientific terms that come from the realm of morality, such as ‘climate deniers’ or ‘climate offenders’. Have you ever encountered a scientific discourse where Einstein was referred to as a ‘time denier’ or a ‘gravitational doubter’? On one side, the climate alarmists try to block any further scientific discussion, whereas the ‘climate skeptics’ try to constantly falsify the assumptions of the climate alarmists.

In terms of understanding, there can be no single correct statement

Now that we know that these predictions of the climate scientists belong within the arena of specific understanding, we can calmly acknowledge that two climate scientists can fully agree upon the data and the relevance they attach to certain drivers. Also, they need contradict neither natural laws nor logical mathematical rules. And yet, they can still arrive at different forecasts. Similarly, one and the same climate scientist can arrive at different results at different times, without contradicting themselves.

The economic forecasts of the IPCC

As far as climate data is concerned, we have ensured clarity. But what about economic forecasts? In a report published on the IPCC website, the authors discuss the effects of climate change on gross domestic product. [37] The Star report by British scientist Nicolas Stern, basically stated that climate change will cost around 5% of global GDP annually, whereas it set the cost of countermeasures at 2% per annum. [38]

The British science journalist Matt Ridley criticised Stern’s report because Stern employed very low discount rates when calculating the effects of climate change. This resulted in very high future damages and costs. In addition, Ridley made the critique that the Stern Report assumes a very high standard of living for all of our future grandchildren. The IPCC scenarios also suggest that our future descendants will have enjoyed a great deal of economic growth, so much so that in the year 2100 their wealth could be four to 18 times higher, on average, than it is for us today. [39] Of course, these rosy economic growth forecasts also affect the forecasts concerning the costs of damage and the levels of future CO2 emissions.

Who then is right? Nicolas Stern and the IPCC or Matt Ridley? To discover the answer, let’s start with a question. What is GDP? The Federal Statistical Office of Germany thinks it knows:

“Gross domestic product (GDP) is a measure of the economic performance of an economy over a period of time. It measures the value of domestically produced goods and services (value added), provided they are not used as inputs for the production of other goods and services.”

Value cannot be measured

So GDP supposedly measures the value of goods and services within an economy. But value cannot be measured. Mises writes:

“Value is not intrinsic, value is not in things. It is in us; it is the way humans react to the conditions of the environment. [40] … Values ​​come from whether we prefer ‘A’ over ‘B’. … Just as there is no standard or measure of sexual love, friendship or sympathy or aesthetic pleasure, there is no measurement of the value of commodities. … There is no way to create a unit of value. … In a market economy there are money prices. Economic accounting is a reckoning in the form of monetary prices. The different amounts of goods and services enter into this economic calculation with the money prices for which they can be bought and sold on the market.” [41]

But if it’s impossible to measure value, can we measure GDP? Mises continues:

“It is possible to express the sum of the income or property of several people in monetary terms. But it is a nonsense to calculate a national income or a national wealth. As soon as we begin to make considerations that are alien to a human being who is acting within the market economy, monetary accounting does not help us any further. … If in a business calculation a stock of potatoes is valued at $100, the idea behind it is that you can sell or replace that stock for that price. … But what is the meaning of a calculation of national wealth? What is the meaning of the result of the calculation? What must be included in such a calculation and what must be left out? Would it be wrong or right, to incorporate the “value” of a country’s climate or the innate and acquired abilities of its inhabitants? The merchant can exchange his goods for money, but a nation cannot. The money terms used in trade and economic accounting are monetary prices, that is, exchange rates between money and other goods and services. Prices are not measured in money; Prices are made up of money. Prices are either prices of the past or expected prices in the future. A price is necessarily a historical data point, either in the past or in the future. There is nothing in a price that would allow it to be used for the measurement of physical or chemical phenomena.” [42]

So it makes no sense to add up your fortune and that of your neighbours, calculated in cash, if you do not intend to act together in the marketplace. It is impossible to measure wealth . An Italian may like his mild climate and his admirable cuisine very much indeed. But how do you incorporate this into an economic calculation? Of course, most people will agree that prosperity is greater today than it was 100 years ago. There are more capital goods now (factories, technical equipment, mines, power stations, etc.). Many people also have much more in the way of consumer goods (cars, apartments, groceries, hairdressers etc.) than they possessed in the past. Alone, they cannot numerically increase this prosperity measure. Different people rate economic progress differently. For example, a retired organic farmer may find it regrettable if a new highway is built within his locality, whereas the employed commuter who lives nearby is overjoyed to use this new highway and certainly sees it as a value. In addition, one and the same person can also rate the same object differently at different times.

The next term the Federal Statistical Office provides us with is this; when describing GDP, we encounter the concept of a national economy. But an economic ‘entity’ that you can point at is merely a theoretical fiction that contradicts a central doctrine of economics:

“The market economy as such does not respect political boundaries. Your area is the world. [43] … It is not “America” ​​who buys champagne from “France”. It is always an individual American who buys it from an individual Frenchman. … As long as there is some room for manoeuvre for individuals, and as long as there is private property and trade in goods and services, there is no economy. Only when there is complete government control over the choices of individuals is there an economy as a real entity.” [44]

Price changes cannot be concluded on purchasing power changes

Another problem is that when it comes to GDP forecasts, scientists look at the purchasing power of money. Their calculations are adjusted for inflation or purchasing power. But that alone is economically impossible:

“All methods proposed to measure the changes in the purchasing power of a monetary unit are based, more or less unintentionally, on the illusory imagination of an immortal and immutable being, who by means of an unalterable standard determines the amount of satisfaction that each monetary unit brings to him. … Only if people always rate the same things the same, could we see price changes as an expression of the change in the purchasing power of money. ” [45]

So, if the IPCC or the Stern Report calculates the cost of climate change in terms of what effects there will be to future damage, or to future incomes, or to future aggregates of GDP, we can say that they contradict the lessons learned by these calculations. Economics cannot measure value, there is no single economic entity in the economic sense, and so therefore it is absurd to aggregate GDPs to calculate a single world economic performance. The calculations of the IPCC should therefore be described as false because they contradict the teachings of economics. Of course, one could estimate from today’s market prices, within in the context of understanding, what the cost of measures could be to prevent any anticipated future damage. One could also estimate what the market prices would be of anticipated future damages or those measures to prevent them. However, such monetary prices of damage, and the measures to prevent them, cannot be calculated economically. Purchasing power itself cannot be measured. The statement of damage and costs in relation to a GDP cannot make sense if the term GDP itself makes no sense.

The IPCC assumptions contradict the doctrines of economics and thought laws

To sum up, basing themselves upon historical data and known scientific relations, the scientists of the IPCC suggest that humans have contributed to climate change by emitting CO2. But they’re not certain. They also feel that damage can be caused by climate change. However, they cannot calculate the value ​​humans will attach to these damages, They also believe that this damage, if it remains unprevented, could be mitigated. Surely they cannot be certain here either, if they’re already unsure about whether humans have actually contributed to the cause of climate change? And if scientists propose compulsory levies and regulation, i.e. state prohibitions and regulations to prevent or to mitigate the damage considered as possible and preventable, this becomes an ethical evaluation, which is of course unscientific. It does though perhaps tell us something about the attitudes of these scientists to the desirability of coercive measures.

Compulsory levies and regulation against citizens are unjustified on the basis of the assumptions of the IPCC scientists

So how to deal with this problem? The economist Murray Rothbard (1926 – 1995) said this: [46]

“If one party causes harm to another through an act [such as the emission of CO2], and if this can be proven to be probable in a court or within arbitration, then the injured party would have defence and compensation claims. But there should be no reasonable doubt that the behaviour of the offender has caused the damage. However, if all reasonable doubt cannot be dispelled, it is far better not to condemn a wrongdoer instead of intentionally inflicting damage upon someone who is innocent. The principle of the Hippocratic Oath: ‘at least do no harm to anyone’ must also apply to anyone who applies to or who enforces the law.”

However, it’s far from the case that the IPCC or NASA can almost certainly detect man-made climate change. The statement of a numerical probability contradicts laws of thought. The metaphorically given 95% figure should not serve as proof of causality in any judicial procedure. It certainly should not come to a question of whether reasonable doubts remain, because the burden of evidence already admits to the statement that there are indeed weighty doubts.

The calculation of the expected level of damage and the costs of damage control could therefore have saved the scientists of the IPCC. But since these calculations have been carried out, we can say that they contradict the laws of economics and are therefore wrong.

The results of climate research can therefore not justify the taxation of citizens. To avoid harm to the citizens, the existing taxes and subsidies, which were justified by man-made climate change, should be abolished immediately. And under no circumstances can tax increases, new taxes, or any other new subsidies be justified.