This last update in French, namely Ma petite turbine éolienne à l’axe vertical, it stirred something interesting in my mind. As my internal happy bulldog is sniffing around that patent application no. EP 3 214 303 A1, questions take shape. How can this particular technology interact with its social environment?
How can any invention interact with its environment? Surprisingly enough, inventions behave very much akin to living organisms, in that respect: the main way they interact with their environment is breeding. Cross-breeding too, as I think of it. One invention seldom is a game changer. As soon as it starts multiplying, things start happening seriously. Let’s see, then, what’s up in the multiplying department. Any kind of multiplying results in a multiple, i.e. in a certain number of something. Mind you, if the incriminated multiplying goes on like really dynamically, it could be an uncertain number of something. Whatever. I am developing on that data you can already find in the Excel file you can see and download from the archive of my blog: I used https://patents.google.comonce again and I sifted out all those patent applications, which pertain to wind turbines with vertical axis, just as the one in that patent application no. EP 3 214 303 A1. I did for the same three big patent offices: the European Patent Office (EPO), the U.S. Patent and Trademark Office (USPTO), and finally for the patent office of the People’s Republic of China (just ‘China’).
Table 1, below, shows the results of that little rummaging I did. This is one of those rare times when I am really puzzled by the numbers I find. You can notice that in all the three patent offices under scrutiny, patent applications pertaining to wind turbines with vertical axis make a very consistent percentage in the total stream of inventions filed for patenting under the general category of ‘wind turbine’. Especially with the EPO and the USPTO, that percentage is solid like a tax rate. With the Chinese patent office, it is a clement, descending tax rate.
Table 1 – Patent applications pertaining to wind turbines with vertical axis
|Year||Number of patent applications with EPO||% share in the total of EPO’s ‘wind turbine’ patent applications||Number of patent applications with USPTO||% share in the total of USPTO’s ‘wind turbine’ patent applications||Number of patent applications in China||% share in the total of Chinese ‘wind turbine’ patent applications|
I am definitely surprised with those results. Let’s rephrase it, to understand better the phenomenon hiding behind the numbers: whatever the actual number of patent application filed under the general category of ‘wind turbine’, those pertaining to wind turbines with vertical axis make around 40% in Europe and in the United States, whilst consistently descending from around 30% to some 20% in China. Here, we can see one of those phenomena that remain structurally stable no matter what is their actual size.
This is the moment when the teacher in me awakens and wants to do some lecturing about the foundations of the scientific method. In my previous updates, I gave you a glimpse of two distinct types of logic in interpreting numerical data: the frontier plot (At the frontier, with my numbers), and that of an indifference curve (Good hypotheses are simple). Now, I am going to use the occasion – namely that of explaining how a technology of wind turbine can interact with its social environment – to expose the fundamentals of studying time series.
The data in Table 1, above, shows, in general, how frequently people apply for patenting technologies connected to wind turbines with vertical axis. The ‘how frequently?’ further decomposes into ‘how many times in a unit of time?’, and ‘how many times out of a broader number?’, and these two shades of ‘how frequently?’ have different meanings. When I wonder (and measure) how many times a given thing is being done in a unit of time, it is like the social size of that thing. Big social things are those done a lot of times, like over one year, and small social things are performed much lesser a number of times.
Columns [b], [d], and [f] in Table 1express this approach to the social phenomenon labelled ‘invention in wind turbines with vertical axis’. They inform about the size of the phenomenon. In Europe, and in the United States, the size in question had been growing since 2001 until 2014, when it reached a temporary peak, which seems to have become sort of less protruding in 2016 and 2017. In China, the size of the thing named ‘invention in wind turbines with vertical axis’ has been changing differently: it is continuous growth since 2001 all the way through 2017.
Now, I pass to studying the ‘how many times out of a broader number?’ shade of ‘how frequently?’. Columns [c], [e], and [g] in Table 1 give me some insight in that respect. Those percentages are proportions, and thus they are measures of structure rather than size. Values in columns [c], [e] are remarkably recurrent, as if pegged down by some invisible hand. Those structures, in Europe and in the United States, are really stable. Whatever the size of the phenomenon labelled ‘invention in wind turbines with vertical axis’, its proportion to the broader phenomenon named ‘invention in wind turbines’remains fairly constant.
What does it mean? Imagine a human body. When it grows in size, do its internal proportions remain constant? Sometimes they do, but really just sometimes. When a child grows into an adult, many morphological proportions change, like the proportion ‘waist circumference to the length of the torso’. When an adult grows into more corpulent an adult (happens frequently), it changes, too. If a proportion is to remain stable over many different sizes, it has to be really, bloody fundamental.
You could raise a legitimate objection, here. After all, those numbers I quote in Table 1 come from semantic filtering at https://patents.google.com. There can be a cartload of semantic coincidences, for example an invention pertaining to wind turbines with horizontal axis might be mentioning the vertical axis of rotation. Windmills with horizontal axis can do that, i.e. turn on their vertical axis to catch the best wind. Already those Dutch oldies from the 17thcentury were able to perform that trick. It is possible that some of the patent applications accounted for in Table 1 contain this semantic bias. Still, it would be a remarkably consistent bias, occurring over and over again in the space of many years.
China presents a different picture. As the size of the phenomenon labelled ‘invention in wind turbines with vertical axis’, its proportion to the broader phenomenon named ‘invention in wind turbines’shrinks. This particular structure changes as the size of the phenomenon changes. Still, the change is far from random: it follows a relatively smooth, downwards path.
We have a first approach of how this particular technology – wind turbines with vertical axis – can work with its social environment. It can stay in some sort of homeostasis with other, similar technologies, or it can sort of slowly retreat to the benefit of those other, similar ones. Let’s go one step further and connect it to the share of renewable energy in the overall, final consumption of energy, as published by the World Bank. In Table 2, below, you can find the data pertaining to our three markets under scrutiny.
|Share of renewable energy in the total consumption of energy|
|Year||European Union||United States||China|
OK, now I have two sets of variables: one about those inventions pertaining to wind turbines with vertical axis, and another one about the share of renewables in the overall energy consumption. Both are presented in the form of time series. What I can do with them both is to check for their mutual correlation. Among the many possible coefficients of correlation, I go for a classic: the Pearson correlation coefficient. I start checking that correlation in pairs of time series, for each geographical region separately. Table 3, below, presents the results, which are a bit puzzling. Before discussing them, let me introduce to the little presentational trick I am doing. In that table, I ascribed symbols to lines – [A] and [B] – and to columns, as consecutive roman numbers from [I] to [III]. It is just for the sake of convenience. In order not to repeat, each time, that long name ‘correlation between … and …’, I can just say ‘correlation [I][A]’ and everybody knows I am talking about the coefficient in the left upper case of the matrix etc. So, armed with that little editorial subterfuge, I develop my interpretation further below, underneath Table 3.
Table 3 – Matrix of Pearson correlation coefficients between the incidence of patent applications pertaining to wind turbines with vertical axis, and the share of renewable energy in the total consumption of energy
|Share of renewable energy in the total consumption of energy|
|European Union||United States||China|
|Number of patent applications pertaining to wind turbine with vertical axis||[A]||0,945423082||0,986363487||-0,776361916|
|% share in the total of ‘wind turbine’ patent applications||[B]||0,288279238||0,321381613||0,722853177|
I start with correlations [I][A] and [II][A]. They are high, and, I you want my opinion, they are surprisingly high. I didn’t expect such high values. They mean that the respective pairs of variables determine each other’s variance like around 90%, and this is very nearly a perfect congruence. It is as if each kilowatt hour of renewable energy literally dragged an invention about wind turbines with vertical axis out of the void of wannabe ideas, and vice versa.
Now, correlations [III][A], [I][B], and [II][B] do not really make me gasp for air. Looking at the numbers being correlated, these coefficients come as sort of logical. On the other hand, the last one, the [III][B] once again surprises me with its high positive value.
Good, time to come to the ad rem, as one of my professors in the law studies used to say. I asked a question: how can this particular technology, namely those vertical wind rotors, interact with its social environment? My first conclusion is that it interacts differently, depending on where it is actually interacting. In Europe and in the United States it interacts in a really strange, extremely patterned manner, as if each invention pertaining to those vertical Aeolian rotors had strings attached to it, and as if those strings had their other extremity anchored in a different invention in the wind energy, and to a kilowatt hour of renewables. Once again, believe, such strong, stable, structural patterns happen really seldom, particularly between so different phenomena. It looks almost like a Cartesian mechanism, with cogwheels moving each other. In China, that interaction is different, sort of less rigid and less determinist. There is some play in the game, over there.
All that little research about wind turbines with vertical axis turns weird. This is another of those empirical observations, which look extremely interesting, whilst I wish I could phrase out what they mean. I can cautiously formulate a working hypothesis, that the technologies of wind turbines make systems of different coherence according to the geographical region of the world, and that in some regions, those systems can be extremely determinist. Still, as scientific standards come, this is more a sketch of a hypothesis, rather than truly rigorous stuff.
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