The difference jumps to my eye, but what does it mean?

My editorial

I hope I am on the right track with that idea that the maturing of markets can be represented as incremental change in the density of population. This is what I came up with yesterday, in my research update in French (see ‘Le mûrissement progressif du marché, ça promet’). I am still trying to sort it out, intellectually. This is one of those things, which just seem to work but you don’t exactly know how they do it. I think I need some time and some writing in order to develop a nice, well-rounded, intellectual crystallization of that concept. It all started, I think, as I multiplied tests on different quantitative models to explain incremental changes in the value of those two variables I am currently interested in: the percentage of renewable energy in the primary production of electricity ( ), and the percentage of renewables in the final consumption of energy ( ).

With the software I have, that Wizard for MacOS – and this is really not heavy artillery as statistical software comes – testing models sums up to quick clicking. Setting up and testing a model – or an equation – with that tool is much faster than my writing about it. This is both the blessing and the curse of modern technology: it does things much faster than we can wrap our mind around things. In order to understand fully this idea that I came up with yesterday, I need to reconstruct, more or less, the train of my clicking. That should help me in reconstructing the train of my thinking. So, yesterday, I was trying to develop, once again, on that idea of the Wasun, or virtual currency connected to the market of renewable energies. I assumed that empirical exploration of the question would consist in taking the same equations I have been serving you on my blog for the last few weeks, and inserting the supply of money as one more explanatory variable on the right side in those equations. It kind of worked, but just kind of: adding the supply of money, as a percentage of the GDP, to a model explaining the percentage of renewables in the final consumption of energy, for instance, added some explanatory power to that model, i.e. it pumped the R2 coefficient of determination up. Still, the correlation attached to the supply of money, in that model, did not seem very robust. With a p-value like 0,3 or 0,4 – depending on the exact version of the equation I was testing – it turned out that I have like 30 or 40% of probability that I can have any percentage of renewable energies with a given velocity of money. That p-value is the probability of the null hypothesis, i.e. of no correlation whatsoever between variables.

Interestingly, I had the same problem with a structural variable I was using as well: the density of population. I routinely use the density of population as a quantitative estimator of difference between social structures. I have that deeply rooted intuition that societies displaying noticeable differences in their densities of population are very different in other respects as well. Being around in a certain number in a given territory, and thus having, on average, a given surface of that territory per person, is, for me, a fundamental trait of any society. Fundamental or not, it behaved in those equations of mine in the same way the supply of money did: it added to the coefficient of determination R2, but it refused to establish robust correlations. Just for you, my readers, to understand the position I was in, as a researcher: imagine that you discover some kind of super cool spice, which can radically improve the taste of a sauce. You know it does, but you have one tiny little problem: you don’t know how much of that spice, exactly, you should add to the sauce, and you know that if you add too much or too little, the sauce will taste much worse. Imagined that? Good. Now, imagine you have two such spices, in the same recipe. Bit of a cooking challenge, isn’t it?

What you can do, and what great cooks allegedly do, is to prepare a few alternative sauces, each with the same recipe, but with a different, and precisely defined amount of the spice under investigation. As you taste each of those alternative sauces, you can discover the right amount of spice to add. If you are really good at it, you can even discover the gradient of taste, i.e. the incremental change in taste that has been brought by a given incremental change in the quantity of one particular ingredient. In quantitative research, we call it ‘control variable’: instead of putting a variable right in the equation, we keep it out, we select different subsets of empirical data, each characterized by a different class of value in this particular variable, and we test the equation, without the variable in question, in those different subsets. The mathematical idea behind this approach is that we never know for sure whether our way of counting and measuring things is accurate and adequate to the changes and differences we can observe in those things. Take distance, for example: sometimes it is better to use kilometres, but sometimes even a centimetre it too much. Sometimes, small incremental changes in a measurable phenomenon induce too much complexity for us to crystallize any intelligible thought about it. In statistics, it manifests as a relatively high p-value, or the probability of the null hypothesis. Taking that complexity out of the equation and simplify it into a few big chunks of reality can help our understanding.

Anyway, I had two spices: the density of population, and the supply of money. I had to take one of them out of the equation and treat as control variable. As I am investigating the role of monetary systems in all that business of renewable energies, it seemed just stupid to take it out of the equation. Mind you: it seemed, which does not mean it was. There is a huge difference between seeming to be stupid and being really stupid. Anyway, I decided to keep the supply of money in, whilst taking the density of population out and just controlling for it, i.e. testing the equation in different classes of said density. For a reason that I ignore, when I ask my statistical software to define classes in a control variable, it makes sextiles (spelled jointly!), i.e. it divides the whole sample into six subsets of roughly the same size, 1577 or 1578 observations each in the case of the actual database I am using in that research. Why six? Dunno… Why not, after all?

So I had those sextiles in the density of population, and I had my equation, regarding the percentage of renewable energies in the final consumption of energy, and I had that velocity of money in it, and I tested inside each sextile. Interesting things happened. In the least dense populations, the equation barely had any explanatory power at all. As my equation was climbing the ladder of density in population, it gained explanatory power as well. Still, there is an interval of density, where that explanatory power fell again, just to soar in the densest populations. Those changes in the coefficient of determination R2 were accompanied by visible changes in the sign and the magnitude of the regression coefficient attached to the velocity of money. The same happened in other explanatory variables as well. My equation, as I was trying to wrap my mind around all that, works differently in different types of populations, regarding their density. It works the most logically, in economic terms, in the densest populations. The percentage of renewable energy in the final basket of consumption depends nicely and positively on the accumulation of production factors and on the supply of money. The more developed the local economic system, the better are the chances of going greener and greener in that energy mix.

In economics, demographic variables tend to be considered as a rich and weird cousin. The cousin is rich, so they cannot be completely ignored, but the cousin is kind of a weirdo as well, not really the kind you would invite risk-free to a wedding, so we don’t really invite them a lot. This nice metaphor sums up to saying that I tried to find a purely economic interpretation for those changes I observed when controlling for the density of population. My roughest guess was that money matters the most when we have really a lot of people around us and a lot of transactions to make (or avoid). With hardly any people around me (around is another simplification here, it can be around via Internet), money tends to have less importance. That’s logical. In other words, the velocity of money depends on the degree of development in the market we consider. The more developed a market is, the more transactions are there to finance, and the more money we need in the system to make that market work. Right, this works for any market, regardless whether we are talking about long-range missiles, refrigerators or spices. Now, how does it matter for this particular market, the market of energy? Please, notice: I used the ‘how?’ question instead of ‘why?’. Final consumption of energy is a lifestyle and a social structure doing its job. If the factors determining the percentage of renewable energies in said final consumption work differently in different classes of density in the population, those classes probably correspond to different lifestyles and different types of local social structures.

I imagined a local community, where people progressively transition towards the idea of renewable energies. In the beginning, there are just a few enthusiasts, who, with time, turn into a few hundred, then a few thousands and so on. From then on, I unhinged my mind a bit. I equalled the local community at the starting point, when nobody gives a s*** about green energy, as a virgin land. As new settlers come, new social relations emerge, and new opportunities to transact and pay turn up. Each person, who starts actively to use renewable energies, is like a pioneering settler coming to that virgin land. The emergence of a new market, like that of renewable energy, in an initially indifferent population, is akin to a growing density in a population of settlers. So, I further speculated, the nascence and development of a new market can be represented as a growing density in the population of customers. I know: at this point, it could be really hard to follow me. I even have trouble following myself. After all, if there are like 150 people per square kilometre in a population, according to my database, there are just them in that square kilometre, and no one else. It is not like they are here, those 150 pioneers, and a few hundred others, who are there, but remain kind of passive. Here, you have an example of the kind of mindfuck a researcher deals all the time. Data exploration is great, but data tends to have sharp edges. There is a difference, regarding the role of money in going green in our energies, between a population of 100 per km2 and a population of 5000 per km2. The difference is there, it jumps to my eye, but what does it mean? How does it work? My general intuition is that the density of population, as control variable, controls for the intensity of social interactions (i.e. interactions per unit of time). The degree of maturity in a market is the closest economic meaning I can associate with that intensity of interactions, but there could be something else.

It warms my heart to know I am not totally insane

My editorial

This is a rainy morning in Amplepuis, France, where I am staying until tomorrow. I am meditating, which means I am thinking without pretending to think anything particularly clever. Just basic, general flow of thinking, enough not to suck my thumb, sitting in a corner. I am mentally reviewing that report by Sebastiano Rwengabo (Rwengabo 2017[1]), which I commented on in my last two updates, and in some strange way I keep turning in returning in my mind that formula of multiple probability by Thomas Bayes (Bayes, Price 1763[2]), namely that if I want more than one success over n trials, at some uncertain, and therefore interesting action, I have always more than one way to have those p successes over n trials. Thomas Bayes originally equated that ‘more than one’ to (pq)/q!, where q is the tolerable number of failures. You can try by yourself: as long as you want more than one success, your (pq)/q! is always greater than one. There is always more than one way to have more than one success. Interesting intuition.

I am digging into the topic of local power systems based on renewable energies, possibly connected to a local cryptocurrency. I want to prepare something like a business plan for that idea. I want to know how many ways of being successful in this type of endeavour are reported in the literature. I start with a leaflet I found and archived on my website under this link . It is entitled ‘100% – RES communities’. As usually, I start at the end, and the end is sub-headed ‘Stay in The Game’. Good. If it is important to stay in the game, then logically it is important not to drop off, which, in turn, means that dropping off is an observable end to local efforts at going 100% renewable. One of the ways to stay in the game consists in joining other people who want to. There is a network, the Global Covenant of Mayors ( ), which currently unites 7 477 cities with almost 685 million people living in them and which has been created quite recently by the merger of the Covenant of Mayors, mentioned in that ‘100% – RES communities’ leaflet, with the Compact of Mayors, in June, 2016. Having more than one success in going 100% means, thus, staying in the game with others, in networks, and those networks tend to merge and create even bigger networks. I have a nice conditional probability, here: my probability of successfully going 100% green, as a local community, depends on the probability we manage to stick to our commitments, which, in turn, depends on our ability to join a network of other communities with similar goals. There are other networks, besides the Covenant of Mayors, such as the RES League ( ), 100% RES Communities ( ), or the French RURENER ( ).

The next thing, which apparently helps to stay in the game is a SEAP, or Sustainable Energy Action Plan. As I am writing this paragraph, I am browsing the Internet in the search of details about this approach, and I am simultaneously reading that leaflet. SEAP seems to be a general line of approach, which sums up to assuming that we can induce only as much change as we can really plan, i.e. that we can translate into real action on a given date and in a given place. If I am grasping well the concept, it means that whenever a ‘we will do it somehow’ pokes its head out of our action plan, it indicates we have no proper SEAP. I like the approach, I have experienced its soundness in other areas of life: we can usually achieve more than we think we can, but we need to understand very precisely what is the path to cover, step by step. Here, the coin drops: if we need a good SEAP, it is important to tap into other people’s experience, whence the point of forming networks. Being maybe a bit less ambitious, but more realistic and more in touch with what the local community really can do, is apparently helpful in going 100% green.

That was a leaflet, now I take a scientific paper: “Exploring residents’ willingness to pay for renewable energy supply: Evidences from an Italian case study” by Grilli et al.[3] , an unpublished working paper accessible via the Social Sciences Research Network . The paper explored the attitudes of people living in the Gesso and Vermenagna valleys, towards the prospect of paying higher energy bills as long as those bills will be 100% green. The case study suggests an average acceptance for a 5,1€, or 13% increase in the monthly energy bill. Knowledge about renewable energies seems to be a key factor in shaping those attitudes. Good, so I have another nice, conditional probability: having more than one success in going 100% green locally depends on the local acceptance of higher energy bills, which, in turn, depends on the general awareness of the population involved.

I move forward along that financial path, and I am having a look at a published article, entitled “Is energy efficiency capitalized into home prices? Evidence from three US cities.“, by Walls et al.[4] . Margaret Walls and the team of associated researchers found a positive impact of ‘green certification’ of residential properties upon their market price, with a premium ranging from 2 to 8%. Still, this premium remains strongly local, thus largely idiosyncratic. Staying in this path of thinking, i.e. thinking about money whilst thinking about grand green initiatives, I am having a look at an article by Patrick Hartmann and Vanessa Apaolaza-Ibáñez as for the consumers’ attitude towards the so-called ‘green energy brands’[5]. In this case, the most interesting thing is the methodology, as the results are quite tentative, based on a total of 726 street interviews in six towns and villages in northern Spain. The methodology is based on a set of assumed benefits that an individual can derive from purchasing energy from suppliers certified as 100% green in their process of generation. There is a nice piece of fine reasoning from the part of Patrick Hartmann and Vanessa Apaolaza-Ibáñez. They hypothesise that altruistic environmental concerns and their satisfaction are just one among the many psychological factors affecting the decision of purchasing renewable energy. There is a bunch of egoistic factors, slightly in the lines of Thorstein Veblen’s theory of the leisure class: consuming green energy can provide something described as ‘warm glow’, or personal satisfaction derived from experiencing a subjectively positive impact on the social and natural environment, as well as from the social recognition of that impact that we experience as a feedback from other people. In other words, if a person can expect interactions like ‘Oh! You are buying that 100% green energy? Fantastic! You are such a precious member of the community!’. Let’s face it: each of us would like to be praised like that, from time to time. On the top of that, the purchase may be further affected by the general reputation of the given brand, and by individual attitudes towards experiencing the contact with nature. Whilst tentative, the results of those interviews suggest, quite interestingly that the general attitude towards the suppliers of green energy is strongly influenced by that personal, individual experience of nature in general.

Still following the money, but moving from the small money spent by consumer towards the big money held by banks, I am browsing through an unpublished paper by Karen Wendt, from MODUL University in Vienna[6]. This particular paper is precious, from my point of view, mostly because of the interesting stylized facts it presents. In science, stylized facts are facts that we can express in a graph, but we cannot exactly explain why the graph looks the way it looks. So, Karen Wendt lets me learn, for example, that a large part of the known reserves in fossil fuels, probably between 60 and 80% of them, must stay nicely in the ground if we are to meet the 2°C limit of temperature jump. These reserves are accounted for as assets in the balance sheet of your average Exxon Mobil. If they are to stay where they are, they will have to be kicked the hell out of those balance sheets, and that’s gonna hurt. The same is valid for carbon-intensive infrastructure, like chains of petrol stations or oil-refining plants. If we turn green, all that stuff will have to be written off someone’s equity, and this, once again, is likely to make some people nervous. It shows that if we really want to go green, we really could do with some capitalistic mechanism of transition, which would allow, sadly but realistically, to switch relatively smoothly from a carbon-intensive balance sheet, with the corresponding capital profits financing the corresponding private islands, to a balance sheet based on renewable energies. It warms my heart, those observations from Karen Wendt, as it suggests I am not totally insane when I think about monetary systems specifically oriented on giving market value to green energy.

[1] Rwengabo, S., 2017, Efficiency, Sustainability, and Exit Strategy in the Oil and Gas Sector: Lessons from Ecuador for Uganda, ACODE Policy Research Series No.81, 2017, Kampala, ACODE, ISBN: 978-9970-567-01-0

[2] Mr. Bayes, and Mr Price. “An essay towards solving a problem in the doctrine of chances. by the late rev. mr. bayes, frs communicated by mr. price, in a letter to john canton, amfrs.” Philosophical Transactions (1683-1775) (1763): 370-418

[3] Grilli, G., Balest, J., Garengani, G., Paletto, A., 2015, Exploring residents’ willingness to pay for renewable energy supply: Evidences from an Italian case study,

[4] Walls, M., Palmer, K., Geranden, T, Xian Bak, 2017, Is energy efficiency capitalized into home prices? Evidence from three US cities, Journal of Environmental Economics and Management vol. 82 (2017), pp. 104-124

[5] Hartmann, P., Apaolaza-Ibáñez, V., 2012, Consumer attitude and purchase intention toward green energy brands: The roles of psychological benefits and environmental concern, Journal of Business Research, vol. 65.9 (2012), pp. 1254-1263

[6] Wendt, K., 2016, Decarbonizing Finance – Recent Developments and the Challenge Ahead, Available at SSRN: