Month: June 2019

La ville – éponge

Mon éditorial sur You Tube

Je développe sur le concept que je viens d’esquisser dans ma dernière mise à jour en anglais : « Another idea – urban wetlands ». C’est un concept d’entreprise et concept environnementaliste en même temps : un réseau d’étangs et des cours d’eau qui serviraient à la fois comme réserve d’eau et l’emplacement pour un réseau des petites turbines hydrauliques.  Oui, je sais, je n’en ai pas encore fini avec EneFin, le concept financier. Je compte de l’appliquer ici de façon créative. Point de vue mécanique des liquides, l’esquisse de l’idée est la suivante. On a besoin d’une rivière qui sera la source primaire d’eau pour le système. Dans les environs immédiats de cette rivière nous construisons un réseau des cours d’eau et d’étangs. Les étangs jouent le rôle des réservoirs naturels d’eau. Ils collectent un certain surplus d’eau de pluie conduite par la rivière. De cette façon, l’eau de pluie est mise en réserve.

Les cours d’eau connectent la rivière avec les étangs ainsi que les étangs entre eux. Les cours d’eau ont une double fonction. D’une part, ils sont l’emplacement à proprement dit des petites turbines hydrauliques qui produisent l’électricité. D’autre part, ils assurent de la circulation d’eau dans le système afin de minimiser la putréfaction de débris organiques dans les étangs et par la même façon de minimiser l’émission de méthane. Le tout est complété par les cultures d’arbres et arbustes. Ces grosses plantes vertes ont une double fonction aussi. D’une part, leurs racines servent de stabilisateurs pour le sol du système, qui en raison de l’abondance d’eau peut avoir tendance à bouger. D’autre part, ces plantes vont absorber du carbone de l’atmosphère et contrebalancent ainsi les émissions des gaz de putréfaction des étangs.

La façon dont le système entier se présente dépend de la dénivellation relative du terrain. Le design de base c’est dans le terrain plat (ou presque) où la circulation d’eau dans le réseau est forcée par la pression provenant de la rivière. La présence des monts et vallées change le jeu : à part la pression de flux riverain, on peut utiliser les siphons romains pour créer un courant additionnel.

Je sais que dès un système comme celui-là est proposé, l’objection courante est celle à propos des moustiques. Des étangs à proximité d’habitations humaines veulent dire des tonnes de moustiques. L’une des observations pratiques sur lesquelles je me base est que ça arrive de toute façon. Je peux observer ce phénomène chez moi, en Pologne du sud. Année après année, certains endroits progressivement s’imbibent d’eau. Des petits creux de terrains se transforment en des marais microscopiques. Des complexes résidentiels entiers dans les banlieues des grandes villes connaissent des vagues de travaux de rénovation pour renforcer l’isolation hydrophobe des fondements.  Oui, ça arrive déjà et le problème c’est que ça pose que des problèmes, sans retombés positifs niveau accès à l’eau potable. Autant civiliser le phénomène. Ci-dessous, je présente une carte d’Europe Centrale et Méridionale, où les emplacements des vallées fluviales sont marqués.

En plus, on peut de débarrasser des moustiques – ou les rendre, au moins, presque inoffensifs – avec l’aide de la végétation adéquate. J’ai fait un peu de recherche et voilà la liste des plantes qui repoussent les moustiques et qui donc, si plantées abondamment à travers ces structures faites d’étangs et des cours d’eau, peuvent largement résoudre ce problème-là :  la citronnelle (Cymbopogon nardus), la mélisse officinale (Melissa officinalis), la cataire (Nepeta cataria)

le souci officinal (Calendula officinalis), la rose d’Inde (Tagetes erecta), l’œillet d’Inde (Tagetes patula), la Tagète lucida (Tagetes lucida), la Tagète citron (Tagetes tenuifolia), Baileya multiradiata (pas de nom français distinctif, pour autant que je sache), le populage des marais (Caltha palustres), le basilic (Ocimum basilicum), la lavande (famille Lamiacae), la menthe poivrée (Mentha x piperita), l’ail (Allium sativum), la menthe pouliot (Mentha pulegium), le romarin (Rosmarinus officinalis) et finalement les géraniums (famille Geraniums).

Source: https://www.eea.europa.eu/data-and-maps/figures/floodplain-distribution dernier accès 20 Juin 2019

Ah, oui, j’ai oublié : dans un premier temps, je veux étudier la possibilité d’installer tout ce bazar dans l’environnement urbain, quelque chose comme des marais civilisés et citadins, Ça fait plus d’un an que j’ai abordé le sujet des villes intelligentes et ben voilà un concept qui va à merveille. Je veux développer cette idée comme projet de promotion immobilière. Je me suis dit que si je réussis à y donner une forme purement entrepreneuriale, ce sera le test le plus exigeant en termes de faisabilité. Je veux dire que si c’est profitable – ou plutôt s’il y a des fortes chances que ce soit profitable – le concept peut se développer sans aide publique. Cette dernière peut apporter du changement positif additionnel, bien sûr, mais le truc peut se développer par la force des marchés locaux de l’immobilier. Voilà donc que je considère la valeur économique d’un projet comme la valeur actuelle nette du flux de trésorerie. Sur un horizon de « n » périodes, deux choses adviennent : le projet génère un flux de trésorerie, d’une part, et il note un changement de valeur du marché d’autre part. La formule que je présente ci-dessous est une modification de celle présentée par Hatata et al. 2019[1]. À part une notation légèrement modifiée, j’élimine la catégorie séparée des coûts de maintenance des installations et je les inclue dans la catégorie générale des coûts opérationnels. En revanche, si les dépenses sur la maintenance courante des installations sont une compensation de l’amortissement physique et donc s’ils constituent des additions à la valeur brute des biens immobiliers, on les compte comme investissement.  

Je commence l’application empirique de la formule par étudier le marché des terrains de construction en Europe, plus spécialement dans les zones riveraines. Je retourne à la comparaison entre ma ville natale, Krakow, Pologne, où je vis, en Lyon, France, où j’avais passé quelques années autant troublées qu’intéressantes de mon adolescence. Krakow d’abord : 1 mètre carré de terrain de construction, dans la ville-même, coûte entre €115 et €280. À Lyon, la fourchette des prix est plus large et plus élevée : entre €354 et €1200 par m2.

Question : quelle superficie pourrait bien avoir un terrain urbain transformé en ce marécage artificiel ? Question dure à répondre. J’essaie de l’attaquer par le bout aquatique. Ce système a pour une des fonctions de stocker, dans le réseau d’étangs, suffisamment d’eau de pluie pour satisfaire la demande de la population locale et de laisser encore un surplus résiduel. J’ai fait un peu de recherche sur la quantité d’eau consommée dans les ménages. En fait, il y a peu de données claires et sans équivoque sur le sujet. La source qui a l’air d’être la plus sérieuse est AQUASTAT – Système d’information mondial de la FAO sur l’eau et l’agriculture.

Une déconstruction prudente des données publiées par la Banque Mondiale indique que la consommation domestique d’eau en France est d’à peu près 81 ÷ 82 m3 par personne par an, soit entre 81 000 et 82 000 litres. En Danemark, c’est à peu près 59 ÷ 60 m3 par personne par an (59 000 ÷ 60 000 litres) et je n’ai aucune idée où cette différence peut bien venir. J’ai déjà éliminé l’usage non-domestique, au moins selon la structure logique des données présentées par la banque mondiale. En revanche, lorsque j’ai étudié quelques publications polonaises sur le sujet, il paraît que la consommation domestique d’eau est plutôt répétitive à travers l’Europe et elle oscille entre 36 et 40 m3 par personne par an.

Il y a certainement une source de ces disparités : la distinction entre, d’une part, la consommation ménagère strictement comptée, avec des compteurs d’eau associés aux personnes précises et d’autre part, la consommation personnelle totale, y compris l’usage d’eau de puits et d’eau en bouteilles et bidons. Du point de vue hydrologique, chaque endroit sur Terre reçoit une certaine quantité d’eau Ep de précipitations atmosphériques – donc de pluie ou de neige – ainsi qu’à travers des rivières qui apportent l’eau des territoires adjacents. Le même endroit déverse une quantité définie Ed d’eau dans les mers et océans adjacents, à travers les fleuves. Le territoire entier perd aussi une quantité définie Ev d’eau par évaporation. La différence Er = Ep – Ev – Ed est la quantité absorbée par le territoire.

Lorsque nous, les humains, utilisons l’eau dans notre vie quotidienne, la plupart de cette consommation atterrit dans des égouts de toute sorte, qui la conduisent vers et dans le réseau fluvial. Oui, lorsque nous arrosons nos jardins, une partie de cette eau s’évapore, mais la grande majorité de notre consommation d’eau entre dans la composante Ed ci-dessus. Le flux Ed peut être décomposé en deux sous-flux : le flux strictement naturel Ed-n d’eau qui coule tout simplement, ça et là, et le flux Ed-h qui passe à travers l’utilisation humaine. Pour être tout à fait précis, on peut adopter la même distinction pour l’eau d’évaporation, donc Ev = Ev-n + Ev-h.

Le sentier conceptuel préliminairement défriché, je peux passer en revue un peu de littérature. Katsifarakis et al. (2015[1]) décrivent l’application d’une structure urbaine appelée « jardin pluvial » (« rain garden » en anglais). Grosso modo, un jardin pluvial est une agglomération des structures superficielles qui favorisent la collection d’eau de pluie – égouts, puits, arbustes, près humides, étangs ouverts – avec des structures souterraines qui favorisent la rétention de la même eau dans des couches successives du sol. Ici, ‘y a un truc intéressant que l’article de Katsifarakis et al. suggère comme attribut possible d’un jardin pluvial : le drainage inversé. Normalement, les tuyaux de drainage servent à éconduire l’eau de pluie en dehors du terrain donné. Cependant, il est possible d’enfoncer les tuyaux de drainage verticalement, vers et dans les couches profondes du sol, pour favoriser la rétention d’eau de pluie dans des poches souterraines profondes, un peu comme des poches artésiennes. J’ai essayé de présenter l’idée visuellement ci-dessous. Normalement, un étang, ça se creuse jusqu’à ce qu’on arrive à une couche géologique imperméable ou peu perméable. C’est comme ça que l’eau reste dedans. Si en-dessous de cette couche imperméable il y a une nappe perméable et poreuse, capable de retenir de l’eau, une nappe aquifère peut se former dans les roches sous l’étang. L’étang de surface est alors une structure de captage et la rétention proprement dite survient dans l’aquifère sous-jacent. Remarquez, faut faire gaffe avec le drainage renversé et les aquifères. Ça marche bien dans des endroits vraiment plats et naturellement fluviaux, comme dans les plaines riveraines d’une rivière. C’est plat et – grâce au boulot qu’avaient fait les glaciers, dans le passé – ça contient des larges poches sableuses insérées entre des nappes rocheuses imperméables. En revanche, si le terrain est en pente ou bien s’il se termine par une falaise, un aquifère peut provoquer des glissements de terrain gigantesques.  

Alors, voyons voir comment des trucs comme drainage inversé peuvent marcher pour stocker l’eau de pluie ou bien celle d’inondation. Je m’en tiens à mes deux exemples : Krakow en Pologne et Lyon en France. En France, les précipitations annuelles moyennes[1] sont de 867 milimètres par an par mètre carré ; en Pologne, c’est 600 mm. Un milimètre de précipitation par mètre carré veut dire 1 litre, donc 0,001 mètre cube. En France, le mètre carré moyen de territoire collecte donc 0,867 m3 de précipitations annuelles, avec une consommation moyenne ménagère d’environ 81,69 m3 par personne par an. Pour que la personne moyenne aie sa consommation d’eau contrebalancée par le stockage d’eau de pluie, il faut donc 81,69 m3 / 0,867 [m3/m2] = 94,23 m2 de surface de collection d’eau. Ajoutons à ceci un surplus de 20%, à titre de stockage résiduel par-dessus la consommation courante : ceci fait 94,23 m2 * 1,2 = 113,07 m2. En d’autres mots, en France, l’eau de pluie (ou neige) collectée de la surface d’environ 113 ÷ 114 mètres carrés de terrain ouvert exposé directement aux précipitations peut pourvoir, si captée proprement, à la consommation moyenne d’eau d’une personne plus un résidu mis en réserve.

En ce qui concerne la Pologne, même la source la plus exhaustive, donc AQUASTAT de FAO, ne donne pas d’estimation de consommation d’eau par personne. Je vais donc faire un petit tour de maths, prendre les estimations pour la France et les comparer avec un pays voisin à tous les deux, donc l’Allemagne : consommation totale d’eau par personne par an égale à 308,5 mètres cube, dont la consommation ménagère devrait prendre à peu de choses près 20%, soit 62 m3. J’assume donc qu’un Polonais moyen consomme ces 62 m3 d’eau par an, j’y ajoute 20% pour stockage résiduel, ce qui me fait 74,4 m3. Je divise ça par les 0,6 m3 de précipitations annuelles par mètre carré. En fin de compte j’obtiens 124 m2 de surface arrangée en jardin pluvial. Encore une fois, je résume graphiquement.

Je reviens à la revue de littérature. Shao et al. (2018[1]) présentent un concept similaire au mien : la ville – éponge ou « sponge city » en anglais. La ville – éponge absorbe l’eau et le carbone. E plus, grâce à l’absorption de l’eau pluviale, la ville – éponge a besoin de moins d’énergie pour pomper l’eau dans l’infrastructure urbaine et de cette façon une telle structure dégage moins de CO2. La ville – éponge combine la verdure et les jardins pluviaux avec des zones marécageuses, comme le concept que j’essaie de développer. Selon les estimations présentées par Shao et al., la capacité d’absorption de carbone dans des villes – éponges déjà mises en place en Chine est très variable : de 4,49 grammes de carbone par an par mètre carré dans les marécages des plaines du Nord – Est de Chine jusqu’à 56,67 grammes par an par mètre carré dans les marécages des lacs des plaines orientales. Shao et al. présentent une analyse détaillée de la ville de Xiamen. Avec 3,5 millions d’habitants, une surface totale de 1 865 km2 et son infrastructure de ville – éponge couvrant à peu près 118 kilomètres carrés, la ville de Xiamen compte retenir 17,18 millions des mètres cubes d’eau de pluie par an, à travers la technologie des structures – éponge.

Pour donner une image complète, il faut dire que Xiamen note des précipitations tout à fait significatives : 1131 millimètres par an, selon le service Climate-Data.org[2]. Bon, calmons le jeu, parce qu’il y a quelque chose qui cloche dans ces calculs de par Shao et al. J’assume que l’infrastructure de la ville – éponge collecte l’eau de pluie de toute la ville, donc que les 118 km2 de cette infrastructure absorbent l’eau qui tombe sur la surface totale des 1 865 km2 de la ville. Les précipitations annuelles de 1131 millimètres –  donc 1,131 m3 – par mètre carré donnent 1865000 m2 * 1,131 m3/m2 =  2 109 315 m3. Cela voulait dire que selon les calculs de Shao et al. l’infrastructure – éponge de Xiamen absorbe 8 fois plus d’eau de pluie qu’il y a de pluie. Ambitieux mais peu réaliste.  La hydrologie, c’est compliqué. Je continue à vous fournir de la bonne science, presque neuve, juste un peu cabossée dans le processus de conception. Je vous rappelle que vous pouvez télécharger le business plan du projet BeFund (aussi accessible en version anglaise). Vous pouvez aussi télécharger mon livre intitulé “Capitalism and Political Power”. Je veux utiliser le financement participatif pour me donner une assise financière dans cet effort. Vous pouvez soutenir financièrement ma recherche, selon votre meilleur jugement, à travers mon compte PayPal. Vous pouvez aussi vous enregistrer comme mon patron sur mon compte Patreon . Si vous en faites ainsi, je vous serai reconnaissant pour m’indiquer deux trucs importants : quel genre de récompense attendez-vous en échange du patronage et quelles étapes souhaitiez-vous voir dans mon travail ? Vous pouvez me contacter à travers la boîte électronique de ce blog : goodscience@discoversocialsciences.com .

[1] Shao, W., Liu, J., Yang, Z., Yang, Z., Yu, Y., & Li, W. (2018). Carbon Reduction Effects of Sponge City Construction: A Case Study of the City of Xiamen. Energy Procedia, 152, 1145-1151.

[2] https://en.climate-data.org/asia/china/fujian/xiamen-2623/ dernier accès 30 Juin 2019

[1] https://data.worldbank.org/indicator/AG.LND.PRCP.MM dernier accès 30 Juin 2019

[1] Katsifarakis, K. L., Vafeiadis, M., & Theodossiou, N. (2015). Sustainable drainage and urban landscape upgrading using rain gardens. Site selection in Thessaloniki, Greece. Agriculture and agricultural science procedia, 4, 338-347.

[1] Hatata, A. Y., El-Saadawi, M. M., & Saad, S. (2019). A feasibility study of small hydro power for selected locations in Egypt. Energy Strategy Reviews, 24, 300-313.

Another idea – urban wetlands

My editorial on You Tube

I have just come with an idea. One of those big ones, the kind that pushes you to write a business plan and some scientific stuff as well. Here is the idea: a network of ponds and waterways, made in the close vicinity of a river, being both a reservoir of water – mostly the excess rainwater from big downpours – and a location for a network of small water turbines. The idea comes from a few observations, as well as other ideas, that I had over the last two years. Firstly. in Central Europe, we have less and less water from the melting snow – as there is almost no snow anymore in winter – and more and more water from sudden, heavy rain. We need to learn how to retain rainwater in the most efficient way. Secondly, as we have local floods due to heavy rains, some sort of spontaneous formation of floodplains happens. Even if there is no visible pond, the ground gets a bit spongy and soaked, flood after flood. We have more and more mosquitoes. If it is happening anyway, let’s use it creatively. This particular point is visualised in the map below, with the example of Central and Southern Europe. Thus, my idea is to utilise purposefully a naturally happening phenomenon, component of climate change.

Source: https://www.eea.europa.eu/data-and-maps/figures/floodplain-distribution last access June 20th, 2019

Thirdly, there is some sort of new generation in water turbines: a whole range of small devices, simple and versatile, has come to the market.  You can have a look at what those guys at Blue Freedom are doing. Really interesting. Hydroelectricity can now be approached in an apparently much less capital-intensive way. Thus, the idea I have is to arrange purposefully the floodplains we have in Europe into as energy-efficient and carbon-efficient places as possible. I give the general idea graphically in the picture below.

I am approaching the whole thing from the economics’ point of view, i.e. I want a piece of floodplain arranged into this particular concept to have more value, financial value included, than the same piece of floodplain just being ignored in its inherent potential. I can see two distinct avenues for developing the concept: that of a generally wild, uninhabited floodplain, like public land, as opposed to an inhabited floodplain, under incumbent or ongoing construction, residential or other. The latter is precisely what I want to focus on. I want to study, and possibly to develop a business plan for a human habitat combined with a semi-aquatic ecosystem, i.e. a network of ponds, waterways and water turbines in places where people live and work. Hence, from the geographic point of view, I am focusing on places where the secondary formation of floodplain-type of terrain already occurs in towns and cities, or in the immediate vicinity thereof. For more than one century, the growth of urban habitats has been accompanied by the entrenching of waterways in strictly defined, concrete-reinforced beds. I want to go the other way, and let those rivers spill around their waters, into wetlands, in a manner beneficial to human dwelling.

My initial approach to the underlying environmental concept is market based. Can we create urban wetlands, in flood-threatened areas, where the presence of the explicitly and purposefully arranged aquatic structures increases the value of property so as to top the investment required? I start with the most fundamental marks in the environment. I imagine a piece of land in an urban area. It has its present market value, and I want to study its possible value in the future.

I imagine a piece of land located in an urban area with the characteristics of a floodplain, i.e. recurrently threatened by local floods or the secondary effects thereof. At the moment ‘t’, that piece of land has a market value M(t) = S * m(t), being the product of its total surface S, constant over time, and the market price m(t) per unit of surface, changing over time. There are two moments in time, i.e. the initial moment t0, and the subsequent moment t1, after the development into urban wetland. Said development requires a stream of investment I(t0 -> t1). I want to study the conditions for M(t1) – M(t0) > I(t0 -> t1). As surface S is constant over time, my problem breaks down into units of surface, whence the aggregate investment I(t0 -> t1) being decomposed into I(t0 -> t1) = S * i(t0 -> t1), and the problem restated as m(t1) – m(t0) >  i(t0 -> t1).

I assume the market price m(t) is based on two types of characteristics: those directly measurable as financials, for one, e.g. the average wage a resident can expect from a locally based job, and those more diffuse ones, whose translation into financial variables is subtler, and sometimes pointless. I allow myself to call the latter ones ‘environmental services’. They cover quite a broad range of phenomena, ranging from the access to clean water outside the public water supply system, all the way to subjectively perceived happiness and well-being. All in all, mathematically, I say m(t) = f(x1, x2, …, xk) : the market price of construction land in cities is a function of k variables. Consistently with the above, I assume that f[t1; (x1, x2, …, xk)] – f[t0; (x1, x2, …, xk)] > i(t0 -> t1).    

It is intellectually honest to tackle those characteristics of urban land that make its market price. There is a useful observation about cities: anything that impacts the value of urban real estate, sooner or later translates into rent that people are willing to pay for being able to stay there. Please, notice that even when we own a piece of real estate, i.e. when we have property rights to it, we usually pay to someone some kind of periodic allowance for being able to execute our property rights fully: the real estate tax, the maintenance fee paid to the management of residential condominiums, the fee for sanitation service (e.g. garbage collection) etc. Any urban piece of land has a rent tag attached. Even those characteristics of a place, which pertain mostly to the subjectively experienced pleasure and well-being derived out of staying there have a rent-like price attached to them, at the end of the day.

Good. I have made a sketch of the thing. Now, I am going to pass in review some published research, in order to set my landmarks. I start with some literature regarding urban planning, and as soon as I do so, I discover an application for artificial intelligence, a topic of interest for me, those last months. Lyu et al. (2017[1]) present a method for procedural modelling of urban layout, and in their work, I can spot something similar to the equations I have just come up with: complex analysis of land-suitability. It starts with dividing the total areal of urban land at hand, in a given city, into standard units of surface. Geometrically, they look nice when they are equisized squares. Each unit ‘i’ can be potentially used for many alternative purposes. Lyu et al. distinguish 5 typical uses of urban land: residential, industrial, commercial, official, and open & green. Each such surface unit ‘i’ is endowed with a certain suitability for different purposes, and this suitability is the function of a finite number of factors. Formally, the suitability sik of land unit i for use k is a weighted average over a vector of factors, where wkj is the weight of factor j for land use k, and rij is the rating of land unit i on factor j. Below, I am trying to reproduce graphically the general logic of this approach.

In a city approached analytically with the general method presented above, Lyu et al. (2017[1]) distribute three layers of urban layout: population, road network, and land use. It starts with an initial state (input state) of population, land use, and available area. In a first step of the procedure, a simulation of highways and arterial transport connections is made. The transportation grid suggests some kind of division of urban space into districts. As far as I understand it, Lyu et al. define districts as functional units with the quantitative dominance of certain land uses, i.e. residential vs. industrial rather than rich folks’ estate vs. losers’ end, sort of.

As a first sketch of district division is made, it allows simulating a first distribution of population in the city, and a first draft of land use. The distribution of population is largely a distribution of density in population, and the corresponding transportation grid is strongly correlated with it. Some modes of urban transport work only above some critical thresholds in the density of population. This is an important point: density of population is a critical variable in social sciences.

Then, some kind of planning freedom can be allowed inside districts, which results in a second draft of spatial distribution in population, where a new type of unit – a neighbourhood – appears. Lyu et al. do not explain in detail the concept of neighbourhood, and yet it is interesting. It suggests the importance of spontaneous settlement vs. that of planned spatial arrangement.

I am strongly attached to that notion of spontaneous settlement. I am firmly convinced that on the long run people live where they want to live, and urban planning can just make that process somehow smoother and more efficient. Thus comes another article in my review of literature, by Mahmoud & Divigalpitiya (2019[2]). By the way, I have an interesting meta-observation: most recent literature about urban development is based on empirical research in emerging economies and in developing countries, with the U.S. coming next, and Europe lagging far behind. In Europe, we do very little research about our own social structures, whilst them Egyptians or Thais are constantly studying the way they live collectively.

Anyway, back to by Mahmoud & Divigalpitiya (2019[3]), the article is interesting from my point of view because its authors study the development of new towns and cities. For me, it is an insight into how the radically new urban structures sink into the incumbent spatial distribution of population. The specific background of this particular study is a public policy of the Egyptian government to establish, in a planned manner, new cities some distance away from the Nile, and do it so as to minimize the encroachment on agricultural land. Thus, we have scarce space and people to fit into, with optimal use of land.

As I study that paper by Mahmoud & Divigalpitiya, some kind of extension to my initial idea emerges. Those researchers report that with proper water and energy management, more specifically with the creation of irrigative structures like those which I came up with – networks of ponds and waterways – paired with a network of small hydropower units, it is possible both to accommodate an increase of 90% in local urban population, and create 3,75% more of agricultural land. Another important finding about those new urban communities in Egypt is that they tend to grow by sprawl rather than by distant settlement. New city dwellers tend to settle close to the incumbent residents, rather than in more remote locations. In simple words: it is bloody hard to create a new city from scratch. Habits and social links are like a tangible expanse of matter, which opposes resistance to distortions.

I switch to another paper based on Egyptian research, namely that by Hatata et al. 2019[4], relative to the use of small hydropower generators. The paper is rich in technicalities, and therefore I note to come back to it many times when I will be going more into the details of my concept. For now, I have a few general takeaways. Firstly, it is wise to combine small hydro off grid with that connected to the power grid, and more generally, small hydro looks like a good complementary source of power, next to a regular grid, rather than a 100% autonomous power base. Still, full autonomy is possible, mostly with the technology of Permanent Magnet Synchronous Generator. Secondly, Hatata et al. present a calculation of economic value in hydropower projects, based on their Net Present Value, which, in turn, is calculated on the grounds of a basic assumption that hydropower installations carry some residual capital value Vr over their entire lifetime, and additionally can generate a current cash flow determined by: a) the revenue Rt from the sales of energy b) the locally needed investment It c) the operating cost Ot and d) the maintenance cost Mt, all that in the presence of a periodic discount rate r.

I am consistently delivering good, almost new science to my readers, and love doing it, and I am working on crowdfunding this activity of mine. You can communicate with me directly, via the mailbox of this blog: goodscience@discoversocialsciences.com. As we talk business plans, I remind you that you can download, from the library of my blog, the business plan I prepared for my semi-scientific project Befund  (and you can access the French version as well). You can also get a free e-copy of my book ‘Capitalism and Political Power’ You can support my research by donating directly, any amount you consider appropriate, to my PayPal account. You can also consider going to my Patreon page and become my patron. If you decide so, I will be grateful for suggesting me two things that Patreon suggests me to suggest you. Firstly, what kind of reward would you expect in exchange of supporting me? Secondly, what kind of phases would you like to see in the development of my research, and of the corresponding educational tools?

[1] Lyu, X., Han, Q., & de Vries, B. (2017). Procedural modeling of urban layout: population, land use, and road network. Transportation research procedia, 25, 3333-3342.

[2] Mahmoud, H., & Divigalpitiya, P. (2019). Spatiotemporal variation analysis of urban land expansion in the establishment of new communities in Upper Egypt: A case study of New Asyut city. The Egyptian Journal of Remote Sensing and Space Science, 22(1), 59-66.

[3] Mahmoud, H., & Divigalpitiya, P. (2019). Spatiotemporal variation analysis of urban land expansion in the establishment of new communities in Upper Egypt: A case study of New Asyut city. The Egyptian Journal of Remote Sensing and Space Science, 22(1), 59-66.

[4] Hatata, A. Y., El-Saadawi, M. M., & Saad, S. (2019). A feasibility study of small hydro power for selected locations in Egypt. Energy Strategy Reviews, 24, 300-313.

Sketching quickly alternative states of nature

My editorial on You Tube

I am thinking about a few things, as usually, and, as usually, it is a laborious process. The first one is a big one: what the hell am I doing what I am doing for? I mean, what’s the purpose and the point of applying artificial intelligence to simulating collective intelligence? There is one particular issue that I am entertaining in this regard: the experimental check. A neural network can help me in formulating very precise hypotheses as for how a given social structure can behave. Yet, these are hypotheses. How can I have them checked?

Here is an example. Together with a friend, we are doing some research about the socio-economic development of big cities in Poland, in the perspective of seeing them turning into so-called ‘smart cities’. We came to an interesting set of hypotheses generated by a neural network, but we have a tiny little problem: we propose, in the article, a financial scheme for cities but we don’t quite understand why we propose this exact scheme. I know it sounds idiotic, but well: it is what it is. We have an idea, and we don’t know exactly where that idea came from.

I have already discussed the idea in itself on my blog, in « Locally smart. Case study in finance.» : a local investment fund, created by the local government, to finance local startup businesses. Business means investment, especially at the aggregate scale and in the long run. This is how business works: I invest, and I have (hopefully) a return on my investment. If there is more and more private business popping up in those big Polish cities, and, in the same time, local governments are backing off from investment in fixed assets, let’s make those business people channel capital towards the same type of investment that local governments are withdrawing from. What we need is an institutional scheme where local governments financially fuel local startup businesses, and those businesses implement investment projects.

I am going to try and deconstruct the concept, sort of backwards. I am sketching the landscape, i.e. the piece of empirical research that brought us to formulating the whole idea of investment fund paired with crowdfunding.  Big Polish cities show an interesting pattern of change: local populations, whilst largely stagnating demographically, are becoming more and more entrepreneurial, which is observable as an increasing number of startup businesses per 10 000 inhabitants. On the other hand, local governments (city councils) are spending a consistently decreasing share of their budgets on infrastructural investment. There is more and more business going on per capita, and, in the same time, local councils seem to be slowly backing off from investment in infrastructure. The cities we studied as for this phenomenon are: Wroclaw, Lodz, Krakow, Gdansk, Kielce, Poznan, Warsaw.

More specifically, the concept tested through the neural network consists in selecting, each year, 5% of the most promising local startups, and funds each of them with €80 000. The logic behind this concept is that when a phenomenon becomes more and more frequent – and this is the case of startups in big Polish cities – an interesting strategy is to fish out, consistently, the ‘crème de la crème’ from among those frequent occurrences. It is as if we were soccer promotors in a country, where more and more young people start playing at a competitive level. A viable strategy consists, in such a case, in selecting, over and over again, the most promising players from the top of the heap and promote them further.

Thus, in that hypothetical scheme, the local investment fund selects and supports the most promising from amongst the local startups. Mind you, that 5% rate of selection is just an idea. It could be 7% or 3% just as well. A number had to be picked, in order to simulate the whole thing with a neural network, which I present further. The 5% rate can be seen as an intuitive transference from the s-Student significance test in statistics. When you test a correlation for its significance, with the t-Student test, you commonly assume that at least 95% of all the observations under scrutiny is covered by that correlation, and you can tolerate a 5% outlier of fringe cases. I suppose this is why we picked, intuitively, that 5% rate of selection among the local startups: 5% sounds just about right to delineate the subset of most original ideas.

Anyway, the basic idea consists in creating a local investment fund controlled by the local government, and this fund would provide a standard capital injection of €80 000 to 5% of most promising local startups. The absolute number STF (i.e. financed startups) those 5% translate into can be calculated as: STF = 5% * (N/10 000) * ST10 000, where N is the population of the given city, and ST10 000 is the coefficient of startup businesses per 10 000 inhabitants. Just to give you an idea what it looks like empirically, I am presenting data for Krakow (KR, my hometown) and Warsaw (WA, Polish capital), in 2008 and 2017, which I designate, respectively, as STF(city_acronym; 2008) and STF(city_acronym; 2017). It goes like:

STF(KR; 2008) = 5% * (754 624/ 10 000) * 200 = 755

STF(KR; 2017) = 5* * (767 348/ 10 000) * 257 = 986

STF(WA; 2008) = 5% * (1709781/ 10 000) * 200 = 1 710

STF(WA; 2017) = 5% * (1764615/ 10 000) * 345 = 3 044   

That glimpse of empirics allows guessing why we applied a neural network to that whole thing: the two core variables, namely population and the coefficient of startups per 10 000 people, can change with a lot of autonomy vis a vis each other. In the whole sample that we used for basic stochastic analysis, thus 7 cities from 2008 through 2017 equals 70 observations, those two variables are Pearson-correlated at r = 0,6267. There is some significant correlation, and yet some 38% of observable variance in each of those variables doesn’t give a f**k about the variance of the other variable. The covariance of these two seems to be dominated by the variability in population rather than by uncertainty as for the average number of startups per 10 000 people.

What we have is quite predictable a trend of growing propensity to entrepreneurship, combined with a bit of randomness in demographics. Those two can come in various duos, and their duos tend to be actually trios, ‘cause we have that other thing, which I already mentioned: investment outlays of local governments and the share of those outlays in the overall local budgets. Our (my friend’s and mine) intuitive take on that picture was that it is really interesting to know the different ways those Polish cities can go in the future, rather that setting one central model. I mean, the central stochastic model is interesting too. It says, for example, that the natural logarithm of the number of startups per 10 000 inhabitants, whilst being negatively correlated with the share of investment outlays in the local government’s budget, it is positively correlated with the absolute amount of those outlays. The more a local government spends on fixed assets, the more startups it can expect per 10 000 inhabitants. That latter variable is subject to some kind of scale effects from the part of the former. Interesting. I like scale effects. They are intriguing. They show phenomena, which change in a way akin to what happens when I heat up a pot full of water: the more heat have I supplied to water, the more different kinds of stuff can happen. We call it increase in the number of degrees of freedom.

The stochastically approached degrees of freedom in the coefficient of startups per 10 000 inhabitants, you can see them in Table 1, below. The ‘Ln’ prefix means, of course, natural logarithms. Further below, I return to the topic of collective intelligence in this specific context, and to using artificial intelligence to simulate the thing.

Table 1

Explained variable: Ln(number of startups per 10 000 inhabitants) R2 = 0,608 N = 70
Explanatory variable Coefficient of regression Standard error Significance level
Ln(investment outlays of the local government) -0,093 0,048 p = 0,054
Ln(total budget of the local government) 0,565 0,083 p < 0,001
Ln(population) -0,328 0,09 p < 0,001
Constant    -0,741 0,631 p = 0,245

I take the correlations from Table 1, thus the coefficients of regression from the first numerical column, and I check their credentials with the significance level from the last numerical column. As I want to understand them as real, actual things that happen in the cities studied, I recreate the real values. We are talking about coefficients of startups per 10 000 people, comprised somewhere the observable minimum ST10 000 = 140, and the maximum equal to ST10 000 = 345, with a mean at ST10 000 = 223. It terms of natural logarithms, that world folds into something between ln(140) = 4,941642423 and ln(345) = 5,843544417, with the expected mean at ln(223) = 5,407171771. Standard deviation Ω from that mean can be reconstructed from the standard error, which is calculated as s = Ω/√N, and, consequently, Ω = s*√N. In this case, with N = 70, standard deviation Ω = 0,631*√70 = 5,279324767.  

That regression is interesting to the extent that it leads to an absurd prediction. If the population of a city shrinks asymptotically down to zero, and if, in the same time, the budget of the local government swells up to infinity, the occurrence of entrepreneurial behaviour (number of startups per 10 000 inhabitants) will tend towards infinity as well. There is that nagging question, how the hell can the budget of a local government expand when its tax base – the population – is collapsing. I am an economist and I am supposed to answer questions like that.

Before being an economist, I am a scientist. I ask embarrassing questions and then I have to invent a way to give an answer. Those stochastic results I have just presented make me think of somehow haphazard a set of correlations. Such correlations can be called dynamic, and this, in turn, makes me think about the swarm theory and collective intelligence (see Yang et al. 2013[1] or What are the practical outcomes of those hypotheses being true or false?). A social structure, for example that of a city, can be seen as a community of agents reactive to some systemic factors, similarly to ants or bees being reactive to pheromones they produce and dump into their social space. Ants and bees are amazingly intelligent collectively, whilst, let’s face it, they are bloody stupid singlehandedly. Ever seen a bee trying to figure things out in the presence of a window? Well, not only can a swarm of bees get that s**t down easily, but also, they can invent a way of nesting in and exploiting the whereabouts of the window. The thing is that a bee has its nervous system programmed to behave smartly mostly in social interactions with other bees.

I have already developed on the topic of money and capital being a systemic factor akin to a pheromone (see Technological change as monetary a phenomenon). Now, I am walking down this avenue again. What if city dwellers react, through entrepreneurial behaviour – or the lack thereof – to a certain concentration of budgetary spending from the local government? What if the budgetary money has two chemical hooks on it – one hook observable as ‘current spending’ and the other signalling ‘investment’ – and what if the reaction of inhabitants depends on the kind of hook switched on, in the given million of euros (or rather Polish zlotys, or PLN, as we are talking about Polish cities)?

I am returning, for a moment, to the negative correlation between the headcount of population, on the one hand, and the occurrence of new businesses per 10 000 inhabitants. Cities – at least those 7 Polish cities that me and my friend did our research on – are finite spaces. Less people in the city means less people per 1 km2 and vice versa. Hence, the occurrence of entrepreneurial behaviour is negatively correlated with the density of population. A behavioural pattern emerges. The residents of big cities in Poland develop entrepreneurial behaviour in response to greater a concentration of current budgetary spending by local governments, and to lower a density of population. On the other hand, greater a density of population or less money spent as current payments from the local budget act as inhibitors of entrepreneurship. Mind you, greater a density of population means greater a need for infrastructure – yes, those humans tend to crap and charge their smartphones all over the place – whence greater a pressure on the local governments to spend money in the form of investment in fixed assets, whence the secondary in its force, negative correlation between entrepreneurial behaviour and investment outlays from local budgets.

This is a general, behavioural hypothesis. Now, the cognitive challenge consists in translating the general idea into as precise empirical hypotheses as possible. What precise states of nature can happen in those cities? This is when artificial intelligence – a neural network – can serve, and this is when I finally understand where that idea of investment fund had come from. A neural network is good at producing plausible combinations of values in a pre-defined set of variables, and this is what we need if we want to formulate precise hypotheses. Still, a neural network is made for learning. If I want the thing to make those hypotheses for me, I need to give it a purpose, i.e. a variable to optimize, and learn as it is optimizing.

In social sciences, entrepreneurial behaviour is assumed to be a good thing. When people recurrently start new businesses, they are in a generally go-getting frame of mind, and this carries over into social activism, into the formation of institutions etc. In an initial outburst of neophyte enthusiasm, I might program my neural network so as to optimize the coefficient of startups per 10 000 inhabitants. There is a catch, though. When I tell a neural network to optimize a variable, it takes the most likely value of that variable, thus, stochastically, its arithmetical average, and it keeps recombining all the other variables so as to have this one nailed down, as close to that most likely value as possible. Therefore, if I want a neural network to imagine relatively high occurrences of entrepreneurial behaviour, I shouldn’t set said behaviour as the outcome variable. I should mix it with others, as an input variable. It is very human, by the way. You brace for achieving a goal, you struggle the s**t out of yourself, and you discover, with negative amazement, that instead of moving forward, you are actually repeating the same existential pattern over and over again. You can set your personal compass, though, on just doing a good job and having fun with it, and then, something strange happens. Things get done sort of you haven’t even noticed when and how. Goals get nailed down even without being phrased explicitly as goals. And you are having fun with the whole thing, i.e. with life.

Same for artificial intelligence, as it is, as a matter of fact, an artful expression of our own, human intelligence: it produces the most interesting combinations of variables as a by-product of optimizing something boring. Thus, I want my neural network to optimize on something not-necessarily-fascinating and see what it can do in terms of people and their behaviour. Here comes the idea of an investment fund. As I have been racking my brains in the search of place where that idea had come from, I finally understood: an investment fund is both an institutional scheme, and a metaphor. As a metaphor, it allows decomposing an aggregate stream of investment into a set of more or less autonomous projects, and decisions attached thereto. An investment fund is a set of decisions coordinated in a dynamically correlated manner: yes, there are ways and patterns to those decisions, but there is a lot of autonomous figuring-out-the-thing in each individual case.

Thus, if I want to put functionally together those two social phenomena – investment channelled by local governments and entrepreneurial behaviour in local population – an investment fund is a good institutional vessel to that purpose. Local government invests in some assets, and local homo sapiens do the same in the form of startups. What if we mix them together? What if the institutional scheme known as public-private partnership becomes something practiced serially, as a local market for ideas and projects?

When we were designing that financial scheme for local governments, me and my friend had the idea of dropping a bit of crowdfunding into the cooking pot, and, as strange as it could seem, we are bit confused as for where this idea came from. Why did we think about crowdfunding? If I want to understand how a piece of artificial intelligence simulates collective intelligence in a social structure, I need to understand what kind of logical connections had I projected into the neural network. Crowdfunding is sort of spontaneous. When I am having a look at the typical conditions proposed by businesses crowdfunded at Kickstarter or at StartEngine, these are shitty contracts, with all the due respect. Having a Master’s in law, when I look at the contracts offered to investors in those schemes, I wouldn’t sign such a contract if I had any room for negotiation. I wouldn’t even sign a contract the way I am supposed to sign it via a crowdfunding platform.

There is quite a strong piece of legal and business science to claim that crowdfunding contracts are a serious disruption to the established contractual patterns (Savelyev 2017[2]). Crowdfunding largely rests on the so-called smart contracts, i.e. agreements written and signed as software on Blockchain-based platforms. Those contracts are unusually flexible, as each amendment, would it be general or specific, can be hash-coded into the history of the individual contractual relation. That puts a large part of legal science on its head. The basic intuition of any trained lawyer is that we negotiate the s**t of ourselves before the signature of the contract, thus before the formulation of general principles, and anything that happens later is just secondary. With smart contracts, we are pretty relaxed when it comes to setting the basic skeleton of the contract. We just put the big bones in, and expect we gonna make up the more sophisticated stuff as we go along.

With the abundant usage of smart contracts, crowdfunding platforms have peculiar legal flexibility. Today you sign up for having a discount of 10% on one Flower Turbine, in exchange of £400 in capital crowdfunded via a smart contract. Next week, you learn that you can turn your 10% discount on one turbine into 7% on two turbines if you drop just £100 more into that pig coin. Already the first step (£400 against the discount of 10%) would be a bit hard to squeeze into classical contractual arrangements as for investing into the equity of a business, let alone the subsequent amendment (Armour, Enriques 2018[3]).

Yet, with a smart contract on a crowdfunding platform, anything is just a few clicks away, and, as astonishing as it could seem, the whole thing works. The click-based smart contracts are actually enforced and respected. People do sign those contracts, and moreover, when I mentally step out of my academic lawyer’s shoes, I admit being tempted to sign such a contract too. There is a specific behavioural pattern attached to crowdfunding, something like the Russian ‘Davaj, riebiata!’ (‘Давай, ребята!’ in the original spelling). ‘Let’s do it together! Now!’, that sort of thing. It is almost as I were giving someone the power of attorney to be entrepreneurial on my behalf. If people in big Polish cities found more and more startups, per 10 000 residents, it is a more and more recurrent manifestation of entrepreneurial behaviour, and crowdfunding touches the very heart of entrepreneurial behaviour (Agrawal et al. 2014[4]). It is entrepreneurship broken into small, tradable units. The whole concept we invented is generally placed in the European context, and in Europe crowdfunding is way below the popularity it has reached in North America (Rupeika-Aboga, Danovi 2015[5]). As a matter of fact, European entrepreneurs seem to consider crowdfunding as really a secondary source of financing.

Time to sum up a bit all those loose thoughts. Using a neural network to simulate collective behaviour of human societies involves a few deep principles, and a few tricks. When I study a social structure with classical stochastic tools and I encounter strange, apparently paradoxical correlations between phenomena, artificial intelligence may serve. My intuitive guess is that a neural network can help in clarifying what is sometimes called ‘background correlations’ or ‘transitive correlations’: variable A is correlated with variable C through the intermediary of variable B, i.e. A is significantly correlated with B, and B is significantly correlated with C, but the correlation between A and C remains insignificant.

When I started to use a neural network in my research, I realized how important it is to formulate very precise and complex hypotheses rather than definitive answers. Artificial intelligence allows to sketch quickly alternative states of nature, by gazillions. For a moment, I am leaving the topic of those financial solutions for cities, and I return to my research on energy, more specifically on energy efficiency. In a draft article I wrote last autumn, I started to study the relative impact of the velocity of money, as well as that of the speed of technological change, upon the energy efficiency of national economies. Initially, I approached the thing in the nicely and classically stochastic a way. I came up with conclusions of the type: ‘variance in the supply of money makes 7% of the observable variance in energy efficiency, and the correlation is robust’. Good, this is a step forward. Still, in practical terms, what does it give? Does it mean that we need to add money to the system in order to have greater an energy efficiency? Might well be the case, only you don’t add money to the system just like that, ‘cause most of said money is account money on current bank accounts, and the current balances of those accounts reflect the settlement of obligations resulting from complex private contracts. There is no government that could possibly add more complex contracts to the system.

Thus, stochastic results, whilst looking and sounding serious and scientific, have remote connexion to practical applications. On the other hand, if I take the same empirical data and feed it into a neural network, I get alternative states of nature, and those states are bloody interesting. Artificial intelligence can show me, for example, what happens to energy efficiency if a social system is more or less conservative in its experimenting with itself. In short, artificial intelligence allows super-fast simulation of social experiments, and that simulation is theoretically robust.

I am consistently delivering good, almost new science to my readers, and love doing it, and I am working on crowdfunding this activity of mine. You can communicate with me directly, via the mailbox of this blog: goodscience@discoversocialsciences.com. As we talk business plans, I remind you that you can download, from the library of my blog, the business plan I prepared for my semi-scientific project Befund  (and you can access the French version as well). You can also get a free e-copy of my book ‘Capitalism and Political Power’ You can support my research by donating directly, any amount you consider appropriate, to my PayPal account. You can also consider going to my Patreon page and become my patron. If you decide so, I will be grateful for suggesting me two things that Patreon suggests me to suggest you. Firstly, what kind of reward would you expect in exchange of supporting me? Secondly, what kind of phases would you like to see in the development of my research, and of the corresponding educational tools?

[1] Yang, X. S., Cui, Z., Xiao, R., Gandomi, A. H., & Karamanoglu, M. (2013). Swarm intelligence and bio-inspired computation: theory and applications.

[2] Savelyev, A. (2017). Contract law 2.0:‘Smart’contracts as the beginning of the end of classic contract law. Information & Communications Technology Law, 26(2), 116-134.

[3] Armour, J., & Enriques, L. (2018). The promise and perils of crowdfunding: Between corporate finance and consumer contracts. The Modern Law Review, 81(1), 51-84.

[4] Agrawal, A., Catalini, C., & Goldfarb, A. (2014). Some simple economics of crowdfunding. Innovation Policy and the Economy, 14(1), 63-97

[5] Rupeika-Apoga, R., & Danovi, A. (2015). Availability of alternative financial resources for SMEs as a critical part of the entrepreneurial eco-system: Latvia and Italy. Procedia Economics and Finance, 33, 200-210.