Tag: renewable energy

Energy Ponds – l’état des lieux

I drift away from investor-relations sites, and I go back to my ‘Energy Ponds’ concept. I feel like going through it once again. First thing first, I want to lay out the idea such as I have figured it out so far. The second of the first things is that I am describing a yet-unimplemented, tentative technological solution, which combines water management with the generation of renewable energies. Ram-pumps are installed in the stream of a river. Kinetic energy of the river creates a by-flow through the ram-pumps, endowed with its own kinetic energy and flow rate, derived from those of the river. That by-flow is utilized in two ways. Some of it, within the limits of environmental sustainability of the riverine ecosystem, is pumped into wetland-type structures, which serve the purpose of water retention. On the way to wetlands, that sub-stream passes through elevated tanks, which create a secondary hydraulic head and allow placing hydroelectric turbines on pipes leading from elevated tanks to wetlands, i.e. back to ground level. The remaining part of the ram-pumped by-flow, before going back into the river, is recirculated through hydroelectric turbines located close to the ram-pump. The basic idea is shown graphically in Figure 1.

The remaining part of the article develops a complex proof of concept for ‘Energy Ponds’. Component solutions are discussed against the background of relevant literature, and a quantitative simulation of its basic parameters is presented, for simulated location in Poland, author’s home country.

Figure 1

‘Energy Ponds’ are supposed to create a system of water retention with as little invasive change in the landscape as possible. Typical reservoirs, such as artificial ponds and lakes, need space, and that space ought to be taken away from other employments thereof. This is a dilemma in land management: do we use a given portion of terrain for a retention reservoir or do we use it for other purposes? In densely populated regions, that dilemma becomes acute, when it comes to mutual arrangement of human architectural structures, agricultural land, and something which, fault of a better word, can be called ‘natural landscape’ (quotation marks refer to the fact that many landscapes which we intuitively gauge as ‘natural’ are actually man-made and artificially maintained).  

Water management, as a distinct field of technology, with an environmental edge, could benefit from innovation-boosting tools typical for other technological fields (Wehn & Montalvo 2018[1]; Mvulirwenande & Wehn 2020[2]). Technological change as regards water management is needed both in rural areas and in cities. Interestingly enough, urban environments seem to be more conservative than agriculture in terms of water management (Wong, Rogers & Brown 2020[3]). There is a quest for scientifically based constants in water management, such as the required quantity of water per person per day; Hogeboom (2020[4]) argues it is roughly 3800 liters after covering all the conventional uses of water. On the other hand, Mohamed et al. (2020[5]) claim that the concept of ‘arid region’, so far reserved for desertic environments, is de facto a type of environmental context when we systematically lack accurate environmental data as regards quickly changing availability of water. Kumar et al. (2020[6]) go even further and postulate something called ‘socio-hydrology’: human societies seem to develop characteristically differentiated patterns of collective behaviour in different hydrological conditions. Other research suggests that societies adapt to increased use of water by visibly increasing the productivity of that use, whilst increased income per capita seems being correlated with increased productivity in the use of water (Bagstad et al. 2020[7]).

In the ‘Energy Ponds’ concept, retention of water is supposed to be a slow, looped recirculation through local ecosystems, rather than accumulation in fixed reservoirs. Once water has been ram-pumped from a river into wetlands, the latter allow slow runoff, via ground waters, back into the hydrological system of the river basin. It is as if rain was falling once again in that river basin, with rainwater progressively collected by the drainage of the river. In hydrology, such a system is frequently referred to as quasi-reservoirs (Harvey et al. 2009[8]; Phiri et al. 2021[9]). Groundwater seems being the main factor of the observable water storage anomalies (Neves, Nunes, & Monteiro 2020[10]). Purposeful control of the size and density in the patches of green vegetation seems to be a good practical regulator of water availability, whence the interest in using wetlands as reservoirs (Chisola, Van der Laan, & Bristow 2020[11]).

Ram-pumps placed in the stream of a river become distributed energy resources based on renewable energy: they collect and divert part of the kinetic energy conveyed by the flow of water. Of course, one can ask: isn’t it simpler to put just hydroelectric turbines in that stream, without ram-pumps as intermediary? A ram-pump, properly instrumented with piping, becomes a versatile source of kinetic energy, which can be used for many purposes. In ‘Energy Ponds’, the idea is to use that energy both for creating a system of water retention, and for generating electricity. The former has its constraints. The amount of water to adsorb from the river is limited by the acceptable impact on ecosystems downstream. That impact can be twofold. Excessive adsorption upstream can expose the downstream ecosystems to dangerously low water levels, yet the opposite can happen as well: when we consider wetlands as pseudo-reservoirs, and thus as a reserve of water, its presence can stabilize the flow downstream (Hunt et al. 2022[12]), and the biological traits of ecosystems downstream are not necessarily at risk (Zhao et al. 2020[13]). Strong local idiosyncrasies can appear in that respect (Xu et al. 2020[14]).

Major rivers, even those in plains, have a hydraulic head counted in dozens of meters and a flow rate per second in the hundreds of cubic meters per second. With the typical efficiency of ram-pumps ranging from 35% to 66%, the basic physical model of ram-pumping (Fatahi-Alkouhi et al. 2019[15];  Zeidan & Ostfeld 2021[16]) allows pumping from the river more water than it is possible to divert into the local wetlands.  

Thus, two sub-streams are supposed to be ram-pumped in the ‘Energy Ponds’ system: one sub-stream ultimately directed to and retained in the local wetlands, and another one being the non-retainable excess, to be redirected immediately back into the river. The latter can immediately flow through hydroelectric turbines placed as close as possible to ram-pumps, in order not to lose kinetic energy. The other goes further, through the system of elevated tanks. Why introducing elevated tanks in the system? Isn’t it better to direct the retainable flow of water as directly as possible to wetlands, thus along a trajectory as flat as is feasible in the given terrain? The intuition behind elevated tanks is the general concept of Roman siphon (e.g. Angelakis et al. 2020[17]). When we place an artificially elevated tank along the flow of water from the river to the wetlands, it allows transporting water over a longer distance without losing kinetic energy in that flow. Therefore, the wetlands themselves, as well as the points of discharge from the piping of ‘Energy Ponds’ can be spread over a greater radius from the point of intake from the river. That gives flexibility as regards adapting the spatial distribution of the whole installation to the landscape at hand. Elevated water tanks can increase the efficiency of water management (Abunada et al. 2014[18]; Njepu, Zhang & Xia 2019[19]).

A water tank placed at the top of the Roman siphon is a reserve of energy in pumped storage, and thus allows creating a flow with the same kinetic energy as was generated by ram-pumps, yet further away from the point of intake in the river. Depending on the volumetric capacity and the relative height of the elevated tank, various amounts of energy can be stored. Two points are to consider in that respect. ‘Energy Ponds’ is supposed to be a solution as friendly as possible to the landscape. Big water towers are probably to exclude, and the right solution for elevated tanks seems closer to those encountered in farms, with relatively light structure. If there is need to store more energy in a local installation of ‘Energy Ponds’, there can be more such elevated tanks, scattered across the landscape. With respect to the relative height, documented research indicates a maximum cost-effective elevation between 30 and 50 meters, with 30 looking like a pragmatically conservative estimate (Inthachot et al. 2015[20]; Guo et al. 2018[21]; Li et al. 2021[22]). Elevated tanks such as conceived in ‘Energy Ponds’ can be various combinations of small equalizing tanks (serving just to level up intermittence in the flow of water), and structures akin raingardens (see e.g. Bortolini & Zanin 2019[23]), thus elevated platforms with embedded vegetal structures, capable of retaining substantial amounts of water.

As an alternative to artificial elevated tanks, and to the extent of possibilities offered by natural landscape, a mutation of the STORES technology (short-term off-river energy storage) can be used (Lu et al. 2021[24]; Stocks et al. 2021[25]). The landscape needed for that specific solution is a low hill, located next to the river and to the wetland. The top of such hill can host a water reservoir.

The whole structure of ‘Energy Ponds’, such as conceptually set for now, looks like a wetland, adjacent to the channel of a river, combined with tubular structures for water conduction, ram pumps and hydroelectric turbines. As for the latter, we keep in mind the high likelihood of dual stream: one close to ram-pumps, the other one after the elevated tanks. Proper distribution of power between the generating units can substantially reduce the amount of water used to generate the same amount of energy (Cordova et al. 2014[26]).

Hydroelectricity is collected in energy storage installations, which sell electricity to its end users. The whole concept follows the general stream of research on creating distributed energy resources coupled with landscape restoration (e.g.  Vilanova & Balestieri 2014[27]; Vieira et al. 2015[28]; Arthur et al. 2020[29] ; Cai, Ye & Gholinia 2020[30] ; Syahputra & Soesanti 2021[31]). In that path of research, combinations of energy sources (small hydro, wind and photovoltaic) plus battery backup, seem to be privileged as viable solutions, and seem allowing investment in RES installations with an expected payback time of approximately 10 – 11 years (Ali et al. 2021[32]). For the sake of clarity in the here-presented conceptualization of ‘Energy Ponds’, only the use of hydropower is considered, whilst, of course, the whole solution is open to adding other power sources to the mix, with limitations imposed by the landscape. As wetlands are an integral component of the whole concept, big windfarms seem excluded from the scope of energy sources, as they need solid support in the ground. Still, other combinations are possible. Once there is one type of RES exploited in the given location, it creates, with time, a surplus of energy which can be conducive to installing other types of RES power stations. The claim seems counterintuitive (if one source of energy is sufficient, then it is simply sufficient and crowds out other sources), yet there is evidence that local communities can consider RES according to the principle that ‘appetite grows as we eat’ (Sterl et al. 2020[33]). Among different solutions, floating solar farms, located on the surface of the wetland, could be an interesting extension of the ‘Energy Ponds’ concept (Farfan & Breyer 2018[34]; Sanchez et al. 2021[35]).

Material and methods

The general method of studying the feasibility of ‘Energy Ponds’ is always specific to a location, and it unfolds at two levels: a) calibrating the basic physical properties of the installation and b) assessing its economic viability. Hydrological determinants are strongly idiosyncratic, especially the amount of water possible do adsorb from the local river and to retain in wetlands, and the impact of retention in wetlands upon the ecosystems downstream. With wetlands as a vital component, the conceptual scheme of the ‘Energy Ponds’ naturally belongs to plains, as well as to wide river valleys surrounded by higher grounds. That kept in mind, there are conceptual developments as regards artificially created wetlands in the mountains (Shih & Hsu 2021[36]).

The local feasibility of ‘Energy Ponds’ starts with the possible location and size of wetlands. Places where wetlands either already exist or used to exist in the past, before being drained, seem to be the most natural, as local ecosystems are likely to be more receptive to newly created or expanded wetlands. Conflicts in land management between wetlands and, respectively, farmland and urban settlements, should be studied. It seems that the former type is sharper than the latter. There is documented technological development as regards the so-called Sponge Cities, where urban and peri-urban areas can be transformed into water-retaining structures, including the wetland-type ones (Sun et al. 2020[37]; Köster 2021[38]; Hamidi, Ramavandi & Sorial 2021[39]). On the other hand, farmland is a precious resource and conflicts between retention of water and agriculture are (and probably should be) settled in favour of agriculture.   

Quantitatively, data regarding rivers boils down to the flow rate in cubic meters per second, and to the hydraulic head. Flow and head are the elementary empirical observables of the here-presented method, and they enter into the basic equation of ram-pumping, as introduced by Zeidan & Ostfeld (2021 op.cit.), namely:

HR*QR*η = HT*QT                     (1)

…where HR is the hydraulic head of the river, QR is its flow rate in m3/s, HT is the relative elevation where water is being ram-pumped, QT is the quantity of pumped water, and η is a coefficient of general efficiency in the ram-pumps installed. That efficiency depends mostly on the length of pipes and their diameter, and ranges between 35% and 66% in the solutions currently available in the market. Knowing that QT is a pre-set percentage p of QR and, given the known research, p = QT/QR ≤ 20%, it can be written that QT = QRp. Therefore, equation (1) can be transformed:  

η = [HT*QR*p] / [HR*QR] = HT*p / HR              (2)

The coefficient η is predictable within the range that comes with the producer’s technology. It is exogenous to the ‘Energy Ponds’ system as such unless we assume producing special ram-pumps in the project. With a given flow per second in the river, efficiency η natural dictates the amount of water being ram-pumped, thus the coefficient of adsorption p.  Dual utilization of the ram-pumped flow (i.e. retention in wetlands and simple recirculation through proximate turbines) allows transforming equation (1) into equivalence (3):

{[HRQRHTp / HR] = [HRQC + HTQW] } {QRHTp = [HRQC + HTQW]}         (3)

…where Q­C stands for the sub-flow that is just being recirculated through turbines, without retention in wetlands, and QW is the amount retained. The balance of proportions between QC and QW is an environmental cornerstone in the ‘Energy Ponds’ concept, with QW being limited by two factors: the imperative of supplying enough water to ecosystems downstream, and the retentive capacity of the local wetlands. The latter is always a puzzle, and its thorough understanding requires many years of empirical observation. Still, a more practical method is proposed here: observable change in the surface of wetlands informs about changes in the amount of water stored. Of course, this is a crude observable, yet it can serve for regulating the amount of water conducted into the wetland.      

The hydraulic head of the river (HR) is given by the physical properties thereof, and thus naturally exogenous. Therefore, the fundamental technological choice in ‘Energy Ponds’ articulates into four ‘big’ variables: a) the producer-specific technology of ram-pumping b) the relative height HT of elevated tanks c) the workable fork of magnitudes in the amount of water QW to store in wetlands, and d) the exact technology of energy storage for hydroelectricity. These 4 decisions together form the first level of feasibility as regards the ‘Energy Ponds’ concept. They are essentially adaptive: they manifest the right choice for the given location, with its natural and social ecosystem.

A local installation of ‘Energy Ponds’ impacts the local environment at two levels, namely the retention of water, and the supply of energy. Water retained in wetlands has a beneficial impact on the ecosystem, yet it is not directly consumable: it needs to pass through the local system of supply in potable water first. The direct consumable generated by ‘Energy Ponds’ is hydroelectricity. Besides, there is some empirical evidence for a positive impact of wetlands upon the value of the adjacent, residential real estate (Mahan et al. 2000[40]; Tapsuwan et al. 2009[41]; Du & Huang 2018[42]). Thus comes the second level of feasibility for ‘Energy Ponds’, namely the socio-economic one. As ‘Energy Ponds’ is an early-stage concept, bearing significant uncertainty, the Net Present Value (NPV) of discounted cash flows seems suitable in that respect. Can the thing pay its own bills, and bring a surplus?

Answering that question connects once again to the basic hydrological metrics, namely head and flow. Hydroelectric power is calculated, in watts, as: water density (1000 kg/m3) * gravity acceleration constant (9,8 m/s2) * Net Head (meters) * Q (water flow rate m3/s). The output of electricity is derived from the power generated. It is safe to assume 50 weeks per year of continuous hydrogeneration, with the remaining time reserved for maintenance, which gives 50*7*24 = 8400 hours. Based on the previous formulations, power W generated in an installation of ‘Energy Ponds’ can be expressed with equation (4), and the resulting output E of electricity is given by equation (5):

W[kW] = ρ * g * (HRQC + HTQW) = 9,81 * (HRQC + HTQW)      (4)

E[kWh] = 8400 * W           (5)

The Net Present Value (NPV) of cash flow in an ‘Energy Ponds’ project is the residual part of revenue from the sales of electricity, as in equation (6).

The revenue is calculated as RE = PE*E , with PE standing for the price of electricity per 1 kWh. Investment outlays and the current costs of maintenance can be derived from the head and the flow specific to the given location. In that respect, the here-presented method, including parameters in equation (6), follows that by Hatata, El-Saadawi, & Saad (2019[43]). A realistic, technological lifecycle of an installation can be estimated at 12 years. Crossflow turbines seem optimal for flow rates inferior or equal to 20 m3 per second, whilst above 20 m3 Kaplan turbines look like the best choice. Investment and maintenance costs relative to ram pumps, elevated tanks, and the necessary piping remain largely uncertain, and seemingly idiosyncratic as regards the exact location and its physical characteristics. That methodological difficulty, seemingly inherent to the early conceptual phase of development in the ‘Energy Ponds’ concept, can be provisionally bypassed with the assumption that those hydraulic installations will consume the cost which would normally correspond to the diversion weir and intake, as well as to the cost of the powerhouse building. The variable IH corresponds to investment outlays in the strictly hydrological part of ‘Energy Ponds’ (i.e. ram-pumps, piping, and elevated tanks), whilst the ITU component, on the other hand, represents investment in the strictly spoken turbines and the adjacent power equipment (generator, electrical and mechanical auxiliary, transformer, and switchyard). The LCOS variable in equation (6) is the Levelized Cost of Storage, estimated for a discharge capacity of 6 hours in Li-Ion batteries, at €0,28 per 1 kWh (Salvini & Giovannelli 2022[44]; Chadly et al. 2022[45]). The ‘0,000714’ factor in equation (6) corresponds to the 6 hours of discharge as a fraction of the total 8400 working hours of the system over 1 year.

Case study with calculations

The here presented case study simulates the environmental and economic impact which could possibly come from the hypothetical development of the ‘Energy Ponds’ concept in author’s own country, namely Poland. The concept is simulated in the mouths of 32 Polish rivers, namely: Wisła, Odra, Warta, Narew, Bug, Noteć, San, Wieprz, Pilica, Bzura, Biebrza, Bóbr, Łyna, Drwęca, Barycz, Wkra, Gwda, Prosna, Dunajec, Brda, Pisa, Wisłoka, Nida, Nysa Kłodzka, Wisłok, Drawa, Krzna, Parsęta, Rega, Liwiec, Wełna, Nysa Łużycka (the spelling is original Polish). Flow rates, in cubic meters per second, as observed in those mouths, are taken as the quantitative basis for further calculations, and they are provided in Table 1, below. Figure 1, further below, presents the same graphically, on the map of Poland. The corresponding locations are marked with dark ovals. There are just 28 ovals on the map, as those located near Warsaw encompass more than one river mouth. That specific place in Poland is exceptionally dense in terms of fluvial network. Further in this section, one particular location is studied, namely the mouth of the Narew River, and it is marked on the map as a red oval.  

Table 1

River nameMouth opening on…Average flow rate [m3/s]River nameMouth opening on…Average flow rate [m3/s]
WisłaBaltic Sea1080ProsnaWarta17,4
OdraBaltic Sea567DunajecWisła85,5
WartaOdra216BrdaWisła28
NarewWisła313PisaNarew26,8
BugNarew155WisłokaWisła35,5
NotećWarta76,6NidaWisła21,1
SanWisła129Nysa KłodzkaOdra37,7
WieprzWisła36,4WisłokSan24,5
PilicaWisła47,4DrawaNoteć21,3
BzuraWisła28,6KrznaBug11,4
BiebrzaNarew35,3ParsętaBaltic Sea29,1
BóbrOdra44,8RegaBaltic Sea21,1
ŁynaPregoła[46]34,7LiwiecBug12,1
DrwęcaWisła30WełnaWarta9,2
BaryczOdra18,8Nysa ŁużyckaOdra31
WkraNarew22,3

Figure 2

The hypothetical location of Energy Ponds installations at the mouths of rivers is based on the availability of hydrological data, and more specifically the flow rate in cubic meters per second. That variable is being consistently observed at the mouths of rivers, for the most part, unless some specific research is conducted. Thus, locating the simulated Energy Ponds at the mouths of rivers is not a substantive choice. Real locations should be selected on the grounds of observable properties in the local ecosystem, mostly as regards the possibility of storing water in wetlands. 

The map of location chosen for this simulation goes pretty much across all the available fluvial valleys in Poland, the physical geography of which naturally makes rivers grow as they head North, and therefore the northern part of the country gives the most water to derive from rivers. Once again, when choosing real locations for the Energy Ponds installations, more elevated ground is a plausible location as well. Most of the brown ovals on the map are in the broad vicinity of cities. This is another feature of the Polish geography: high density of population, the latter being clearly concentrated along rivers. This is also an insight into the function of Energy Ponds in real life. Such as it is simulated in Poland, i.e. in a densely populated environment, it is a true challenge to balance environmental services provided by wetlands, on the one hand, and the need for agricultural land, on the other hand. The simulation allows guessing that Energy Ponds can give more to city dwellers than to those living in the countryside.  

Another important trait of this specific simulation for Energy Ponds is the fact that virtually all the locations on the map correspond to places where wetlands used to exist in the past, before being drained and dried for the needs of human settlements. Geological and hydrological conditions are naturally conducive to swamp and pond formation in these ecosystems. It is important to prevent any ideological take on these facts. The present article is far from pushing simplistic claims such as “nature is better than civilisation”. Still, the draining and drying of wetlands in the past happened in the context of technologies which did not really allow reliable construction amidst a wetland-type environment. Today, we dispose of a much better technological base, such as comfortable barge-based houses, for example. The question of cohabitation between wetlands and human habitat can be reconsidered productively.   

Three levels of impact upon the riverine ecosystem are simulated as three hypothetical percentages of adsorption from the river through ram pumping: 5% of the flow, 10% and 20%, where 20% corresponds to the maximum possible pump-out as regards environmental impact. With these assumptions, the complete hypothetical set of 32 installations would yield 5 163 231 600 m3 a year at 5% of adsorption, and, respectively 10 326 463 200 m3 and 20 652 926 400 m3 with the adsorption rates at 10% and 20%.  In 2020, the annual precipitations were around 201,8 billion of m3, which means the 32 hypothetical installations of Energy Ponds could recirculate from 2,5% to 10% of that total volume, and that, in turn, translates into a significant environmental impact. 

Let’s focus on one particular location in order to further understand the environmental impact: the place where the Narew River mouths into Vistula River, north of Warsaw. The town of Nowy Dwór Mazowiecki, population 28 615, is located right at this junction of rivers. With the average consumption of water at the level of households being around 33,7 m3 a year, that local population consumes annually some 964 326 m3 of water. The flow rate in the Narew River close to its mouth into Vistula is 313 m3 per second, which amounts to a total of 9 870 768 000,00 m3 a year. Adsorbing 5%, 10% or 20% from that total flow amounts to, respectively, 493 538 400 m3, 987 076 800 m3, and 1 974 153 600 m3. From another angle, the same annual consumption of water in households, in Nowy Dwór Mazowiecki, corresponds to 0,0098% of the annual waterflow in the river mouth. The ‘Energy Ponds’ concept would allow to recirculate easily into the surrounding ecosystem the entire annual household consumption of water in this one single town.           

Let’s stay in this specific location and translate water into energy, and further into investment. The first issue to treat is a workable approach to using the capacity of ram-pumps in that specific location, or, in other words, a realistic estimation of the total pumped volume QC + QW . Metric flow per second at the mouth of the Narew River is 313 m3 on average. It is out of the question to install ram-pumps across the entire width of the stream, i.e. some 300 metres on average, as Narew is a navigable river. Still, what if we replaced width with length, i.e. what if a row of ram-pumps was placed along one bank of the river, over a total length of 1 km? Then, with the average efficiency of ram-pumps pegged at 50,5%, it can be assumed that 50,5% of the total flow per second, thus 50,5%*313 m3/s = 158,065 m3/s would flow through ram-pumps. With the baseline head of the Narew River being 92 meters, Table 2 below offers an estimation of the electric power thus possible to generate at the mouth, in an installation of ‘Energy Ponds’, according to equation (4).

Table 2 – Electric power [kW] possible to generate in an installation of ‘Energy Ponds’ at the mouth of the Narew River, Poland.

 Percentage of the total flow to be stored in wetlands (QW)
The relative height of elevated tanks5%10%20%
10 meters130 067,65117 478,4892 300,13
20 meters131 602,92120 549,0198 441,19
30 meters133 138,18123 619,54104 582,25

Source: author’s

When the highest possible elevated tanks are chosen (30 m), combined with the lowest percentage of the flow retained in wetlands (5%), electric power generated is the greatest, i.e. 133,138 MW. The optimal point derives logically from natural conditions. Comparatively to the artificially elevated tanks and their 30 meters maximum, the head of the river itself (92 meters) is an overwhelming factor. An interesting aspect of the ‘Energy Ponds’ concept comes out: the most power can be derived from the natural denivelation of terrain, with elevated tanks and their Roman siphon being just an additional source of potential energy. Further calculations, as regards the necessary investment outlays and the cost of storage, demonstrate that the lowest investment per unit of useful power – 987,16 Polish Zloty (PLN) per 1 kW – is reached precisely at the same point. Comparatively, the lowest power – generated at 20% of the flow adsorbed into wetlands and the lowest height of 10 meters of elevated tanks – is connected to the highest investment per unit, namely 1 111,13 PLN per 1 kW.       

The local urban population in Nowy Dwór Mazowiecki represents an annual consumption of electricity amounting to 828 752 048 kWh, and electricity hypothetically generated at that greatest power amounts to 1 118 360 718,72 kWh a year, thus covering, with an overhead, the local needs. This output of energy would be currently worth PLN 845,48 million a year at the retail prices of electricity[47] (i.e. when sold in a local peer-to-peer market). Should it be sold at wholesale prices[48], to the national power grid, it would represent PLN 766,08 million annually. Corrected with an annual Levelized Cost of Storage estimated at 107 161,99 PLN for Li-Ion batteries, that stream of revenue gives a 12-year discounted present value of PLN 5 134 million at retail prices, and PLN 4 647 million at wholesale prices.  With investment outlays estimated, according to the method presented earlier in this article, at some PLN 131,43 million, the project seems largely profitable. As a matter of fact, it could reach a positive Net Present Value already on the first year.

Comparatively, at the point of lowest power and highest investment per unit thereof, thus at 20% of adsorption into wetlands and 10 meters of height in elevated tanks, the 12-year discounted stream of revenue corrected for LCOS would be PLN 3 555,3 million (retail) and PLN 3 217,8 million (wholesale), against an investment of PLN 102,56 million.                   

Conclusion

The above-presented case study in the hypothetical implementation of the ‘Energy Ponds’ concept sums up optimistically. Still, the ‘Energy Ponds’ is still just a concept, and the study of its possible feasibility is hypothetical. That suggests caution, and the need to take a devil’s advocate’s stance. The case study can be utilized for both outlining the positive potential in ‘Energy Ponds’ and showing the possible angles of stress-testing the concept. The real financial value and the real engineering difficulty of investment in the basic hydraulic infrastructure of ‘Energy Ponds’ has been just touched upon, and essentially bypassed with a few assumptions. Those assumptions seem to be holding when written down, but confrontation with real life can bring about unpredicted challenges. This superficiality stems from the author’s own limitations, as an economist attempting to form an essentially engineering solution. Still, even from the economic point of view, one factor of uncertainty emerges: the pace of technological change. The method used for this feasibility study is a textbook one, similar to calculating the Levelized Cost of Energy: there is an initial investment, which we spread over the expected lifecycle of the technology in question. However, technologies can change at an unexpected pace, and the actual lifecycle of an installation – especially a highly experimental one – might be much shorter than expected. In the author’s (very intuitive) perspective, technological uncertainty is a big pinch of salt to add to the results of the case study.

Another factor of uncertainty is the real possibility of restoring wetlands in densely populated areas. Whilst new technologies in construction and agriculture do allow better overlapping between wetlands, cities, and farming, this is still just overlapping. At the bottom line, wetlands objectively take land away from many other possible uses. Literature is far from decisive about solutions in that respect. The great majority of known projects in the restoration of wetlands aim at and end up in restoring wildlife, not in assuring smooth coexistence between nature and civilisation. Some serious socio-economic experimentation might be involved in projects such as ‘Energy Ponds’.

Hydrogeneration in ‘Energy Ponds’ belongs to the category of Distributed Energy Resources (DER). DER systems become more and more popular, across the world, and they prove to be workable solutions in very different conditions of physical geography (McIlwaine et al. 2021[49]). Connection to local markets of energy, and into local smart grids, seems critical for the optimization of DER systems (Zakeri et al. 2021[50]; Touzani et al. 2021[51]; Haider et al. 2021[52]; Zhang et al. 2022[53]). How necessary is the co-existence – or pre-existence – of such a local network for the economically successful deployment of ‘Energy Ponds’?  What happens when the local installation of ‘Energy Ponds’ is the only Distributed Energy Resource around?

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[46] The Pregoła river is located in the Russian Kaliningrad district, whilst much of the Łyna river is located in Poland. The hypothetical location of Energy Ponds is assumed to be in Poland, thus upstream from the mouth into Pregoła. 

[47] https://www.globalpetrolprices.com/Poland/electricity_prices/ last access May 17th, 2022

[48] https://www.statista.com/statistics/1066654/poland-wholesale-electricity-prices/ last access May 17th, 2022

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We keep going until we observe

I keep working on a proof-of-concept paper for my idea of ‘Energy Ponds’. In my last two updates, namely in ‘Seasonal lakes’, and in ‘Le Catch 22 dans ce jardin d’Eden’, I sort of refreshed my ideas and set the canvas for painting. Now, I start sketching. What exact concept do I want to prove, and what kind of evidence can possibly confirm (or discard) that concept? The idea I am working on has a few different layers. The most general vision is that of purposefully storing water in spongy structures akin to swamps or wetlands. These can bear various degree of artificial construction, and can stretch from natural wetlands, through semi-artificial ones, all the way to urban technologies such as rain gardens and sponge cities. The most general proof corresponding to that vision is a review of publicly available research – peer-reviewed papers, preprints, databases etc. – on that general topic.

Against that general landscape, I sketch two more specific concepts: the idea of using ram pumps as a technology of forced water retention, and the possibility of locating those wetland structures in the broadly spoken Northern Europe, thus my home region. Correspondingly, I need to provide two streams of scientific proof: a review of literature on the technology of ram pumping, on the one hand, and on the actual natural conditions, as well as land management policies in Europe, on the other hand.  I need to consider the environmental impact of creating new wetland-like structures in Northern Europe, as well as the socio-economic impact, and legal feasibility of conducting such projects.

Next, I sort of build upwards. I hypothesise a complex technology, where ram-pumped water from the river goes into a sort of light elevated tanks, and from there, using the principle of Roman siphon, cascades down into wetlands, and through a series of small hydro-electric turbines. Turbines generate electricity, which is being stored and then sold outside.

At that point, I have a technology of water retention coupled with a technology of energy generation and storage. I further advance a second hypothesis that such a complex technology will be economically sustainable based on the corresponding sales of electricity. In other words, I want to figure out a configuration of that technology, which will be suitable for communities which either don’t care at all, or simply cannot afford to care about the positive environmental impact of the solution proposed.

Proof of concept for those two hypotheses is going to be complex. First, I need to pass in review the available technologies for energy storage, energy generation, as well as for the construction of elevated tanks and Roman siphons. I need to take into account various technological mixes, including the incorporation of wind turbines and photovoltaic installation into the whole thing, in order to optimize the output of energy. I will try to look for documented examples of small hydro-generation coupled with wind and solar. Then, I have to rack the literature as regards mathematical models for the optimization of such power systems and put them against my own idea of reverse engineering back from the storage technology. I take the technology of energy storage which seems the most suitable for the local market of energy, and for the hypothetical charging from hydro-wind-solar mixed generation. I build a control scenario where that storage facility just buys energy at wholesale prices from the power grid and then resells it. Next, I configure the hydro-wind-solar generation so as to make it economically competitive against the supply of energy from the power grid.

Now, I sketch. I keep in mind the levels of conceptualization outlined above, and I quickly move through published science along that logical path, quickly picking a few articles for each topic. I am going to put those nonchalantly collected pieces of science back-to-back and see how and whether at all it all makes sense together. I start with Bortolini & Zanin (2019[1]), who study the impact of rain gardens on water management in cities of the Veneto region in Italy. Rain gardens are vegetal structures, set up in the urban environment, with the specific purpose to retain rainwater.  Bortolini & Zanin (2019 op. cit.) use a simplified water balance, where the rain garden absorbs and retains a volume ‘I’ of water (‘I’ stands for infiltration), which is the difference between precipitations on the one hand, and the sum total of overflowing runoff from the rain garden plus evapotranspiration of water, on the other hand. Soil and plants in the rain garden have a given top capacity to retain water. Green plants typically hold 80 – 95% of their mass in water, whilst trees hold about 50%. Soil is considered wet when it contains about 25% of water. The rain garden absorbs water from precipitations at a rate determined by hydraulic conductivity, which means the relative ease of a fluid (usually water) to move through pore spaces or fractures, and which depends on the intrinsic permeability of the material, the degree of saturation, and on the density and viscosity of the fluid.

As I look at it, I can see that the actual capacity of water retention in a rain garden can hardly be determined a priori, unless we have really a lot of empirical data from the given location. For a new location of a new rain garden, it is safe to assume that we need an experimental phase when we empirically assess the retentive capacity of the rain garden with different configurations of soil and vegetation used. That leads me to generalizing that any porous structure we use for retaining rainwater, would it be something like wetlands, or something like a rain garden in urban environment, has a natural constraint of hydraulic conductivity, and that constraint determines the percentage of precipitations, and the metric volume thereof, which the given structure can retain.

Bortolini & Zanin (2019 op. cit.) bring forth empirical results which suggest that properly designed rain gardens located on rooftops in a city can absorb from 87% to 93% of the total input of water they receive. Cool. I move on and towards the issue of water management in Europe, with a working paper by Fribourg-Blanc, B. (2018[2]), and the most important takeaway from that paper is that we have something called European Platform for Natural Water Retention Measures AKA http://nwrm.eu , and that thing have both good properties and bad properties. The good thing about http://nwrm.eu is that it contains loads of data and publications about projects in Natural Water Retention in Europe. The bad thing is that http://nwrm.eu is not a secure website. Another paper, by Tóth et al. (2017[3]) tells me that another analytical tool exists, namely the European Soil Hydraulic Database (EU‐ SoilHydroGrids ver1.0).

So far, so good. I already know there is data and science for evaluating, with acceptable precision, the optimal structure and the capacity for water retention in porous structures such as rain gardens or wetlands, in the European context. I move to the technology of ram pumps. I grab two papers: Guo et al. (2018[4]), and Li et al. (2021[5]). They show me two important things. Firstly, China seems to be burning the rubber in the field of ram pumping technology. Secondly, the greatest uncertainty as for that technology seems to be the actual height those ram pumps can elevate water at, or, when coupled with hydropower, the hydraulic head which ram pumps can create. Guo et al. (2018 op. cit.) claim that 50 meters of elevation is the maximum which is both feasible and efficient. Li et al. (2021 op. cit.) are sort of vertically more conservative and claim that the whole thing should be kept below 30 meters of elevation. Both are better than 20 meters, which is what I thought was the best one can expect. Greater elevation of water means greater hydraulic head, and more hydropower to be generated. It pays off to review literature.

Lots of uncertainty as for the actual capacity and efficiency of ram pumping means quick technological change in that domain. This is economically interesting. It means that investing in projects which involve ram pumping means investment in quickly changing a technology. That means both high hopes for an even better technology in immediate future, and high needs for cash in the balance sheet of the entities involved.

I move to the end-of-the-pipeline technology in my concept, namely to energy storage. I study a paper by Koohi-Fayegh & Rosen (2020[6]), which suggests two things. Firstly, for a standalone installation in renewable energy, whatever combination of small hydropower, photovoltaic and small wind turbines we think of, lithium-ion batteries are always a good idea for power storage, Secondly, when we work with hydrogeneration, thus when we have any hydraulic head to make electricity with, pumped storage comes sort of natural. That leads me to an idea which looks even crazier than what I have imagined so far: what if we create an elevated garden with strong capacity for water retention. Ram pumps take water from the river and pump it up onto elevated platforms with rain gardens on it. Those platforms can be optimized as for their absorption of sunlight and thus as regards their interaction with whatever is underneath them.  

I move to small hydro, and I find two papers, namely Couto & Olden (2018[7]), and Lange et al. (2018[8]), which are both interestingly critical as regards small hydropower installations. Lange et al. (2018 op. cit.) claim that the overall environmental impact of small hydro should be closely monitored. Couto & Olden (2018 op. cit.) go further and claim there is a ‘craze’ about small hydro, and that craze has already lead to overinvestment in the corresponding installations, which can be damaging both environmentally and economically (overinvestment means financial collapse of many projects). Those critical views in mind, I turn to another paper, by Zhou et al. (2019[9]), who approach the issue as a case for optimization, within a broader framework called ‘Water-Food-Energy’ Nexus, WFE for closer friends. This paper, just as a few others it cites (Ming et al. 2018[10]; Uen et al. 2018[11]), advocates for using artificial intelligence in order to optimize for WFE.

Zhou et al. (2019 op.cit.) set three hydrological scenarios for empirical research and simulation. The baseline scenario corresponds to an average hydrological year, with average water levels and average precipitations. Next to it are: a dry year and a wet year. The authors assume that the cost of installation in small hydropower is $600 per kW on average.  They simulate the use of two technologies for hydro-electric turbines: Pelton and Vortex. Pelton turbines are optimized paddled wheels, essentially, whilst the Vortex technology consists in creating, precisely, a vortex of water, and that vortex moves a rotor placed in the middle of it.

Zhou et al. (2019 op.cit.) create a multi-objective function to optimize, with the following desired outcomes:

>> Objective 1: maximize the reliability of water supply by minimizing the probability of real water shortage occurring.

>> Objective 2: maximize water storage given the capacity of the reservoir. Note: reservoir is understood hydrologically, as any structure, natural or artificial, able to retain water.

>> Objective 3: maximize the average annual output of small hydro-electric turbines

Those objectives are being achieved under the corresponding sets of constraints. For water supply those constraints all turn around water balance, whilst for energy output it is more about the engineering properties of the technologies taken into account. The three objectives are hierarchized. First, Zhou et al. (2019 op.cit.) perform an optimization regarding Objectives 1 and 2, thus in order to find the optimal hydrological characteristics to meet, and then, on the basis of these, they optimize the technology to put in place, as regards power output.

The general tool for optimization used by Zhou et al. (2019 op.cit.) is a genetic algorithm called NSGA-II, AKA Non-dominated Sorting Genetic Algorithm. Apparently, NSGA-II has a long and successful history of good track in engineering, including water management and energy (see e.g. Chang et al. 2016[12]; Jain & Sachdeva 2017[13];  Assaf & Shabani 2018[14]). I want to stop for a while here and have a good look at this specific algorithm. The logic of NSGA-II starts with creating an initial population of cases/situations/configurations etc. Each case is a combination of observations as regards the objectives to meet, and the actual values observed in constraining variables, e.g. precipitations for water balance or hydraulic head for the output of hydropower. In the conventional lingo of this algorithm, those cases are called chromosomes. Yes, I know, a hydro-electric turbine placed in the context of water management hardly looks like a chromosome, but it is a genetic algorithm, and it just sounds fancy to use that biologically marked vocabulary.

As for me, I like staying close to real life, and therefore I call those cases solutions rather than chromosomes. Anyway, the underlying math is the same. Once I have that initial population of real-life solutions, I calculate two parameters for each of them: their rank as regards the objectives to maximize, and their so-called ‘crowded distance’. Ranking is done with the procedure of fast non-dominated sorting. It is a comparison in pairs, where the solution A dominates another solution B, if and only if there is no objective of A worse than that objective of B and there is at least one objective of A better than that objective of B. The solution which scores the most wins in such peer-to-peer comparisons is at the top of the ranking, the one with the second score of wins is the second etc. Crowding distance is essentially the same as what I call coefficient of coherence in my own research: Euclidean distance (or other mathematical distance) is calculated for each pair of solutions. As a result, each solution is associated with k Euclidean distances to the k remaining solutions, which can be reduced to an average distance, i.e. the crowded distance.

In the next step, an off-spring population is produced from that original population of solutions. It is created by taking relatively the fittest solutions from the initial population, recombining their characteristics in a 50/50 proportion, and adding them some capacity for endogenous mutation. Two out of these three genetic functions are de facto controlled. We choose relatively the fittest by establishing some kind of threshold for fitness, as regards the objectives pursued. It can be a required minimum, a quantile (e.g. the third quartile), or an average. In the first case, we arbitrarily impose a scale of fitness on our population, whilst in the latter two the hierarchy of fitness is generated endogenously from the population of solutions observed. Fitness can have shades and grades, by weighing the score in non-dominated sorting, thus the number of wins over other solutions, on the one hand, and the crowded distance on the other hand. In other words, we can go for solutions which have a lot of similar ones in the population (i.e. which have a low average crowded distance), or, conversely, we can privilege lone wolves, with a high average Euclidean distance from anything else on the plate.  

The capacity for endogenous mutation means that we can allow variance in all or in just the selected variables which make each solution. The number of degrees of freedom we allow in each variable dictates the number of mutations that can be created. Once again, discreet power is given to the analyst: we can choose the genetic traits which can mutate and we can determine their freedom to mutate. In an engineering problem, technological and environmental constraints should normally put a cap on the capacity for mutation. Still, we can think about an algorithm which definitely kicks the lid off the barrel of reality, and which generates mutations in the wildest registers of variables considered. It is a way to simulate a process when the presence of strong outliers has a strong impact on the whole population.

The same discreet cap on the freedom to evolve is to be found when we repeat the process. The offspring generation of solutions goes essentially through the same process as the initial one, to produce further offspring: ranking by non-dominated sorting and crowded distance, selection of the fittest, recombination, and endogenous mutation. At the starting point of this process, we can be two alternative versions of the Mother Nature. We can be a mean Mother Nature, and we shave off from the offspring population all those baby-solutions which do not meet the initial constraints, e.g. zero supply of water in this specific case. On the other hand, we can be even meaner a Mother Nature and allow those strange, dysfunctional mutants to keep going and see what happens to the whole species after a few rounds of genetic reproduction.

With each generation, we compute an average crowded distance between all the solutions created, i.e. we check how diverse is the species in this generation. As long as diversity grows or remains constant, we assume that the divergence between the solutions generated grows or stays the same. Similarly, we can compute an even more general crowded distance between each pair of generations, and therefore to assess how far has the current generation gone from the parent one. We keep going until we observe that the intra-generational crowded distance and the inter-generational one start narrowing down asymptotically to zero. In other words, we consider resuming evolution when solutions in the game become highly similar to each other and when genetic change stops bringing significant functional change.

Cool. When I want to optimize my concept of Energy Ponds, I need to add the objective of constrained return on investment, based on the sales of electricity. In comparison to Zhou et al. (2019 op.cit.), I need to add a third level of selection. I start with selecting environmentally the solutions which make sense in terms of water management. In the next step, I produce a range of solutions which assure the greatest output of power, in a possible mix with solar and wind. Then I take those and filter them through the NSGA-II procedure as regards their capacity to sustain themselves financially. Mind you, I can shake it off a bit by fusing together those levels of selection. I can simulate extreme cases, when, for example, good economic sustainability becomes an environmental problem. Still, it would be rather theoretical. In Europe, non-compliance with environmental requirements makes a project a non-starter per se: you just can get the necessary permits if your hydropower project messes with hydrological constraints legally imposed on the given location.     

Cool. It all starts making sense. There is apparently a lot of stir in the technology of making semi-artificial structures for retaining water, such as rain gardens and wetlands. That means a lot of experimentation, and that experimentation can be guided and optimized by testing the fitness of alternative solutions for meeting objectives of water management, power output and economic sustainability. I have some starting data, to produce the initial generation of solutions, and then try to optimize them with an algorithm such as NSGA-II.

[1] Bortolini, L., & Zanin, G. (2019). Reprint of: Hydrological behaviour of rain gardens and plant suitability: A study in the Veneto plain (north-eastern Italy) conditions. Urban forestry & urban greening, 37, 74-86. https://doi.org/10.1016/j.ufug.2018.07.003

[2] Fribourg-Blanc, B. (2018, April). Natural Water Retention Measures (NWRM), a tool to manage hydrological issues in Europe?. In EGU General Assembly Conference Abstracts (p. 19043). https://ui.adsabs.harvard.edu/abs/2018EGUGA..2019043F/abstract

[3] Tóth, B., Weynants, M., Pásztor, L., & Hengl, T. (2017). 3D soil hydraulic database of Europe at 250 m resolution. Hydrological Processes, 31(14), 2662-2666. https://onlinelibrary.wiley.com/doi/pdf/10.1002/hyp.11203

[4] Guo, X., Li, J., Yang, K., Fu, H., Wang, T., Guo, Y., … & Huang, W. (2018). Optimal design and performance analysis of hydraulic ram pump system. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 232(7), 841-855. https://doi.org/10.1177%2F0957650918756761

[5] Li, J., Yang, K., Guo, X., Huang, W., Wang, T., Guo, Y., & Fu, H. (2021). Structural design and parameter optimization on a waste valve for hydraulic ram pumps. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 235(4), 747–765. https://doi.org/10.1177/0957650920967489

[6] Koohi-Fayegh, S., & Rosen, M. A. (2020). A review of energy storage types, applications and recent developments. Journal of Energy Storage, 27, 101047. https://doi.org/10.1016/j.est.2019.101047

[7] Couto, T. B., & Olden, J. D. (2018). Global proliferation of small hydropower plants–science and policy. Frontiers in Ecology and the Environment, 16(2), 91-100. https://doi.org/10.1002/fee.1746

[8] Lange, K., Meier, P., Trautwein, C., Schmid, M., Robinson, C. T., Weber, C., & Brodersen, J. (2018). Basin‐scale effects of small hydropower on biodiversity dynamics. Frontiers in Ecology and the Environment, 16(7), 397-404.  https://doi.org/10.1002/fee.1823

[9] Zhou, Y., Chang, L. C., Uen, T. S., Guo, S., Xu, C. Y., & Chang, F. J. (2019). Prospect for small-hydropower installation settled upon optimal water allocation: An action to stimulate synergies of water-food-energy nexus. Applied Energy, 238, 668-682. https://doi.org/10.1016/j.apenergy.2019.01.069

[10] Ming, B., Liu, P., Cheng, L., Zhou, Y., & Wang, X. (2018). Optimal daily generation scheduling of large hydro–photovoltaic hybrid power plants. Energy Conversion and Management, 171, 528-540. https://doi.org/10.1016/j.enconman.2018.06.001

[11] Uen, T. S., Chang, F. J., Zhou, Y., & Tsai, W. P. (2018). Exploring synergistic benefits of Water-Food-Energy Nexus through multi-objective reservoir optimization schemes. Science of the Total Environment, 633, 341-351. https://doi.org/10.1016/j.scitotenv.2018.03.172

[12] Chang, F. J., Wang, Y. C., & Tsai, W. P. (2016). Modelling intelligent water resources allocation for multi-users. Water resources management, 30(4), 1395-1413. https://doi.org/10.1007/s11269-016-1229-6

[13] Jain, V., & Sachdeva, G. (2017). Energy, exergy, economic (3E) analyses and multi-objective optimization of vapor absorption heat transformer using NSGA-II technique. Energy Conversion and Management, 148, 1096-1113. https://doi.org/10.1016/j.enconman.2017.06.055

[14] Assaf, J., & Shabani, B. (2018). Multi-objective sizing optimisation of a solar-thermal system integrated with a solar-hydrogen combined heat and power system, using genetic algorithm. Energy Conversion and Management, 164, 518-532. https://doi.org/10.1016/j.enconman.2018.03.026

Cautiously bon-vivant

I keep developing on a few topics in parallel, with a special focus on two of them. Lessons in economics and management which I can derive for my students, out of my personal experience as a small investor in the stock market, for one, and a broader, scientific work on the civilizational role of cities and our human collective intelligence, for two.

I like starting with the observation of real life, and I like ending with it as well. What I see around gives me the initial incentive to do research and makes the last pitch for testing my findings and intuitions. In my personal experience as investor, I have simply confirmed an initial intuition that giving a written, consistent and public account thereof helps me nailing down efficient strategies as an investor. As regards cities and collective intelligence, the first part of that topic comes from observing changes in urban life since COVID-19 broke out, and the second part is just a generalized, though mild an intellectual obsession, which I started developing once I observed the way artificial neural networks work.

In this update, I want to develop on two specific points, connected to those two paths of research and writing. As far as my investment is concerned, I am seriously entertaining the idea of broadening my investment portfolio in the sector of renewable energies, more specifically in the photovoltaic. I can notice a rush on the solar business in the U.S. I am thinking about investing in some of those shares. I already have, and have made a nice profit on the stock of First Solar (https://investor.firstsolar.com/home/default.aspx ) as well as on that of SMA Solar (https://www.sma.de/en/investor-relations/overview.html ). Currently, I am observing three other companies: Vivint Solar (https://investors.vivintsolar.com/company/investors/investors-overview/default.aspx ),  Canadian Solar (http://investors.canadiansolar.com/investor-relations ), and SolarEdge Technologies (https://investors.solaredge.com/investor-overview ). Below, I am placing the graphs of stock price over the last year, as regards those solar businesses. There is something like a common trend in those stock prices. March and April 2020 were a moment of brief jump upwards, which subsequently turned into a shy lie-down, and since the beginning of August 2020 another journey into the realm of investors’ keen interest seems to be on the way.

Before you have a look at the graphs, here is a summary table with selected financials, approached as relative gradients of change, or d(x).

 Change from 01/01/2020 to 31/08/2020
Companyd(market cap)d(assets)d(operational cash-flow)
First Solar+23,9%-6%Deeper negative: – $80 million
SMA Solar+27,5%-10%Deeper negative: -€40 million
Vivint Solar+362%+11%Deeper negative: – $9 million
SolarEdge+98%0+ $50 million
Canadian Solar+41%+4%+ $90 million

There are two fundamental traits of business models which I am having a close look at. Firstly, it is the correlation between changes in market capitalization, and changes in assets. I am checking if the solar businesses I want to invest in have their capital base functionally connected to the financial market. Looks a bit wobbly, as for now. Secondly, I look at current operational efficiency, measured with operational cash flow. Here, I can see there is still a lot to do. Here is the link to You Tube video with all that topic developed: Business models in renewable energies #3 Solar business and investment opportunities [Renew BM 3 2020-09-06 09-20-30 ; https://youtu.be/wYkW5KHQlDg ].

Those business models seem to be in a phase of slow stabilization. The industry as a whole seems to be slowly figuring out the right way of running that PV show, however the truly efficient scheme is still to be nailed down. Investment in those companies is based on reasonable trust in the growth of their market, and in the positive impact of technological innovation. Question: is it a good move to invest now? Answer: it is risky, but acceptably rational; once those business models become really efficient, the industry will be in or close to the phase of maturity, which, in turn, does not really allow expecting abnormally high return on investment.  

This is a very ‘financial’, hands-off approach to business models. In this case, business models of those photovoltaic businesses matter to me just to the extent of being fundamentally predictable. I don’t want to run a solar business, I just want to have elementary understanding of what’s going on, business-wise, to make my investment better grounded. Looking from inside a business, such an approach is informative about the way that a business model should ‘speak’ to investors.

At the end of the day, I think I am most likely to invest in SolarEdge. It seems to have all the LEGO blocks in place for a good opening. Good cash flow, although a bit sluggish when it comes to real investment.

As regards COVID-19 and cities, I am formulating the following hypothesis: COVID-19 has awakened some deeply rooted cultural patterns, which date back to the times of high epidemic risk, long before vaccines, sanitation and widespread basic healthcare. Those patterns involve less spatial mobility in the population, and social interactions within relatively steady social circles of knowingly healthy people. As a result, the overall frequency of social interactions in cities is likely to decrease, and, as a contingent result, the formation of new social roles is likely to slow down. Then, either digital technologies take over the function of direct social interactions and new social roles will be shaping themselves via your average smartphone, with all the apps it is blessed (haunted?) with, or the formation of new social roles will slow down in general. In that last case, we could have hard times with keeping up our pace of technological change. Here is the link to You Tube video which summarizes what is written below: Urban Economics and City Management #4 COVID and social mobility in cities [ Cities 4 2020-09-06 09-43-06 ; https://youtu.be/m3FZvsscw7A  ].

I want to gain some insight into the epidemiological angle of that claim, and I am passing in review some recent literature. I start with: Gatto, M., Bertuzzo, E., Mari, L., Miccoli, S., Carraro, L., Casagrandi, R., & Rinaldo, A. (2020). Spread and dynamics of the COVID-19 epidemic in Italy: Effects of emergency containment measures. Proceedings of the National Academy of Sciences, 117(19), 10484-10491 (https://www.pnas.org/content/pnas/117/19/10484.full.pdf ). As it is usually the case, my internal curious ape starts paying attention to details which could come as secondary for other people, and my internal happy bulldog follows along and bites deep into those details. The little detail in this specific paper is a parameter: the number of people quarantined as a percentage of those positively diagnosed with Sars-Cov-2. In the model developed by Gatto et al., that parameter is kept constant at 40%, which is, apparently, the average level empirically observed in Italy during the Spring 2020 outbreak. Quarantine is strict isolation between carriers and (supposedly) non-carriers of the virus. Quarantine can be placed on the same scale as basic social distancing. It is just stricter, and, in quantitative terms, it drives much lower the likelihood of infectious social interaction. Gatto el al. insist that testing effort and quarantining are essential components of collective defence against the epidemic. I generalize: testing and quarantine are patterns of collective behaviour. I check whether people around me are carriers or not, and then I split them into two categories: those whom I strongly suspect to host and transmit Sars-Cov-2, and all the rest. I define two patterns of social interaction with those two groups: very restrictive with the former, and cautiously bon vivant with the others (still, no hugging). As the technologies of testing will be inevitably diffusing across the social landscape, that structured pattern is likely to spread as well.    

Now, I pay a short intellectual visit to Jiang, P., Fu, X., Van Fan, Y., Klemeš, J. J., Chen, P., Ma, S., & Zhang, W. (2020). Spatial-temporal potential exposure risk analytics and urban sustainability impacts related to COVID-19 mitigation: A perspective from car mobility behaviour. Journal of Cleaner Production, 123673 https://doi.org/10.1016/j.jclepro.2020.123673 . Their methodology is based on correlating spatial mobility of cars in residential areas of Singapore with the risk of infection with COVID-19. A 44,3% ÷ 55,4% decrease in the spatial mobility of cars is correlated with a 72% decrease in the risk of social transmission of the virus. I intuitively translate it into geometrical patterns. Lower mobility in cars means a shorter average radius of travel by the means of available urban transportation. In the presence of epidemic risk, people move across a smaller average territory.

In another paper (or rather in a commented dataset), namely in Pepe, E., Bajardi, P., Gauvin, L., Privitera, F., Lake, B., Cattuto, C., & Tizzoni, M. (2020). COVID-19 outbreak response, a dataset to assess mobility changes in Italy following national lockdown. Scientific data, 7(1), 1-7. https://www.nature.com/articles/s41597-020-00575-2.pdf?origin=ppub , I find an enlarged catalogue of metrics pertinent to spatial mobility. That paper, in turn, lead me to the functionality run by Google: https://www.google.com/covid19/mobility/ . I went through all of it a bit cursorily, and I noticed two things. First of all, countries are strongly idiosyncratic in their social response to the pandemic. Still, and second of all, there are common denominators across idiosyncrasies and the most visible one is cyclicality. Each society seems to have been experimenting with the spatial mobility they can afford and sustain in the presence of epidemic risk. There is a cycle experimentation, around 3 – 4 weeks. Experimentation means learning and learning usually leads to durable behavioural change. In other words, we (I mean, homo sapiens) are currently learning, with the pandemic, new ways of being together, and those ways are likely to incrust themselves into our social structures.    

The article by Kraemer, M. U., Yang, C. H., Gutierrez, B., Wu, C. H., Klein, B., Pigott, D. M., … & Brownstein, J. S. (2020). The effect of human mobility and control measures on the COVID-19 epidemic in China. Science, 368(6490), 493-497 (https://science.sciencemag.org/content/368/6490/493 ) shows that without any restrictions in place, the spatial distribution of COVID-19 cases is strongly correlated with spatial mobility of people. With restrictions in place, that correlation can be curbed, however it is impossible to drive down to zero. In plain human, it means that even as stringent lockdowns as we could see in China cannot reduce spatial mobility to a level which would completely prevent the spread of the virus. 

By the way, in Gao, S., Rao, J., Kang, Y., Liang, Y., & Kruse, J. (2020). Mapping county-level mobility pattern changes in the United States in response to COVID-19. SIGSPATIAL Special, 12(1), 16-26 (https://arxiv.org/pdf/2004.04544.pdf ), I read that the whole idea of tracking spatial mobility with people’s personal smartphones largely backfired because the GDS transponders, installed in the average phone, have around 20 metres of horizontal error, on average, and are easily blurred when people gather in one place. Still, whilst the idea went down the drain as regards individual tracking of mobility, smartphone data seems to provide reliable data for observing entire clusters of people, and the way those clusters flow across space. You can consult Jia, J. S., Lu, X., Yuan, Y., Xu, G., Jia, J., & Christakis, N. A. (2020). Population flow drives spatio-temporal distribution of COVID-19 in China. Nature, 1-5.  (https://www.nature.com/articles/s41586-020-2284-y?sf233344559=1) .

Bonaccorsi, G., Pierri, F., Cinelli, M., Flori, A., Galeazzi, A., Porcelli, F., … & Pammolli, F. (2020). Economic and social consequences of human mobility restrictions under COVID-19. Proceedings of the National Academy of Sciences, 117(27), 15530-15535 (https://www.pnas.org/content/pnas/117/27/15530.full.pdf ) show an interesting economic aspect of the pandemic. Restrictions in mobility give the strongest economic blow to the poorest people and to local communities marked by relatively the greatest economic inequalities. Restrictions imposed by governments are one thing, and self-imposed limitations in spatial mobility are another. If my intuition is correct, namely that we will be spontaneously modifying and generally limiting our social interactions, in order to protect ourselves from COVID-19, those changes are likely to be the fastest and the deepest in high-income, low-inequality communities. As income decreases and inequality rises, those adaptive behavioural modifications are likely to weaken.

As I am drawing a provisional bottom line under that handful of scientific papers, my initial hypothesis seems to hold. We do modify, as a species, our social patterns, towards more encapsulated social circles. There is a process of learning taking place, and there is no mistake about it. That process of learning involves a downwards recalibration in the average territory of activity, and smart selection of people whom we hang out with, based on what we know about the epidemic risk they convey. This is a process of learning by trial and error, and it is locally idiosyncratic. Idiosyncrasies seem to be somehow correlated with differences in wealth. Income and accumulated capital visibly give local communities an additional edge in the adaptive learning. On the long run, economic resilience seems to be a key factor in successful adaptation to epidemic risk.

Just to end up with, here you have an educational piece as regards Business models in the Media Industry #4 The gaming business[ Media BM 4 2020-09-02 10-42-44; https://youtu.be/KCzCicDE8pc]. I study the case of CD Projekt (https://www.cdprojekt.com/en/investors/ ), a Polish gaming company, known mostly for ‘The Witcher’ game and currently working on the next one, Cyberpunk, with Keanu Reeves giving his face to the hero. I discover a strange business model, which obviously has hard times to connect with the creative process at the operational level. As strange as it might seem, the main investment activity, for the moment, consists in terminating and initiating cash bank deposits (!), and one of the most important operational activities is to push further in time the moment of officially charging customers with some economically due receivables. On the top of all that, those revenues deferred into the future are officially written in the balance sheet as short-term liabilities, which CD Projekt owes to…whom exactly?   

Time to come to the ad rem

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
[a] [b] [c] [d] [e] [f] [g]
2001 616 41,5% 1266 38,5% 369 29,1%
2002 599 37,8% 1294 38,3% 478 27,0%
2003 645 37,6% 1491 40,0% 645 27,0%
2004 806 40,7% 1703 40,7% 961 29,9%
2005 821 41,7% 1744 38,8% 1047 25,7%
2006 937 44,1% 1999 39,4% 1553 27,6%
2007 960 40,0% 2150 38,4% 1844 27,3%
2008 1224 44,5% 2454 39,4% 2342 26,7%
2009 1445 45,4% 2813 40,2% 2497 22,8%
2010 1746 46,6% 3482 42,4% 3298 24,8%
2011 2006 44,9% 3622 39,3% 4139 23,1%
2012 1886 42,1% 3699 39,0% 4551 20,6%
2013 1781 41,8% 3829 39,2% 5307 20,2%
2014 1800 38,8% 4074 40,4% 5740 18,1%
2015 1867 42,5% 4013 40,2% 7870 19,6%
2016 1089 39,8% 3388 40,6% 9325 20,4%
2017 349 42,9% 2115 42,7% 9321 22,3%

 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.

Table 2

  Share of renewable energy in the total consumption of energy
Year European Union United States China
2001 7,9% 4,7% 28,5%
2002 7,9% 4,8% 27,1%
2003 8,2% 5,3% 23,9%
2004 8,4% 5,5% 20,2%
2005 8,8% 5,8% 18,2%
2006 9,4% 6,4% 17,1%
2007 10,3% 6,3% 15,3%
2008 11,0% 6,8% 14,6%
2009 12,2% 7,4% 13,9%
2010 13,0% 7,5% 12,9%
2011 13,3% 8,2% 11,7%
2012 14,5% 8,5% 12,0%
2013 15,3% 8,7% 11,8%
2014 16,2% 8,8% 12,2%
2015 16,6% 8,7% 12,4%

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
    [I] [II] [III]
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.

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. 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 versionas 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 pageand 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?

 

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At the frontier, with my numbers

And so I am working on two business concepts in parallel. One of them is EneFin, my own idea of a FinTech utility in the market of energy, with a special focus on promoting the development of new, local providers in renewable energies. The other is MedUs, a concept I am developing together with a former student of mine, and this one consists in creating an online platform for managing healthcare services, as well as patients’ medical records, in the out-of-pocket market of medical services.

The basic concept of EneFinis to combine trade in futures contracts on retail supply of electricity, with trade in participatory deeds in the providers of said electricity. My sort of idée fixeis to create a FinTech utility that allows, in turn, creating local networks of energy production and distribution as cooperative structures, where the end-users of energy are, in the same time, shareholders in the local power installations. I want to use FinTech tools in order to extract all the advantages of a cooperative structure (low barriers to entry for new projects and investors, low prices of energy) with those of a typically capitalist one (high liquidity and adaptability).

After a cursory review of the available options in terms of legal and financial schemes (see Traps and loopholesas well as Les séquences, ça me pousse à poser cette sorte des questions), I came up with two provisional conclusions. Firstly, a crypto-currency, internal to EneFin looks like the best way of organising smooth trade in both the futures contracts on energy and the participatory shares in the energy providers. Secondly, the whole business has better chances to survive and thrive if the essential concept of EneFin is being offered to users as a set of specific options in an otherwise much broader trading platform.

EneFin as a business in itself can make profits on trading fees strictly spoken, like a percentage on every transaction, still, if the underlying technological platform develops really well, EneFin could grow an engineering branch, supplying that technology in itself to other organizations. This is an option to take into account in any business with ‘tech’ in its description.

MedUs, on the other hand, is based on the idea that the strictly spoken medical services, I mean the out-of-pocket paid ones, tend to be quite chaotic, at least in the context of European markets. In Europe, most healthcare is being financed via public pooled funds, accompanied by private pooled funds (or via network structures that operate de facto as pooled funds). The out-of-pocket paid healthcare is frequently an emergency or a luxury, usually not the bulk of medical care we use. Medical records generated in the out-of-pocket healthcare are technically there (each doctor has to create a file for a patient, even for one visit), and yet they have sort of a nebular structure: it is bloody hell of a nightmare to recreate your personal, medical history out of these.

The basic concept of MedUs consists in using Blockchain technology in order to create a dynamic ledger medical records. Blockchain acts as an archive in itself, very resilient to unlawful modifications. If my otherwise a bit accidental, dispersed medical visits, paid in the out-of-pocket system, are being arranged and paid via a Blockchain-based platform, it is possible to attach a ledger of medical records to the strictly spoken ledger of transactions. I say ‘possible’ because in that nascent business we still don’t have a clear idea of technological feasibility: Blockchain is cool in simple semantic structures, like cryptocurrencies, but becomes really consuming, in terms of energy and disk-space, if we want to handle large, complex sets of data.

MedUs, as we see it now, is supposed to earn money in three essential ways: a) through trading visit-coupons for private healthcare (i.e. coupons that serve to pay for medical care), in the form of coupons strictly spoken or of a cryptocurrency b) through running a closed platform accessible to medical providers after they pay for the initial software package and a monthly, participatory fee, and c) as a provider of the technology of creating local structures in (a) and (b). I can also see a possible carryover from the EneFin concept to MedUs: new, local providers of healthcare could sell their participatory shares to patients together with those visit-coupons, and thus create cooperative structures in local markets.

In this update I am focusing on one specific issue regarding both concepts, namely on the basic, quantitative market research, which I understand as the study of prices and quantities. My point is that you have two fundamental strategies of developing a new business. Your business can grow as your market grows, for one. That’s the classical approach, to find, for example, with Adam Smith. Still, there are businesses which flourish in slowly dying markets. The market of oil is a good example: there is no prospects for big growth, this is certain, and yet there are companies that still make profits in oil.

In a few past updates, I took something like a cursory set of 13 European countries and I calculated their various, quantitative attributes regarding EneFinand the European market of energy. These countries are: Austria, Switzerland, Czech Republic, Germany, Spain, Estonia, Finland, France, United Kingdom, Netherlands, Norway, Poland, Portugal. I am going to keep my focus on this set of countries and run a comparative market research, in terms of basic prices and quantities, for both concepts (i.e. EneFin and MedUS) together.

Now, I will try to move forward along that narrow crest that separates educational content from strictly spoken market research for business purposes. I want this blog to be educational, so I am going to give some methodological explanations as I run my quantitative analysis, and yet, in the same time, I want material, analytical progress for both business plans. Thus, here we go.

Both concepts address a similar relation suppliers and their customers. Households are the target customers in both cases. As for EneFin, the category of ‘households’ is a bit more flexible: it can encompass small businesses, small local NGOs, and farms as well. Still, in both of those business concepts populationis the most fundamental metric for measuring quantities. I usually reach to the demographics published by the World Bank: this source is quick to dig info out of it (I mean the interface is handy), and, as far as I know, it is reliable. I am a big fan of using demographics in market research, by the way: they can tell us much more than it superficially appears.

Demographic data from the World Bank covers the window since 1960 through 2016. Quantitative market research is about dynamics in time, as well as about cross-sectional differences. Here below, in Table 1, there is a bit of demographic info about my 13 countries:

Table 1 – Demographic analysis

Country Population headcount in 2016 Demographic growth since 1960 through 2016
Austria 8 747 358 24,1%
Switzerland 8 372 098 57,1%
Czech Republic 10 561 633 10,0%
Germany 82 667 685 13,5%
Spain 46 443 959 52,5%
Estonia 1 316 481 8,7%
Finland 5 495 096 24,1%
France 66 896 109 42,9%
United Kingdom 65 637 239 25,3%
Netherlands 17 018 408 48,2%
Norway 5 232 929 46,1%
Poland 37 948 016 28,0%
Portugal 10 324 611 16,6%
Total 366 661 622 29,3%

Good, now what do those demographics tell? In am interested in growth rates in the first place. Anyone who knows at least a little about the demographics of Europe can intuitively grasp the difference between, let’s say, the headcount of Switzerland as compared to that of Germany. On the other hand, growth rates are less intuitive. I start from the bottom line, i.e. from that compound rate of demographic growth in all the 13 countries taken together. It is 29,3% since 1960 through 2016, which makes a CAGR (Compound Annual Growth Rate) equal to CAGR = 29,9% / (2016 – 1959) = 0,51%. Nothing to write home about, really. The whole sample of 13 countries makes quite a placid demographic environment. Yet, the overall placidity is subject to strong cross-sectional disparities. Some countries, like Switzerland, or Spain, display strong demographic growth, whilst others are like really placid in that respect, e.g. Germany.

How does it matter? Good question. If each consecutive generation has a bigger headcount than the preceding one, in each such consecutive generations new social roles are likely to form. The faster the headcount grows, the more pronounced is that aspect of social change. On the other hand, we are talking about populations that grow (or not really) in constant territories. More people in a constant space means greater a density of population, which, in turn, means more social interactions and more learning in one unit of time. Summing up, the rate of demographic growth is one of those (rare) quantitative indicators that reflect true structural change.

Now, we can go a bit wild in our thinking and do something I call ‘social physics’. An elephant running at 10 km per hour represents greater a kinetic energy than a dog running at the same speed. Size matters, and speed matters. The size of the population, combined with its growth rate, makes something like a social force. Below, I am presenting a graph, which, I hope, expresses this line of thinking. In that graph, you can see a structure, where a core of 5 countries (Austria, Finland, Estonia, Czech Republic, and Portugal) sort of huddles against the origin of the manifold, whilst another set of countries sort of maxes out along some kind of frontier, enveloping the edges of the distribution. These max-outs are France and Spain, in the first place, followed by Switzerland and Netherlands on the side of growth, as well as by Germany and UK on the side of numerical size.

Some social phenomena behave like that, i.e. like a subset of frontier cases, clearly differentiating themselves from the subset of core cases. Usually, the best business is to be made at the frontier. Mind you, the entities of such a frontier analysis do not need to be countries: they can be products, business concepts, regions, segments of customers. Whatever differs by absolute size and its rate of change can be observed like that.

Demogr13_1 

My little demographic analysis shows me that whichever of the two projects I think about – EneFin or MedUs – sheer demographics make some countries (the frontier cases) in my set of 13 clearly better markets than others. After demographics, I turn towards metrics pertinent to energy in general, renewable energies, and to the out-of-pocket market in healthcare. I am going to apply consistently that frontier-of-size-versus-growth-rate approach you could see at work in the case of demographic data. Let’s see where it leads me.

As for energy, I start with a classic, namely the final consumption of energy per capita, as published by the World Bank. This metric is given in kg of oil equivalent per person per year. You want to convert it into kilowatt hours, like in electricity? Just multiply it by 11,63. Anyway, I take a pinch of that metric, just enough for those 13 countries, and I multiply it by another one, i.e. by the percentage share of renewable energies in that final consumption, also from the website of the World Bank. I stir both of these with the already measured population, and I have like: final consumption of energy per capita * share of renewable energies * population headcount = total final consumption of renewable energies [tons of oil equivalent per year].

Table 2, below, summarizes the results of that little arithmetical rummaging. Is there another frontier? Hell, yes. Germany and United Kingdom are the clear frontier cases. Looks like whatever anyone would like to do with renewable energies, in that set of 13 countries, Germany and UK are THE markets to go.

Table 2 – National markets of renewable energies

Country Final consumption of renewable energies in 2015, tons of oil equivalent Final consumption of renewable energies, compound growth rate 1990 – 2015
Austria 11 296 981,38 80,7%
Switzerland 6 200 709,18 48,6%
Czech Republic 6 036 384,16 241,0%
Germany 44 301 158,29 501,2%
Spain 19 412 734,75 104,4%
Estonia 1 508 374,57 359,5%
Finland 14 036 145,55 101,8%
France 33 167 337,48 42,3%
United Kingdom 15 682 329,72 1069,6%
Netherlands 4 223 183,03 434,9%
Norway 17 433 243,73 39,8%
Poland 11 267 553,99 336,8%
Portugal 5 996 364,89 32,6%

 Good, time to turn my focus to the other project: MedUs. I take a metric available with the World Health Organization, namely ‘Out-of-Pocket Expenditure (OOPS) per Capita in PPP Int$ constant 2010’.  Before I introduce the data, a bit of my beloved lecturing about what it means. So, ‘PPP’ stands for purchasing power parity. You take a standard basket of goods that most people buy, in the amounts they buy it per year, and you measure the value of that basket, in local currencies of each country, at local prices. You take the coefficient of national income per capita in the given country, and you divide it by the monetary value of that basket. It tells you how many such baskets can your average caput(Latin singular from the plural ‘capita’) purchase for an average chunk of national income. That ratio, or purchasing power, makes two ‘Ps’ out of the three. Now, you take the PP of United States as PP = 1,00 and you measure the PP of each other country against the US one. This is how you get the parity of PPs, or PPP.

PPP is handy for converting monetary aggregates from different countries into a common denominator made of US dollars. When we compare national markets, PPP dollars are better than those calculated with the exchange rates, as the former very largely get rid of local inflation, as well as local idiosyncrasies in pricing. With those international dollars being constant for 2010, inflation is basically kicked out of the model. The final point is that measuring national markets in PPP dollars is almost like measuring quantities, sort of standard units of medical services in this case.

So, I take the OOPS and I multiply it by the headcount of the national population, and I get the aggregate OOPS, for all the national capita taken together, in millions of PPP dollars, constant 2010. You can see the results in Table 3, below, once again approached in terms of the latest size on record (2015 in this case) vs. the compound growth rate (2000 – 2015 for this specific metric, as it is available with WHO). Once again, is there a frontier? Yes, it is made of: United Kingdom, Germany and Spain, followed respectfully by Netherlands, Switzerland and Poland. The others are the core.

Question: how can I identify a frontier without making a graph? Answer: you can once again refer to that concept of social physics. You take the size of the market in each country, or its aggregate OOPS. You compute the share of this national OOPS in the total OOPS of all the 13 countries taken together. This is the relative weight of that country in the sample. Next, you multiply the compound growth rate of the national OOPS by its relative weight and you get the metric in the third numerical column, namely ‘Size-weighted growth rate’. The greater value you obtain in that one, the further from the centre of the manifold, the two variables combined, you would find the given country.

Table 3 – Aggregate Out-Of-Pocket Expenditure on Healthcare

Country Aggregate OOPS in millions of PPP dollars in 2015 Compound growth rate in the aggregate OOPS, 2000 -2015 Size-weighted growth rate
Austria 7 951 105,8% 3,8%
Switzerland 17 802 124,9% 9,9%
Czech Republic 3 862 300,2% 5,2%
Germany 54 822 104,9% 25,7%
Spain 35 816 146,9% 23,5%
Estonia 565 308,6% 0,8%
Finland 4 356 98,5% 1,9%
France 20 569 84,7% 7,8%
United Kingdom 39 935 275,5% 49,1%
Netherlands 11 027 227,7% 11,2%
Norway 4 607 100,3% 2,1%
Poland 15 049 124,1% 8,3%
Portugal 7 622 86,8% 3,0%

 Time to wrap up the writing and serious thinking for today. You had an example of quantitative market analysis, in the form of ‘frontier vs. core’ method. When we talk about the relative attractiveness of different markets, that method, i.e. looking  for frontier markets, is quite logical and straightforward.

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. 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 versionas 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 pageand 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?

This is how I got the first numerical column

 

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And so I am developing the concept of Coop EneFin, which I hinted at inLean and adaptableand started developing more seriously in La morale de ce conte de fées. The whole idea comes from the observation that, in the European market of electricity, there is a strong differentiation in the retail price of 1 kWh, depending on the category of consumer. Small users, namely all the households plus small institutional ones, pay a price much higher than the big consumers of energy. I am designing the business concept of Coop EneFinas a way for small, local suppliers of renewable energies to attract capital and to find themselves a place in the market. The basic concept is that of complex contracts, which combine a futures contract on the supplies of electricity with the acquisition of participatory deeds in the supplier of that electricity.

If the price to pay by small users is PH, and the price for the big institutional ones is PI, and a representative small user consumes QHkilowatt hours, that basic concept can be expressed mathematically as QH(t+z)*PH= QH(t+z)*PI+ K(t)and K(t) = QH(t+z)*(PH– PI). In that mathematical expression, ‘t’ is the present moment in time, whilst ‘t+z’ is a moment in the future, with said future being distant from the present by ‘z’ periods. K(t)is investment capital supplied today, to the provider of electricity, by the means of this complex contract.

In other words, the Coop EneFinconcept assumes that households will buy their future supplies in electricity, and, in the same time, they will buy participations in the providers of that future electricity, and they will pay just the normal price they pay today for their average kilowatt hour. Coop EneFinis supposed to be a business on its own right, an essentially FinTech enterprise, partly or completely independent from the suppliers of electricity.

I need to check more thoroughly the components of this business concept. It is worth exploring what exactly should I expect to find, in real life, under the label of ‘small local supplier of renewable energies’, i.e. what do those entities really look like today, what are their ties with their markets, and what are the likely vectors of development for the future. I need to develop the concept of ‘participatory deeds’, and, in general, to blueprint the financial product to be marketed. A more in-depth study of the energy market could serve, too.

There is one thing I certainly need to work on for Coop EneFin: the name. I need to change it. I was so engrossed in the ‘cooperative’ meaning of ‘Coop’ that I completely forgot other connotations, such as ‘chicken coop’. We certainly don’t want any business to stay in a coop, unless it is money laundering. Coops are safe, but sort of limiting. Thus, I am trying to extract some other catchy word from that idea, and, in the meantime, I simply kick the ‘Coop’ out of the name, and I return to the initial ‘EneFin’.

Mind you, I have that curious ape inside me, and that happy bulldog. They love rummaging in anything that can be even remotely useful in my intellectual quest. Here are some sources those two helpful beasts have dug out of the Internet, just like that, on the spot. European Small Hydropower Association, Wind Europe, Solar Power Europe, and World Energy Councilare the ORG-type pages, just as IRENA. The latter (Irena) publishes a lot of useful stuff regarding renewable energies. Here are the links to some of their reports: Renewable technologies cost analysis – hydropower, Renewable Power Generation Costs in 2017, and Cost-competitive renewable power generation: Potential across South East Europe. Besides, I collected some stuff, here and there online: ‘The Economics of Hydroelectricity’ by Jean-Marie Martin-Amouroux, ‘Hydropower Costs. Renewable Energy Hydroelectricity Costs vs Other Renewable & Fossil Costs’ by Glenn Meyers, ‘Hydropower Baseline Cost Modeling’ by Patrick W. O’Connor et al. , and finally State of the Art on Small-Scale Concentrated Solar Power Plantsby A.Giovannelli.

Right, now it is time for the third inside me, my internal austere monk, the one armed against bullshit with the Ockham’s razor, to step into the game. Let’s nail it down: what is a small provider of renewable energy? As I rummage through the literature, being small has different denotations, depending on the exact type of renewable energy we have in mind. In the wind energy, being small is probably the hardest job. One windmill of average size generates about 1,5 MW of electrical power, and still there is one caveat: noise. Ever heard one of those buskers who perform with a two-person buck saw? That long, flexible blade played on with a fiddlestick? If the answer is ‘yes’, now imagine that performance by someone deprived both of musical ear, and of elementary skill with a fiddlestick. This is the type of noise that windmills make, partly in infrasound. Really nasty, I can tell you, and this is why they have to be located some distance from human habitats.

There seems to be some new generation of windmills coming to the market, though. As I can read with Vanessa Bates Ramirez at SingularityHub, a company named Semtiveis launching small windmills, like 1,6 kilowatt each, designed for being used in densely populated, urban habitats. Another one is the Dutch Archimedes, and those guys are doing really small as wind turbines come. Their designs range from 125 watts of power, up to 1 kW. This is really retail in wind energy. The two designs differ substantially from each other, yet both create an opening for reducing both the size of one windmill, and the distance it needs to be located from residential buildings. As a matter of fact, the distance shrinks to zero. That 125-watt thingy by Archimedes is something you can basically drag behind you on a bicycle. As I think of it, my EneFin concept QH(t+z)*PH= QH(t+z)*PI+ K(t)and K(t) = QH(t+z)*(PH– PI)could be a nice financial leverage for launching those technologies among the general public.

As for hydro, you can find all sizes: from a fancy-looking small one, 1 kW of capacity, from PowerSpout, all the way up to the 30-megawatt bulky ones by General Electric. Still, as I browsed through my notes from the last year, there are two thresholds as for the hydroelectric: 1 MW and 10 MW. Anything up to one megawatt is basically considered as DIY power generation, and between 1 MW and 10 MW the installation can be still eligible for public funding addressed to ‘small hydro’. All kinds of designs are burgeoning; it seems to be like the Golden Age of small hydro. You can even have embroideries on.

The photovoltaic is probably the most scalable, with a typical roof-of-my-garage installation going into something like 200 – 300 watts, and possible to expand according to the available surface. Yet, photovoltaic is not the only cat in the yard, as it comes to solar energy. There is that big comeback from the part of concentrated solar power. Do you remember those science-fictionish movies, mostly from the 1980ies, where solar energy was being captured with parabolic mirrors (occasionally turned by evil geniuses into deadly weapons)? Well, this is basically concentrated solar power. You capture the heat in the centre of the parabolic mirror, and then it becomes really hot, and it can give heat to water, which turns into steam and puts in motion the basic electric turbine you have in an ordinary, thermal power plant. Heat can be stored in molten salt during the night, so as not to turn the turbine off completely. In places with really a lot of heat from the sun, like from Marseille (France) southwards, you can have the most of your sun with that technology. The paper I have already linked to, namely State of the Art on Small-Scale Concentrated Solar Power Plantsby A.Giovannelli gives an idea of what is possible. Apparently, the possible is quite versatile, starting below 1 MW of power.

So, all in all, I have two classes of size, out of my research. One is around 1 MW of capacity, the second more like 10 MW. I call them, respectively, a small power installation, and a medium-sized one. Now, I go one step further and I follow Adam Smith: the size of a business is determined by the size of its market. I take my two model sizes: 1 MW and 10 MW, and I calculate the number of individual customers that such an installation could provide with electricity. Table 1, below, shows my calculations. What I did was to take the data about final consumption of energy, in kilograms of oil equivalent, as it is published by the World Bank. Then, I took 17,3% out of this final consumption, for selected European countries. That 17,3% roughly corresponds, according to what I found, to the strictly spoken household use of energy. Then I multiplied the number in kilograms of oil equivalent by 11,63 in order to have it in kilowatt hours. This is how I got the first numerical column in the table. Next, I divided the kilowatt hours by 8760, i.e. by the number of hours in an ordinary year, and so I got the capacity presented in the next column, measured in kilowatts. After having divided 1000 kW (or 1 megawatt) by that required capacity, I obtained the number of households that an installation of 1 MW could possibly supply in electricity, should they switch completely to the services of said installation.

Table 1 

Country Estimated household use of energy, kWh per annum per household Capacity needed for 1 household, in kW Number of households supplied by a 1 MW installation
Austria 7 654,60 0,87 1 144
Switzerland 5 955,64 0,68 1 471
Czech Republic 7 766,29 0,89 1 128
Germany 7 680,87 0,88 1 140
Spain 5 173,51 0,59 1 693
Estonia 8 396,69 0,96 1 043
Finland 11 920,44 1,36 735
France 7 419,85 0,85 1 181
United Kingdom 5 561,10 0,63 1 575
Netherlands 8 516,84 0,97 1 029
Norway 11 701,35 1,34 749
Poland 5 010,27 0,57 1 748
Portugal 4 288,92 0,49 2 042

 The size of the market nailed down, I turn to its value. I return to my QH(t+z)*PH= QH(t+z)*PI+ K(t)and K(t) = QH(t+z)*(PH– PI)golden recipe, and I consider the prices in question. Just to update those, who have not quite followed so far: the whole scheme consists in selling futures contracts on electricity to households, paid nominally at the ordinary household rate per 1 kW, only that ordinary rate buys them electricity at non-household prices, much lower, and, additionally, participatory deeds in the balance sheet of the supplier.

Anyway, I take prices of energy as they are, and I calculate the things you can find in Table 2, below. What I call ‘Revenue from the local market of a 1 MW installation, at non-household prices’ is the market value of electricity sold, and non-household prices to the population of households calculated in the last column of Table 1. The so-called capital contribution from the same population is the amount paid in as the surplus of the household price over the non-household price of energy. Now, I take a value which I found online – €2 445 – which apparently corresponds to the cost of physical investment in 1 kW of capacity in small hydro. It makes €2 445 000 for 1 MW, and I divide my ‘Capital contribution’ by that sum. What I get is the estimated contribution of said capital contribution to the physical setting up of the installation. Why hydro? I am a bit obsessed with it, I admit. You can find an explanation with Impakter.

Germany looks like the best market for my EneFin scheme, hands down, once again. Spain, Austria, Poland, and Portugal follow at a respectable distance.

Table 2

Country Price of electricity for households, per 1 kWh Non-household price of electricity, per 1 kWh Revenue from the local market of a 1 MW installation, at non-household prices Capital contribution from the local market of a 1 MW installation Estimated percentage of the physical investment needed in small hydro
Austria € 0,20 € 0,09 € 788 400,00 € 963 600,00 39%
Switzerland € 0,19 € 0,10 € 898 517,81 € 765 882,19 31%
Czech Republic € 0,14 € 0,07 € 613 200,00 € 613 200,00 25%
Germany € 0,35 € 0,15 € 1 314 000,00 € 1 752 000,00 72%
Spain € 0,23 € 0,11 € 963 600,00 € 1 051 200,00 43%
Estonia € 0,12 € 0,09 € 788 400,00 € 262 800,00 11%
Finland € 0,16 € 0,07 € 613 200,00 € 788 400,00 32%
France € 0,17 € 0,10 € 876 000,00 € 613 200,00 25%
United Kingdom € 0,18 € 0,13 € 1 138 800,00 € 438 000,00 18%
Netherlands € 0,16 € 0,08 € 700 800,00 € 700 800,00 29%
Norway € 0,17 € 0,07 € 613 200,00 € 876 000,00 36%
Poland € 0,15 € 0,09 € 788 400,00 € 525 600,00 21%
Portugal € 0,23 € 0,12 € 1 051 200,00 € 963 600,00 39%

 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. 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 versionas 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 pageand 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?

The other cheek of business

My editorial

I am turning towards my educational project. I want to create a step-by-step teaching method, where I guide a student in their learning of social sciences, and this learning is by doing research in social sciences. I have a choice between imposing some predefined topics for research, or invite each student to propose their own. The latter seems certainly more exciting. As a teacher, I know what a brain storm is, and believe: a dozen dedicated and bright individuals, giving their ideas about what they think it is important to do research about, can completely uproot your (my own?) ideas as what it is important to do research about. Still, I can hardly imagine me, individually, handling efficiently all that bloody blissful diversity of ideas. Thus, the first option, namely imposing some predefined topics for research, seems just workable, whilst still being interesting. People can get creative about methods of research, after all, not just about topics for it. Most of the great scientific inventions was actually methodology, and what was really breakthrough about it consisted in the universal applicability of those newly invented methods.

Thus, what I want to put together is a step-by-step path of research, communicable and teachable, regarding my own topics for research. Whilst I still admit the possibility of student-generated topics coming my way, I will consider them as a luxurious delicacy I can indulge in under the condition I can cope with those main topics. Anyway, my research topics for 2018 are:

  1. Smart cities, their emergence, development, and the practical ways of actually doing business there
  2. Fintech, and mostly cryptocurrencies, and even more mostly those hybrid structures, where cryptocurrencies are combined with the “traditional” financial assets
  • Renewable energies
  1. Social and technological change as a manifestation of collective intelligence

Intuitively, I can wrap (I), (II), and (III) into a fancy parcel, decorated with (IV). The first three items actually coincide in time and space. The fourth one is that kind of decorative cherry you can put on a cake to make it look really scientific.

As I start doing research about anything, hypotheses come handy. If you investigate a criminal case, assuming that anyone could have done anything anyhow gives you certainly the biggest possible picture, but the picture is blurred. Contours fade and dance in front on your eyes, idiocies pop up, and it is really hard to stay reasonable. On the other hand, if you make some hypotheses as for who did what and how, your investigation gathers both speed and sense. This is what I strongly advocate for: make some hypotheses at the starting point of your research. Before I go further with hypothesising on my topics for research, a few preliminary remarks can be useful. First of all, we always hypothesise about anything we experience and think. Yes, I am claiming this very strongly: anything we think is a hypothesis or contains a hypothesis. How come? Well, we always generalise, i.e. we simplify and hope the simplification will hold. We very nearly always have less data than we actually need to make the judgments we make with absolute certainty. Actually, everything we pretend to claim with certainty is an approximation.

Thus, we hypothesise intuitively, all the time. Now, I summon the spirit of Milton Friedman from the abyss of pre-Facebook history, and he reminds us the four basic levels of hypothesising. Level one: regarding any given state of nature, we can formulate an indefinitely great number of hypotheses. In practice, there is infinitely many of them. Level two: just some of those infinitely many hypotheses are checkable at all, with the actual access to data I have. Level three: among all the checkable hypotheses, with the data at hand, there are just some, regarding which I can say with reasonable certainty whether they are true or false. Level four: it is much easier to falsify a hypothesis, i.e. to say under what conditions it does not hold at all, than to verify it, i.e. claiming under what conditions it is true. This comes from level one: each hypothesis has cousins, who sound almost exactly the same, but just almost, so under given conditions many mutually non-exclusive hypotheses can be true.

Now, some of you could legitimately ask ‘Good, so I need to start with formulating infinitely many hypotheses, then check which of them are checkable, then identify those allowing more or less rigorous scientific proof? Great. It means that at the very start I get entangled for eternity into checking how checkable is each of the infinitely many hypotheses I can think of. Not very promising as for results’. This is legit to say that, and this is the reason why, in science, we use that tool known as the Ockham’s razor. It serves to give a cognitive shave to badly kept realities. In its traditional form it consists in assuming that the most obvious answer is usually the correct one. Still, as you have a closer look at this precise phrasing, you can see a lot of hidden assumptions. It assumes you can distinguish the obvious from the dubious, which, in turn, means that you have already applied the razor beforehand. Bit of a loop. The practical way of wielding that razor is to assume that the most obvious thing is observable reality. I start with finding my bearings in reality. Recently, I gave an example of that: check ‘My individual square of land, 9 meters on 9’  . I look around and I assess what kind of phenomena, which, at this stage of research, I can intuitively connect to the general topic of my research, and which I can observe, measure, and communicate intelligibly about. These are my anchors in reality.

I look at those things, I measure them, and I do my best to communicate by observations to other people. This is when the Ockham’s razor is put to an ex post test: if the shave has been really neat, other people can easily understand what I am communicating. If I and a bunch of other looneys (oops! sorry, I wanted to say ‘scientists’) can agree on the current reading of the density of population, and not really on the reading of unemployment (‘those people could very well get a job! they are just lazy!), then the density of population is our Ockham’s razor, and unemployment not really (I love the ‘not really’ expression: it can cover any amount of error and bullshit). This is the right moment for distinguishing the obvious from the dubious, and to formulate my first hypotheses, and then I move backwards the long of the Milton Friedman’s four levels of hypothesising. The empirical application of the Ockham’s razor has allowed me to define what I can actually check in real life, and this, in turn, allows distinguishing between two big bags, each with hypotheses inside. One bag contains the verifiable hypotheses, the other one is for the speculative ones, i.e. those non-verifiable.

Anyway, I want my students to follow a path of research together with me. My first step is to organize the first step on this path, namely to find the fundamental, empirical bearings as for those four topics: smart cities, Fintech, renewable energies and collective intelligence. The topic of smart cities certainly can find its empirical anchors in the prices of real estate, and in the density of population, as well as in the local rate of demographic growth. When these three dance together – once again, you can check ‘My individual square of land, 9 meters on 9’ – the business of building smart cities suddenly gets some nice, healthy, reddish glow on its cheeks. Businesses have cheeks, didn’t you know? Well, to be quite precise, businesses have other cheeks. The other cheek, in a business, is what you don’t want to expose when you already get hit somewhere else. Yes, you could call it crown jewels as well, but other cheek sounds just more elegantly.

As for Fintech, the first and most obvious observation, from my point of view, is diversity. The development of Fintech calls into existence many different frameworks for financial transactions in times and places when and where, just recently, we had just one such framework. Observing Fintech means, in the first place, observing diversity in alternative financial frameworks – such as official currencies, cryptocurrencies, securities, corporations, payment platforms – in the given country or industry. In terms of formal analytical tools, diversity refers to a cross-sectional distribution and its general shape. I start with I taking a convenient denominator. The Gross Domestic Product seems a good one, yet you can choose something else, like the aggregate value of intellectual property embodied in selfies posted on Instagram. Once you have chosen your denominator, you measure the outstanding balances, and the current flows, in each of those alternative, financial frameworks, in the units of your denominator. You get things like market capitalization of Ethereum as % of GDP vs. the supply of US dollar as % of its national GDP etc.

I pass to renewable energies, now. When I think about what is the most obviously observable in renewable energies, it is a dual pattern of development. We can have renewable sources of energy supplanting fossil fuels: this is the case in the developed countries. On the other hand, there are places on Earth where electricity from renewable sources is the first source of electricity ever: those people simply didn’t have juice to power their freezer before that wind farm started up in the whereabouts. This is the pattern observable in the developing countries. In the zone of overlapping, between those two patterns, we have emerging markets: there is a bit of shifting from fossils to green, and there is another bit of renewables popping up where nothing had dared to pop up in the past. Those patterns are observable as, essentially, two metrics, which can possibly be combined: the final consumption of energy per capita, and the share of renewable sources in the final consumption of energy. Crude as they are, they allow observing a lot, especially when combined with other variables.

And so I come to collective intelligence. This is seemingly the hardest part. How can I say that any social entity is kind of smart? It is even hard to say in humans. I mean, virtually everybody claims they are smart, and I claim I’m smart, but when it comes to actual choices in real life, I sometimes feel so bloody stupid… Good, I am getting a grip. Anyway, intelligence for me is the capacity to figure out new, useful things on the grounds of memory about old things. There is one aspect of that figuring out, which is really intriguing my internal curious ape: the phenomenon called ultra-socialisation, or supersocialisation. I am inspired, as for this one, by the work of a group of historians: see ‘War, space, and the evolution of Old World complex societies’ (Turchin et al. 2013[1]). As a matter of fact, Jean Jacques Rousseau, in his “Social Contract”, was chasing very much the same rabbit. The general point is that any group of dumb assholes can get social on the level of immediate gains. This is how small, local societies emerge: I am better at running after woolly mammoths, you are better at making spears, which come handy when the mammoth stops running and starts arguing, and he is better at healing wounds. Together, we can gang up and each of us can experience immediate benefits of such socialisation. Still, what makes societies, according to Jean Jacques Rousseau, as well as according to Turchin et al., is the capacity to form institutions of large geographical scope, which require getting over the obsession of immediate gains and provide long-term, developmental a kick. What is observable, then, are precisely those institutions: law, state, money, universally enforceable contracts etc.

Institutions – and this is the really nourishing takeaway from that research by Turchin et al. (2013[2]) – are observable as a genetic code. I can decompose institutions into a finite number of observable characteristics, and each of them can be observable as switched on, or switched off. Complex institutional frameworks can be denoted as sequences of 1’s and 0’s, depending on whether the given characteristics is, respectively, present or absent. Somewhere between the Fintech, and collective intelligence, I have that metric, which I found really meaningful in my research: the share of aggregate depreciation in the GDP. This is the relative burden, imposed on the current economic activity, due to the phenomenon of technologies getting old and replaced by younger ones. When technologies get old, accountants accounts for that fact by depreciating them, i.e. by writing off the book a fraction of their initial value. All that writing off, done by accountants active in a given place and time, makes aggregate depreciation. When denominated in the units of current output (GDP), it tends to get into interesting correlations (the way variables can socialize) with other phenomena.

And so I come with my observables: density of population, demographic growth, prices of real estate, balances and flows of alternative financial platforms expressed as percentages of the GDP, final consumption of energy per capita, share of renewable energies in said final consumption, aggregate depreciation as % of the GDP, and the genetic code of institutions. What I can do with those observables, is to measure their levels, growth rates, cross-sectional distributions, and, at a more elaborate level, their correlations, cointegrations, and their memory. The latter can be observed, among other methods, as their Gaussian vector autoregression, as well as their geometric Brownian motion. This is the first big part of my educational product. This is what I want to teach my students: collecting that data, observing and analysing it, and finally to hypothesise on the grounds of basic observation.

[1] Turchin P., Currie, T.E.,  Turner, E. A. L., Gavrilets, S., 2013, War, space, and the evolution of Old World complex societies, Proceedings of The National Academy of Science, vol. 110, no. 41, pp. 16384 – 16389

[2] Turchin P., Currie, T.E.,  Turner, E. A. L., Gavrilets, S., 2013, War, space, and the evolution of Old World complex societies, Proceedings of The National Academy of Science, vol. 110, no. 41, pp. 16384 – 16389

Quite abundant a walk of life

My editorial

I have just finished writing an article about the link between energy and human settlement. You could have noticed that I have been kind of absent from scientific blogging for a few days. I had my classes starting, at the university, and this was the first reason, but the second one was precisely that article. On Wednesday, I started doing some calculations, well in the lines of that latest line of my research (you can look up ‘Core and periphery’ ). Nothing very serious, just some casual dabbling with numbers. You know, when you are an economist, you start having cold turkey symptoms when you are parted with an Excel spreadsheet. From time to time, you just need to do some calculations, and so I was doing when, suddenly, those numbers started making sense. It is a peculiar feeling when numbers start making sense, because usually, you just kind of feel that sense but you don’t exactly know what it actually is. That was exactly my case, on Wednesday. I started playing with the parameters of that general equilibrium, with population size on the left side of the equation, and energy use, as well as food intake, on the other side. All of a sudden, that theoretical equilibrium started yielding real, robust, local equilibria in individual countries. Then, something just fired off in my mind. My internal happy bulldog, you know, that little beast who just loves biting into big, juicy loafs of data, really bit in. My internal ape, that curious and slightly impolite part of me, went to force the bulldog’s jaws open, but it got fascinated. My internal austere monk, that really-frontal-cortex guy inside of me, who walks around with the Ockham’s razor ready to slash into bullshit, had to settle the matters. He said: ‘Good, folks, as you are, we need to hatch an article, and we do it know’. You don’t discuss with a guy who has a big razor, and so all of me wrote this article. Literally all of me. It was the first time, since I was 22 (bloody long ago), that I spent a night awake, writing. The result, for the moment in the pre-editorial form, is entitled ‘Settlement by energy – can renewable energies sustain our civilisation?’  and you can read it just by clicking this link.

Anyway, now I am in a post-article frame of mind, which means I need to shake it off a bit. What I usually do in terms of shaking off is having conversations with dead people. No, I don’t need candles. One of my favourite and not-quite-alive-anymore interlocutors is Jacques Savary, a merchant and public officer, who, in 1675, two years after both the real and the fictional d’Artagnan had been dead, published, with the privilege of the King, and through the industrious efforts of the publishing house run by Louis Billaine, located at the Second Pillar of the Grand Salle of the Palace, at Grand Cesar, a book entitled, originally, ‘Le Parfait Négociant ou Instruction Générale Pour Ce Qui Regarde Le Commerce’. In English, that would be ‘The Perfect Merchant or General Instructions as Regards Commerce’. And so I am summoning Master Savary from the after world of social sciences, and we start chatting about what he wrote regarding manufactures (Book II, Chapter XLV and XLVI). First, a light stroke of brush to paint the general landscape. Back in the days, in the second half of the 17th century, manufactures meant mostly textile and garments. There was some industrial activity in other goods (glass, tapestry), but the bulk of industry was about cloth, in many forms. People at the time were really inventive as it came to new types of cloth: they experimented with mixing cotton, wool and silk, in various proportions, and they experimented with dyeing (I mean, they experimented with dying, as well, but we do it all the time), and they had fashions. Anyway, textile and garment was THE industry.

As Master Savary starts his exposition about manufactures, he opens up with a warning: manufactures can lead you to ruin. Interesting opening for an instruction. The question is why? Or rather, how? I mean, how could a manufacturing business lead to ruin? Well, back in the day, in 17th century, in Europe, manufacturing activities used to be quite separated institutionally from the circulation of big money. Really big business was being done mostly in trade, and large-scale manufacturing was seen as kind of odd. In trade, merchants of the time devised various legal tools to speed up the circulation of capital. Bills of exchange, maritime insurance, tax farming – it all allowed, with just the right people to know, a really smooth flow of money, even in the presence of many-year-long maritime commercial trips. In manufacturing, many of those clever tricks didn’t work, or at least didn’t work yet. They had to wait, those people, some 200 years before manufacturing would become really smooth a way of circulating capital. Anyway, putting money in manufacturing meant that you could not recover it as quickly as you could in trade. Basically, when you invested in manufactures, you were much more dependent on the actual marketability of your actual products than you were in trade. Thus, many merchants, Master Savary obviously included, perceived manufacturing as terribly risky.

What did he recommend in the presence of such dire risk? First of all, he advised to distinguish between three strategies. One, imitate a foreign manufacture. Second, invent something new and set a new manufacture. Third, invest in ‘an already established Manufacture, whose merchandise has an ordinary course in the Kingdom as well as in foreign Countries, by the general consent of all the people who had recognized its goodness, in the use of fabric which have been manufactured there’. I tried to translate literally the phrasing of the last strategy, in order to highlight the key points of the corresponding business plan. An established manufacture meant, first of all, the one with ‘an ordinary course in the Kingdom as well as in foreign Countries’. Ordinary course meant a predictable final selling price. As a matter of fact, this is my problem with that translation. Master Savary originally used the French expression: ‘cours ordinaire’, which, in English, becomes ambiguous. First, it can mean ‘ordinary course’, i.e. something like an established channel of distribution. Still, it can also mean ‘ordinary rate of exchange’. Why ‘rate of exchange’? We are some 150 years before the development of modern, standardized monetary systems. We are even some 100 years before the appearance of paper money. There were coins, and there was a s***load of other things you could exchange your goods against. At Master Savary’s time, many things were currencies. In business, you traded your goods against various types of coins, you accepted bills of exchange instead of coins, you traded against gold and silver in ingots, as well, and finally, you did barter. Some young, rich, and spoilt marquis had lost some of its estates by playing cards, he signed some papers, and here you are, with the guy who wants to buy your entire stock of woollen garments and who wants to pay you precisely with those papers signed by the young marquis. If you were doing really big business, none of your goods has one price: instead, they all had complex exchange rates against other valuables. Trading goods with what Master Savary originally called ‘cours ordinaire’ meant that the goods in question were kind of predictable as for their exchange rate against anything else in that economic jungle of the late 17th century.

What worked on the selling side, had to work on the supply side as well. You had to buy your raw materials, your transport, your labour etc. at complex exchange rates, and not at those nice, tame, clearly cut prices in one definite currency. Making the right match between exchange rates achieved when purchasing things, and those practiced at the end of the value chain was an art, and frequently a pain in your ass. In other words, business in 17th century was very much like what we would have now if our banking and monetary systems collapsed. Yes, baby, them bankers are mean and abjectly rich, but they keep that wheel spinning smoothly, and you don’t have to deal with Somalian pirates in order to buy from them some drugs, which you are going to exchange against natural oil in Yemen, which, in turn, you will use to back some bills of exchange, which will allow you to buy cotton for your factory.

Now, let’s return to what Master Savary had to say about those three strategies for manufacturing. As he discusses the first one – imitating a foreign factory – he recommends five wise things to do. One, check if you can achieve exactly the same quality of fabric as those bloody foreigners do. If you cannot, there is no point in starting imitation. Two, make sure you can acquire your raw materials, in the necessary bracket of quality, in the place where you locate your manufacture. Three, make sure the place where you locate your operations will allow you to practice prices competitive as compared to those foreign goods you are imitating. Four, create for yourself conditions for experimenting with your product and your business. Launch some kind of test missiles in many directions, present your fabrics to many potential customers. In other words, take your time, bite your ambition, suck ass and make your way into the market step by step. Five, arrange for acquiring the same tools, and even the same people that work in those foreign manufactures. Today, we would say: acquire the technology, both the formal, and the informal one.

As he passes to discussing the second strategy, namely inventing something new, Master Savary recommends even more prudence, and, in the same time, he pulls open a bit the veil of discretion regarding his own life, and confesses that he, in person, had invented three new fabrics during his business career: a thick woollen ribbon made of camel wool, a thick drugget for making simple, coarse, work clothes, and finally a ribbon made of woven gold and silver. Interesting. Here is a guy, who started his professional life as a merchant, then he went into commercial arbitrage for some time, then he went into the service of a rich aristocrat ( see ‘Comes the time, comes the calm duke’ ), then he entered into a panel of experts commissioned by Louis XIV, the Sun King, to prepare new business law, and in the meantime he invented decorative ribbons for rich people, as well as coarse fabrics for poor people. Quite abundant a walk of life. As I am reading the account of his textile inventions, he seems to be the most attached to, and the most vocal about that last one, the gold and silver ribbon. He insists that nobody before him had ever succeeded in weaving gold and silver into something wearable. He describes in detail all the technological nuances, like for example preventing the chipping off of the very thinly pulled, thread size, golden wire. He concludes: ‘I have given my own example, in order to make those young people, who want to invent new Manufactures, understand they should take their precautions, not to engage imprudently and not to let themselves being carried away by the profits they will make on their first fabrics, and to have a great number of them fabricated, before being certain they will be pleasant to the public, as well as for their beauty as for quality; for it is really dangerous, and they will risk their fortune at it’.