I am working on a specific aspect of the scientific presentation regarding my EneFin concept, namely on transposing the initial idea – a quasi-cooperative scheme between a local supplier of renewable energies and his local customers, in an essentially urban environment (I was thinking about smart cities) – into the context of poor, rural communities in developing countries. Basically, it was worth approaching the topic from the scientific angle, instead of the purely business-planning one. When I do science, I need to show that I have read what other scientists have written and published on a given topic. So I did, and a few articles have given me this precise idea of expanding the initial concept: Muller et al. 2018, Du et al. 2016, Wang et al. 2017, and Moallemi, Malekpour 2018.
I like feeling that the things I do are useful to somebody. I mean, not just interesting, but like really useful. When I write on this blog, I like the thought that some students in social sciences could use the methods presented in their own learning, or that some teachers in social sciences could get inspired. I’m OK with inspiring negatively. If some academic in social sciences, after reading some of my writing, says ‘This Wasniewski guy is one of the dumbest and most annoying people I have ever read anything written by, and I want to prove it by my own research!’, I’m fine with that. This is inspiration, too.
Science is like a screwdriver: the more different contexts you can use your screwdriver in, the more useful it is. This is, by the way, a very scientific approach to economic utility. The more functions a thing can perform, the greater its aggregate utility. So I want those things I write to be useful, and making them functional in more contexts increases their utility. That’s why applying the initial, essentially urban idea of EneFin to the context of alleviating poverty in developing countries is an interesting challenge.
Here is my general method. I imagine a rural community in some remote location, without regular access to electricity at all. All they have are diesel generators. According to Breyer et al. 2010, even in the most favourable conditions, the LCOE (Levelized Cost Of Electricity) for energy generated out of diesel is like 0,16 – 0,34 €/kWh. Those most favourable conditions are made of a relatively low price of crude oil, and, last but not least, the virtual absence of transportation costs as regards the diesel oil itself. In other words, that 0,16 – 0,34 €/kWh is essentially relevant for a diesel generator located right by the commercial port where diesel oil is being unloaded from a tanker ship. Still, we are talking about a remote rural location, and that means far from commercial ports. Diesel has to come there by road, mostly. According to a blog post which I found (OK, Google found) at the blog of the Golden Valley Electric Association, that cost per 1 kWh of electricity could even go up to US$ 0,64 = €0,54.
Technological change brings alternatives to that, in the form of renewable energies. Photovoltaic installations come at really a low cost: their LCOE is already gravitating towards €0,05. Onshore wind and small hydro are quite close to that level. Switching from diesel generators to renewables equals the same type of transition that I already mentioned in « Couldn’t they have predicted that? », i.e. from a bracket of relatively high prices of energy, to that of much lower a price (IRENA 2018).
Here comes the big difference between an urban environment in Europe, and a rural community in a developing country. In the former, shifting from higher prices of energy to lower ones means, in the first place, an aggregate saving on energy bills, which can be subsequently spent on other economic utilities. In the latter, lower price of energy means the possibility of doing things those people simply couldn’t afford before: reading at night, powering a computer 24/24, keeping food in a fridge, using electric tools in some small business etc. More social roles define themselves, more businesses start up; more jobs, crafts and professions develop. It is a quantum leap.
Analytically, the initially lonely price of energy from diesel generators, or PD(t), gets company in the form of energy from renewable sources, PRE(t). As I have already pointed out, PD(t) > PRE(t). The (t) symbol means a moment in time. It is a scientific habit to add moments to categories, like price. Things just need time in order to happen, man. A good price needs to have a (t), if it is to prove its value.
Now, I try to imagine the socio-economic context of PD(t) > PRE(t). If just the diesel generators are available, thus if PD(t) is on its own, a certain consumption of energy occurs. Some people are like 100% on the D (i.e. diesel) energy, and they consume QD(t) = QE(t) kilowatt hours. The aggregate QE(t) is their total use of energy. Some people are to some extent on diesel power, and yet, for various reasons (i.e. lack of money, lack of permanent physical access to a generator etc.), that QD(t) does not cover their QE(t) entirely. I write it as QD(t) = a*QE(t) and 0 < a < 1. Finally, there are people for whom the diesel power is completely out of reach, and, temporarily, their QE(t) = 0.
In a population of N people, I have, thus, three subsets, made, respectively, of ‘m’ people who QD(t) = QE(t), ‘p’ people who QD(t) = a*QE(t) and 0 < a < 1, and ‘q’ people on the strict QE(t) = 0 diet. When renewable energies are being introduced, at a PRE(t+1) < PD(t+1) price, what happens is a new market, balanced or monopolized at the price PRE(t+1), and at the QRE(t+1) aggregate quantity, and people start choosing. As they choose, they actually make that QRE(t+1) happen. Among those who were QE(t) = 0, an aggregate b*QE(t+1) flocks towards QRE(t+1), with 0 < b ≤ 1. In the subset of the QD(t) = a*QE(t), at least (1-a)*QE(t+1) go PRE(t+1) and QRE(t+1), just as some c*QD(t) out of the QD(t) = QE(t) users, with 0 ≤ c ≤ 1.
It makes a lot of different Qs. Time to put them sort of coherently together. What sticks its head through that multitude of Qs is the underlying assumption, which I have just figured out I had made before, that in developing countries there is a significant gap between that sort of full-swing-full-supply consumption of energy, which I can call ‘potential consumption’, or QE(t), on the one hand, and the real, actual consumption, or QA(t). Intuitively, QE(t) > QA(t), I mean way ‘>’.
I like checking my theory with facts. I know, might look not very scientific, but I can’t help it: I just like reality. I go to the website of the World Bank and I check their data on the average consumption of energy per capita. I try to find out a reference level for QE(t) > QA(t), i.e. I want to find a scale of magnitude in QA(t), and from that to infer something about QE(t). The last (t) that yields a more or less comprehensive review of QA(t) is 2014, and so I settle for QA(2014). In t = 2014, the country with the lowest consumption of energy per capita, in kilograms of oil equivalent, was technically South Sudan: QA(2014) = 60,73 kg of oil equivalent = 60,73*11,63 kWh = 706,25 kWh. Still, South Sudan started being present in this particular statistic only in 2012. Thus, if I decide to move my (t) back in ‘t’, there is not much moving to do in this case.
Long story short, I take the next least energy-consuming country on the list: Niger. Niger displays a QA(2014) = 150,73 kg of oil equivalent per person per year = 1753,04 kWh per person per year. I check the energy profile of Niger with the International Energy Agency. Niger is really a good case here. Their total QA(2014) = 2 649 ktoe (kilotons of oil equivalent), where 2 063 ktoe = 77,9% consists in waste and biofuel burnt directly for residential purposes, without even being transformed into electricity. Speaking of the wolf, electricity strictly spoken makes just 55 ktoe in the final consumption, thus 55/2649 = 2% of the total. The remaining part of the cocktail are oil products – 506 ktoe = 19,1% – mostly made domestically from the prevalently domestic crude oil, and burnt principally in transport (388 ktoe), and then in industry (90 ktoe). Households burn just 20 ktoe of oil products per year.
That strange cocktail of energies reflects in the percentages that Niger displays in the World Bank data regarding the share of renewable energies in the overall consumption of energy, as well as in the generation of electricity. As for the former, Niger is, involuntarily, in the world’s vanguard of renewables, with 78,14% coming from renewables. Strange? Well, life is strange. Biofuels are technically renewable source of energy. When you burn the wood and straw that grows around, there will be some new growing around, whence renewability. Still, that biomass in Niger is being just burnt, without transformation of the resulting thermal energy into electric power. As we pass to data on the share of renewables in the output of electricity, Niger is at 0,58%. Not much.
From there, I have many possible paths to follow so as to answer the basic question: ‘What can Niger get out of enriching their energy base with renewables, possibly using an institutional scheme in the lines of the EneFin concept?’. My practical side tells me to look for a benchmark, i.e. for another country in Africa, where the share of renewable energy in the output of electricity is slightly higher than in Niger, without being lightyears away. Here, surprise awaits: there are not really a lot of African countries close to Niger’s rank, regarding this particular metric. There is South Africa, with 1,39% of their electricity coming from renewable sources. Then, after a long gap, comes Senegal, with 10,43% of electricity from renewables.
I quickly check those two countries with the International Energy Agency. South Africa, in terms of energy, is generally coal and oil-oriented, and looks like not the best benchmark in the world for what I what to study. They are thick in energy, by the way: QA(2014) = 2 695,73 kg of oil equivalent, more than 100 times the level of Niger. Very much the same with Senegal: it is like Niger topped with a large oil-based economy, and with a QA(2014) = 272,08 kg of oil equivalent. Sorry, I have to move further up the ranking of African countries in terms of renewables’ share in the output of electricity. Here comes Nigeria, 17,6% of electricity from renewables, and it is like a bigger brother of Niger: 86% of energy comes from the direct burning of biofuels and waste, only those biofuels are like 50 times more than in Niger. Their QA(2014) = 763,4 kg of oil equivalent per person per year.
I check Cote d’Ivoire, 23,93% of electricity from renewable sources, and I get the same, biofuels-dominated landscape. Gabon, Tanzania, Angola, Zimbabwe: all of them, however is their exact metric as for the share or renewables in the output of electricity, have mostly biofuels as renewable sources. Ghana, QA(2014) = 335.05, Mozambique, QA(2014) = 427.6, and Zambia, QA(2014) = 635.5, present slightly different a profile, with a noticeable share of hydro, but still heavily relying on biofuels.
In general, Africa seems to love biofuel, and to be largely ignoring the solar, the wind, and the hydro. This is a surprise. They have a lot of sunlight and sun heat, over there, for one. I started all my research on renewable energies, back in winter 2016, on the inspiration I had from the Ouarzazate-Noor Project in Morocco (see official updates: 2018, 2014, 2011). I imagined that Africa should be developing a huge capacity in renewable sources other than biofuels.
There is that anecdote, to find in textbooks of marketing. Two salesmen of a footwear company are sent to a remote province in a developing country, to research the local market. Everybody around walks barefoot. Salesman A calls his boss and says there are absolutely no market prospects whatsoever, as all the locals walk barefoot. Salesman B makes his call and, using the same premise – no shoes spotted locally at all – concludes there is a huge market to exploit.
Which salesman am I? Being A, I should conclude that schemes like EneFin, in African countries, should serve mostly to develop the usage of biofuels. Still, I am tempted to go B. As the solar, the hydro and the wind power tend to strike by their absence in Africa, this could be precisely the avenue to exploit.
What is there exactly to exploit, in terms of economic gains? The cursory study of African countries with respect to their energy use per capita show huge disparities. The most notable one is to notice between countries relying mostly on biofuels, on the one hand, and those with more complex energy bases. The difference in terms of the QA(2014) consumption of energy per capita is a multiple, not a percentage margin. Introducing a new source of energy into those economies looks like a huge game-changer.
There is that database I built, last year, out of Penn Tables 9.0, and from stuff published by the World Bank, and that database serves me to do like those big econometric tests. Cool stuff. Works well. Everybody should have one. You can see some examples of how I used it last year, if you care to read « Conversations between the dead and the living (no candles) » or « Core and periphery ». I decided to test my claim, namely that introducing more energy per capita into an economy will contribute to the capita in question having more of average Gross Domestic Product, per capita of course.
I made a simple linear equation with natural logarithms of, respectively, GDP per capita, expenditure side, and energy use per capita. It looks like ln(GDP per capita) = ln(Energy per capita) + constant. That’s all. No scale factors, no controlling variables. Just pure, sheer connection between energy and output. A beauty. I am having a first go at the whole sample in my database, with that most basic equation.
|Explained variable: ln(GDP per capita), N = 5498, R2 = 0,752|
|Explanatory variable||Coefficient of regression
|(Robust) Standard Error||Significance level at t Student test|
|Ln(Energy per capita)
|0,947||(0,007)||p < 0,001|
|2,151||(0,053)||p < 0,001|
Looks promising. When driven down to natural logarithm, variance in consumption of energy per capita explains like 75% of variance in GDP per capita. In other words, generally speaking, if any institutional scheme allows enriching the energy base of a country – any country – it gives a high probability of going along with higher an aggregate output per capita.
A (partial) summing up is due. The idea of implementing a contractual scheme like EneFin in developing countries seems to make sense. The gains to expect are actually much higher than those I initially envisaged for this business concept in the urban environments of European countries. If I want to go after a scientific development of this idea, the avenue of developing countries and their rural regions seems definitely promising.
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 version as well). You can also get a free e-copy of my book ‘Capitalism and Political Power’ You can support my research by donating directly, any amount you consider appropriate, to my PayPal account. You can also consider going to my Patreon page and become my patron. If you decide so, I will be grateful for suggesting me two things that Patreon suggests me to suggest you. Firstly, what kind of reward would you expect in exchange of supporting me? Secondly, what kind of phases would you like to see in the development of my research, and of the corresponding educational tools?
Support this blog
 Müller, M. F., Thompson, S. E., & Gadgil, A. J. (2018). Estimating the price (in) elasticity of off-grid electricity demand. Development Engineering, 3, 12-22.
 Du, F., Zhang, J., Li, H., Yan, J., Galloway, S., & Lo, K. L. (2016). Modelling the impact of social network on energy savings. Applied Energy, 178, 56-65.
 Wang, G., Zhang, Q., Li, H., Li, Y., & Chen, S. (2017). The impact of social network on the adoption of real-time electricity pricing mechanism. Energy Procedia, 142, 3154-3159.
 Moallemi, E. A., & Malekpour, S. (2018). A participatory exploratory modelling approach for long-term planning in energy transitions. Energy research & social science, 35, 205-216.
 Breyer, C., Gerlach, A., Schäfer, D., & Schmid, J. (2010, December). Fuel-parity: new very large and sustainable market segments for PV systems. In Energy Conference and Exhibition (EnergyCon), 2010 IEEE International (pp. 406-411). IEEE.
 IRENA (2018), Renewable Power Generation Costs in 2017, International Renewable Energy Agency, Abu Dhabi, ISBN 978-92-9260-040-2