I am returning to a long-followed path of research, that on financial solutions for promoting renewable energies, and I am making it into educational content for my course of « Fundamentals of Finance ». I am developing on artificial intelligence as well. I think that artificial intelligence is just made for finance. Financial markets, and the contractual patterns they use, are akin endocrine systems. They generate signals, more or less complex, and those signals essentially say: ‘you lazy f**ks, you need to move and do something, and what that something is supposed to be you can read from between the lines of those financial instruments in circulation’. Anyway, what I am thinking about is to use artificial intelligence for simulating the social change that a financial scheme, i.e. a set of financial instruments, can possibly induce in the ways we produce and use energy. This update is at the frontier of scientific research, business planning, and education strictly spoken. I know that some students can find it hard to follow, but I just want to show real science at work, 100% pure beef.
I took a database which I have already used in my research on the so-called energy efficiency, i.e. on the amount of Gross Domestic Product we can derive on the basis of 1 kilogram of oil equivalent. It is a complex indicator of how efficient a given social system is as regards using energy for making things turn on the economic side. We take the total consumption of energy in a given country, and we convert it into standardized units equivalent to the amount of energy we can have out of one kilogram of natural oil. This standardized consumption of energy becomes the denominator of a coefficient, where the nominator consists in the Gross Domestic Product. Thus, it goes like “GDP / Energy consumed”. The greater the value of that coefficient, i.e. the more dollars we derive from one unit of energy, the greater is the energy efficiency of our economic system.
Since 2012, the global economy has been going through an unprecedentedly long period of expansion in real output. Whilst the obvious question is “When will it crash?”, it is interesting to investigate the correlates of this phenomenon in the sector of energy. In other terms, are we, as a civilisation more energy-efficient as we get (temporarily) much more predictable in terms of economic growth? The very roots of this question are to find in the fundamental mechanics of our civilisation. We, humans, are generally good at transforming energy. There is a body of historical and paleontological evidence that accurate adjustment of energy balance was one of the key factors in the evolutionary success of humans, both at the level of individual organisms and whole communities (Leonard, Robertson 1997; Robson, Wood 2008; Russon 2010)
When we talk about energy efficiency of the human civilisation, it is useful to investigate the way we consume energy. In this article, the question is being tackled by observing the pace of growth in energy efficiency, defined as GDP per unit of energy use (https://data.worldbank.org/indicator/EG.GDP.PUSE.KO.PP.KD?view=chart ). The amount of value added we can generate out of a given set of production factors, when using one unit of energy, is an interesting metric. It shows energy efficiency as such, and, in the same time, the relative complexity of the technological basket we use. As stressed, for example, by Moreau and Vuille (2018), when studying energy intensity, we need to keep in mind the threefold distinction between: a) direct consumption of energy b) transport c) energy embodied in goods and services.
One of the really deep questions one can ask about the energy intensity of our culture is to what extent it is being shaped by short-term economic fluctuations. Ziaei (2018) proved empirically that observable changes in energy intensity of the U.S. economy are substantial, in response to changes in monetary policy. There is a correlation between the way that financial markets work and the consumption of energy. If the relative increase in energy consumption is greater than the pace of economic growth, GDP created with one unit of energy decreases, and vice versa. There is also a mechanism of reaction of the energy sector to public policies. In other words, some public policies have significant impact on the energy efficiency of the whole economy. Different sectors of the economy respond with different intensity, as for their consumption of energy, to public policies and to changes in financial markets. We can assume that a distinct sector of the economy corresponds to a distinct basket of technologies, and a distinct institutional outset.
Faisal et al. (2017) found a long-run correlation between the consumption of energy and real output of the economy, studying the case of Belgium. Moreover, the same authors found significant causality from real output to energy consumption, and that causality seems to be uni-directional, without any significant, reciprocal loop.
Energy efficiency of national economies, as measured with the coefficient of GDP per unit of energy (e.g. per kg of oil equivalent), should take into account that any given market is a mix of goods – products and services – which generate aggregate output. Any combination “GDP <> energy use” is a combination of product markets, as well as technologies (Heun et al. 2018).
There is quite a fruitful path of research, which assumes that aggregate use of energy in an economy can be approached in a biological way, as a metabolic process. The MuSIASEM methodological framework seems to be promising in this respect (e.g. Andreoni 2017). This leads to a further question: can changes in the aggregate use of energy be considered as adaptive changes in an organism, or in generations of organisms? In another development regarding the MuSIASEM framework, Velasco-Fernández et al (2018) remind that real output per unit of energy consumption can increase, on a given basis of energy supply, through factors other than technological change towards greater efficiency in energy use. This leads to investigating the very nature of technological change at the aggregate level. Is aggregate technological change made only of engineering improvements at the microeconomic level, or maybe the financial reshuffling of the economic system counts, too, as adaptive technological change?
The MuSIASEM methodology stresses the fact that international trade, and its accompanying financial institutions, allow some countries to externalise industrial production, thus, apparently, to decarbonise their economies. Still, the industrial output they need takes place, just somewhere else.
From the methodological point of view, the MuSIASEM approach explores the compound nature of energy efficiency measured as GDP per unit of energy consumption. Energy intensity can be understood at least at two distinct levels: aggregate and sectoral. At the aggregate level, all the methodological caveats make the « GDP per kg of oil equivalent » just a comparative metric, devoid of much technological meaning. At the sectoral level, we get closer to technology strictly spoken.
There is empirical evidence that at the sectoral level, the consumption of energy per unit of aggregate output tends to: a) converge across different entities (regions, entrepreneurs etc.) b) tends to decrease (see for example: Yu et al. 2012).
There is also empirical evidence that general aging of the population is associated with a lower energy intensity, and urbanization has an opposite effect, i.e. it is positively correlated with energy intensity (Liu et al. 2017)
It is important to understand, how and to what extent public policies can influence the energy efficiency at the macroeconomic scale. These policies can either address directly the issue of thermodynamic efficiency of the economy, or just aim at offshoring the most energy – intensive activities. Hardt et al. (2018) study, in this respect, the case of United Kingdom, where each percentage of growth in real output has been accompanied, those last years, by a 0,57% reduction in energy consumption per capita.
There is grounds for claiming that increasing energy efficiency of national economies matters more for combatting climate change that the strictly spoken transition towards renewable energies (Weng, Zhang 2017). Still, other research suggest that the transition towards renewable energies has an indirectly positive impact upon the overall energy efficiency: economies that make a relatively quick transition towards renewables seem to associate that shift with better efficiency in using energy for creating real output (Akalpler, Shingil 2017).
It is to keep in mind that the energy efficiency of national economies has two layers, namely the efficiency of producing energy in itself, as distinct from the usage we make of the so-obtained net energy. This is the concept of Energy Return on Energy Invested (EROI), (see: Odum 1971; Hall 1972). Changes in energy efficiency can occur on both levels, and in this respect, the transition towards renewable sources of energy seems to bring more energy efficiency in that first layer, i.e. in the extraction of energy strictly spoken, as compared with fossil fuels. The problematically slow growth in energy efficiency could be coming precisely from the de-facto decreasing efficiency of transformation in fossil fuels (Sole et al. 2018).
Technology and social structures are mutually entangled (Mumford 1964, McKenzie 1984, Kline and Pinch 1996; David 1990, Vincenti 1994; Mahoney 1988; Ceruzzi 2005). An excellent, recent piece of research by Taalbi (2017) attempts a systematic, quantitative investigation of that entanglement.
The data published by the World Bank regarding energy use per capita in kg of oil equivalent (OEPC) (https://data.worldbank.org/indicator/EG.USE.PCAP.KG.OE ) allows an interesting insight, when combined with structural information provided by the International Energy Agency (https://www.iea.org). As one ranks countries regarding their energy use per capita, the resulting hierarchy is, in the same time, a hierarchy in the broadly spoken socio-economic development. Countries displaying less than 200 kg of oil equivalent per capita are, in the same time, barely structured as economies, with little or no industry and transport infrastructure, with quasi-inexistent institutional orders, and with very limited access to electricity at the level of households and small businesses. In the class comprised between 200 kg OEPC and approximately 600 ÷ 650 kg OEPC, one can observe countries displaying progressively more and more development in their markets and infrastructures, whilst remaining quite imbalanced in their institutional sphere. Past the mark of 650 OEPC, stable institutions are observable. Interestingly, the officially recognised threshold of « middle income », as macroeconomic attribute of whole nations, seems corresponding to a threshold in energy use around 1 500 kg OEPC. The neighbourhood of those 1 500 kg OEPC looks like the transition zone between developing economies, and the emerging ones. This is the transition towards really stable markets, accompanied by well-structured industrial networks, as well as truly stable public sectors. Finally, as income per capita starts qualifying a country into the class of « developed economies », that country is most likely to pass another mark of energy consumption, that of 3000 kg OEPC. This stylized observation of how energy consumption is linked to social structures is partly corroborated by other research, e.g. that regarding social equality in the access to energy (see for example: Luan, Chen 2018)
The nexus of energy use per capita, on the one hand, and institutions on the other hand, has even found a general designation in recent literature: “energy justice”. A cursory review of that literature demonstrates the depth of emotional entanglement between energy and social structures: it seems to be more about the connection between energy and self-awareness of societies than about anything else (see for example: Fuller, McCauley 2016; Broto et al. 2018). The difficulty in getting rid of emotionally grounded stereotypes in this path of research might have its roots in the fact that we can hardly understand what energy really is, and attempts at this understanding send us to the very foundations of our understanding as for what reality is (Coelho 2009; McKagan et al. 2012; Frontali 2014). Recent research, conducted from the point of view of management science reveal just as recent an emergence of new, virtually unprecedented, institutional patterns in the sourcing and the use of energy. A good example of that institutional change is to find in the new role of cities as active players in the design and implementation of technologies and infrastructures critical for energy efficiency (see for example: Geels et al. 2016; Heiskanen et al. 2018; Matschoss, Heiskanen 2018).
Changes observable in the global economy, with respect to energy efficiency measured as GDP per unit of energy consumed, are interestingly accompanied by those in the supply of money, urbanization, as well as the shift towards renewable energies. Years 2008 – 2010, which marked, with a deep global recession, the passage towards currently experienced, record-long and record-calm period of economic growth, displayed a few other interesting transitions. In 2008, the supply of broad money in the global economy exceeded, for the first documented time, 100% of the global GDP, and that coefficient of monetization (i.e. the opposite of the velocity of money) has been growing ever since (World Bank 2018). Similarly, the coefficient of urbanization, i.e. the share of urban population in the global total, exceeded 50% in 2008, and has kept growing since (World Bank 2018). Even more intriguingly, the global financial crisis of 2007 – 2009 took place exactly when the global share of renewable energies in the total consumption of energy was hitting a trough, below 17%, and as the global recovery started in 2010, that coefficient started swelling as well, and has been displaying good growth since then. Besides, empirical data indicates that since 2008, the share of aggregate amortization (of fixed assets) in the global GDP has been consistently growing, after having passed the cap of 15% (Feenstra et al. 2015). Some sort of para-organic pattern emerges out of those observations, where energy efficiency of the global economy is being achieved through more intense a pace of technological change, in the presence of money acting as a hormone, catabolizing real output and fixed assets, whilst anabolizing new generations of technologies.
Thus, I have that database, which you can download precisely by clicking this link. One remark: this is an Excel file, and when you click on the link, it downloads without further notice. There is no opening on the screen. In this set, we have 12 variables: i) GDP per unit of energy use (constant 2011 PPP $ per kg of oil equivalent) ii) Fixed assets per 1 resident patent application iii) Share of aggregate depreciation in the GDP – speed of technological obsolescence iv) Resident patent applications per 1 mln people v) Supply of broad money as % of GDP vi)
Energy use per capita (kg of oil equivalent) vii) Depth of the food deficit (kilocalories per person per day) viii) Renewable energy consumption (% of total final energy consumption) ix) Urban population as % of total population x) GDP (demand side) xi) GDP per capita, and finally xii) Population. My general, intuitive idea is to place energy efficiency in a broad socio-economic context, and to see what role in that context is being played by financial liquidity. In simpler words, I want to discover how can the energy efficiency of our civilization be modified by a possible change in financial liquidity.
My database is a mix-up of 59 countries and years of observation ranging from 1960 to 2014, 1228 records in total. Each record is the state of things, regarding the above-named variables, in a given year. In quantitative research we call it a data panel. You have bits of information inside and you try to make sense out of it. I like pictures. Thus, I made some. These are the two graphs below. One of them shows the energy efficiency of national economies, the other one focuses on the consumption of energy per capita, and both variables are being shown as a function of supply of broad money as % of GDP. I consider the latter to be a crude measure of financial liquidity in the given place and time. The more money is being supplied per unit of Gross Domestic Product, the more financial liquidity people have as for doing something with them units of GDP. As you can see, the thing goes really all over the place. You can really say: ‘that is a cloud of points’. As it is usually the case with clouds, you can see any pattern in it, except anything mathematically regular. I can see a dung beetle in the first one, and a goose flapping its wings in the second. Many possible connections exist between the basic financial liquidity of the economic system, on the one hand, and the way we use energy, on the other hand.
I am testing my database for general coherence. In the table below, I am showing the arithmetical average of each variable. As you hopefully know, since Abraham de Moivre we tend to assume that arithmetical average of a large sample of something is the expected value of that something. Thus, the table below shows what we can reasonably expect from the database. We can see a bit of incoherence. Mean energy efficiency is $8,72 per kg of oil equivalent in energy. Good. Now, I check. I take the energy consumption per capita and I multiply in by the number of capitae, thus I go 3 007,28 * 89 965 651 = 270 551 748,43 tons of oil equivalent. This is the amount of energy consumed in one year by the average expected national society of homo sapiens in my database. Now, I divide the average expected GDP in the sample, i.e. $1 120 874,23 mln, by that expected total consumption of energy, and I hit just $1 120 874,23 mln / 270 551 748,43 tons = $4,14 per kilogram.
It is a bit low, given that a few sentences ago the same variable was supposed to be$8,72 per kg. This is just a minor discrepancy as compared to the GDP per capita, which is the central measure of wealth in a population. The average calculated straight from the database is $22 285,63. Cool. This is quite a lot, you know. Now, I check. I take the aggregate average GDP per country, i.e. $1 120 874,23 mln, and I divide it by the average headcount of population, i.e. I go $1 120 874 230 000 / 89 965 651 = $12 458,91. What? $12 458,91 ? But it was supposed to be is $22 285,63! Who took those 10 thousand dollars away from me? I mean, $12 458,91 is quite respectable, it is just a bit below my home country, Poland, presently, but still… Ten thousand dollars of difference? How is it possible?
It is so embarrassing when numbers are not what we expect them to be. As a matter of fact, they usually aren’t. It is just our good will that makes them look so well fitting to each other. Still, this is what numbers do, when they are well accounted for: they embarrass. As they do so, they force us to think, and to dig meaning out from underneath the numbers. This is what quantitative analysis in social sciences is supposed to do: give us the meaning that we expect when we measure things about our own civilisation.
Table 1 – Average values from the pooled database of N = 1228 country-year observations
||Average expected value from empirical data, N = 1228 records
|GDP per unit of energy use (constant 2011 PPP $ per kg of oil equivalent)
|Fixed assets per 1 resident patent application (constant 2011 PPP $)
|Share of aggregate depreciation in the GDP – speed of technological obsolescence
|Resident patent applications per 1 mln people – speed of invention
|Supply of broad money % of GDP – observed financial liquidity
|Energy use (kg of oil equivalent per capita)
||3 007,28 kg
|Depth of the food deficit (kilocalories per person per day)
|Renewable energy consumption (% of total final energy consumption)
|Urban population as % of total population
|GDP (demand side; millions of constant 2011 PPP $)
||1 120 874,23
|GDP per capita (constant 2011 PPP $)
||89 965 651
Let’s get back to the point, i.e. to finance. As I explain over and over again to my students, when we say ‘finance’, we almost immediately need to say: ‘balance sheet’. We need to think in terms of a capital account. Those expected average values from the table can help us to reconstruct at least the active side of that representative, expected, average economy in my database. There are three variables which sort of overlap: a) fixed assets per 1 resident patent application b) resident patent applications per 1 mln people and c) population. I divide the nominal headcount of population by 1 000 000, and thus I get population denominated in millions. I multiply the so-denominated population by the coefficient of resident patent applications per 1 mln people, which gives me, for each country and each year of observation, the absolute number of patent applications in the set. In my next step, I take the coefficient of fixed assets per 1 patent application, and I multiply it by the freshly-calculated-still-warm absolute number of patent applications.
Now, just to make it arithmetically transparent, when I do (« Fixed assets » / « Patent applications ») * « Patent applications », I take a fraction and I multiply it by its own denominator. It is de-factorisation. I stay with just the nominator of that initial fraction, thus with the absolute amount of fixed assets. For my representative, average, expected country in the database, I get Fixed Assets = $50 532 175,96 mln.
I do slightly the same with money. I take “Supply of money as % of the GDP”, and I multiply it by the incriminated GDP, which makes Money Supplied = 74,60% * $1 120 874,23 mln = $836 213,98 mln. We have a fragment in the broader balance sheet of our average expected economy: Fixed Assets $50 532 175,96 mln and Monetary Balances $836 213,98 mln. Interesting. How does it unfold over time? Let’s zeee… A bit of rummaging, and I get the contents of Table 2, below. There are two interesting things about that table.
Table 2 – Changes over time in the capital account of the average national economy
||Average fixed assets per national economy, $ mln constant 2011 PPP
||GDP per unit of energy use (constant 2011 PPP $ per kg of oil equivalent), in the average national economy
||Supply of broad money in average national economy, $ mln constant 2011 PPP
||Money to fixed assets
||2 036 831,928
||1 955 283,198
||2 338 609,511
||2 267 728,024
||2 399 075,082
||2 277 869,991
||2 409 816,67
||2 466 046,108
||2 539 482,259
||2 634 454,042
||2 623 451,217
||2 658 255,842
||2 734 170,979
||2 885 480,779
||3 088 417,325
||3 346 005,071
||3 781 802,623
||4 144 895,314
||4 372 927,883
||5 166 422,174
||5 073 697,622
||5 702 948,813
||6 039 017,049
||6 568 280,779
||5 559 781,782
This is becoming really interesting. Both components in the capital account of the representative, averaged economy had been growing until 2013, then it fell. Energy efficiency has been growing quite consistently, as well. The ratio of money to assets, thus a crude measure of financial liquidity in this capital account, remains sort of steady, with a slight oscillation. You can see it in the graph below. I represented all the variables as fixed-base indexes: the value recorded for the year 2000 is 1,00, and any other value is indexed over that one. We do that thing all the time, in social sciences, when we want to study apparently incompatible magnitudes. A little test of Pearson correlation, and… Yesss! Energy efficiency is Pearson correlated with the amount of fixed assets at r = 0,953096394, and with the amount of money supplied at r = 0,947606073. All that in the presence of more or less steady a liquidity.
Provisional conclusion: the more capital we accumulate, we, the average national economy, the more energy efficient we are, and we sort of dynamically adjust to keep the liquidity of that capital, at least the strictly monetary liquidity, at a constant level.
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?
 Leonard, W.R., and Robertson, M.L. (1997). Comparative primate energetics and hominoid evolution. Am. J. Phys. Anthropol. 102, 265–281.
 Robson, S.L., and Wood, B. (2008). Hominin life history: reconstruction and evolution. J. Anat. 212, 394–425
 Russon, A. E. (2010). Life history: the energy-efficient orangutan. Current Biology, 20(22), pp. 981- 983.
 Moreau, V., & Vuille, F. (2018). Decoupling energy use and economic growth: Counter evidence from structural effects and embodied energy in trade. Applied Energy, 215, 54-62.
 Ziaei, S. M. (2018). US interest rate spread and energy consumption by sector (Evidence of pre and post implementation of the Fed’s LSAPs policy). Energy Reports, 4, 288-302.
 Faisal, F., Tursoy, T., & Ercantan, O. (2017). The relationship between energy consumption and economic growth: Evidence from non-Granger causality test. Procedia Computer Science, 120, 671-675
 Heun, M. K., Owen, A., & Brockway, P. E. (2018). A physical supply-use table framework for energy analysis on the energy conversion chain. Applied Energy, 226, 1134-1162
 Andreoni, V. (2017). Energy Metabolism of 28 World Countries: A Multi-scale Integrated Analysis. Ecological Economics, 142, 56-69
 Velasco-Fernández, R., Giampietro, M., & Bukkens, S. G. (2018). Analyzing the energy performance of manufacturing across levels using the end-use matrix. Energy, 161, 559-572
 Yu, S., Wei, Y. M., Fan, J., Zhang, X., & Wang, K. (2012). Exploring the regional characteristics of inter-provincial CO2 emissions in China: An improved fuzzy clustering analysis based on particle swarm optimization. Applied energy, 92, 552-562
 Liu, F., Yu, M., & Gong, P. (2017). Aging, Urbanization, and Energy Intensity based on Cross-national Panel Data. Procedia computer science, 122, 214-220
 Hardt, L., Owen, A., Brockway, P., Heun, M. K., Barrett, J., Taylor, P. G., & Foxon, T. J. (2018). Untangling the drivers of energy reduction in the UK productive sectors: Efficiency or offshoring?. Applied Energy, 223, 124-133.
 Weng, Y., & Zhang, X. (2017). The role of energy efficiency improvement and energy substitution in achieving China’s carbon intensity target. Energy Procedia, 142, 2786-2790.
 Akalpler, E., & Shingil, M. E. (2017). Statistical reasoning the link between energy demand, CO2 emissions and growth: Evidence from China. Procedia Computer Science, 120, 182-188.
 Odum, H.T. (1971) Environment, Power, and Society, Wiley, New York, NY, 1971.
 Hall, C.A.S., (1972) Migration and metabolism in a temperate stream ecosystem, Ecology, vol. 53 (1972), pp. 585 – 604.
 Solé, J., García-Olivares, A., Turiel, A., & Ballabrera-Poy, J. (2018). Renewable transitions and the net energy from oil liquids: A scenarios study. Renewable Energy, 116, 258-271.
 Mumford, L., 1964, Authoritarian and Democratic Technics, Technology and Culture, Vol. 5, No. 1 (Winter, 1964), pp. 1-8
 MacKenzie, D., 1984, Marx and the Machine, Technology and Culture, Vol. 25, No. 3. (Jul., 1984), pp. 473-502.
 Kline, R., Pinch, T., 1996, Users as Agents of Technological Change : The Social Construction of the Automobile in the Rural United States, Technology and Culture, vol. 37, no. 4 (Oct. 1996), pp. 763 – 795
 David, P. A. (1990). The dynamo and the computer: an historical perspective on the modern productivity paradox. The American Economic Review, 80(2), 355-361.
 Vincenti, W.G., 1994, The Retractable Airplane Landing Gear and the Northrop “Anomaly”: Variation-Selection and the Shaping of Technology, Technology and Culture, Vol. 35, No. 1 (Jan., 1994), pp. 1-33
 Mahoney, M.S., 1988, The History of Computing in the History of Technology, Princeton, NJ, Annals of the History of Computing 10(1988), pp. 113-125
 Ceruzzi, P.E., 2005, Moore’s Law and Technological Determinism : Reflections on the History of Technology, Technology and Culture, vol. 46, July 2005, pp. 584 – 593
 Taalbi, J. (2017). What drives innovation? Evidence from economic history. Research Policy, 46(8), 1437-1453.
 Duan, C., & Chen, B. (2018). Analysis of global energy consumption inequality by using Lorenz curve. Energy Procedia, 152, 750-755.
 Fuller S, McCauley D. Framing energy justice: perspectives from activism and advocacy. Energy Res Social Sci 2016;11:1–8.
 Broto, V. C., Baptista, I., Kirshner, J., Smith, S., & Alves, S. N. (2018). Energy justice and sustainability transitions in Mozambique. Applied Energy, 228, 645-655.
 Coelho, R. L. (2009). On the concept of energy: History and philosophy for science teaching. Procedia-Social and Behavioral Sciences, 1(1), 2648-2652.
 McKagan, S. B., Scherr, R. E., Close, E. W., & Close, H. G. (2012, February). Criteria for creating and categorizing forms of energy. In AIP Conference Proceedings (Vol. 1413, No. 1, pp. 279-282). AIP.
 Frontali, C. (2014). History of physical terms:‘Energy’. Physics Education, 49(5), 564.
 Geels, F., Kern, F., Fuchs, G., Hinderer, N., Kungl, G., Mylan, J., Neukirch, M., Wassermann, S., 2016. The enactment of socio-technical transition pathways: a reformulated typology and a comparative multi-level analysis of the German and UK low-carbon electricity transitions (1990–2014). Res. Policy 45, 896–913.
 Heiskanen, E., Apajalahti, E. L., Matschoss, K., & Lovio, R. (2018). Incumbent energy companies navigating the energy transitions: Strategic action or bricolage?. Environmental Innovation and Societal Transitions.
 Matschoss, K., & Heiskanen, E. (2018). Innovation intermediary challenging the energy incumbent: enactment of local socio-technical transition pathways by destabilization of regime rules. Technology Analysis & Strategic Management, 30(12), 1455-1469
 https://data.worldbank.org/indicator/FM.LBL.BMNY.GD.ZS last accessed November 25th, 2018
 https://data.worldbank.org/indicator/SP.URB.TOTL.IN.ZS last accessed November 25th, 2018
 https://data.worldbank.org/indicator/EG.FEC.RNEW.ZS last accessed November 25th, 2018
 Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer (2015), “The Next Generation of the Penn World Table” American Economic Review, 105(10), 3150-3182, available for download at www.ggdc.net/pwt