A New Formula to Quantify the National Energy Security of the World’s Top Ten Most Populous Nations

Quantification of global sustainable energy security (ES) becomes urgent, but the concepts of ES are still not clear. Thus, this paper is originated from philosophical ES studies, in which the various concepts came from the differences in determining the observed multi-matters (energy, equipment, human, and ecosystem: EPME) and point of view to see the EPME. Therefore, this research is aimed at measuring the EPME variables, producing ES material quantities (Q es ). Q es is derived after a 4-stage unification and is defined in a formula. The formula is then applied to calculate the top ten populous nations in the world from 1990 to 2015. Based on the top Q es values, the rankings are Russia (Fed.), USA, Japan, Brazil, China, Indonesia, India, Nigeria, Pakistan, and Bangladesh. The results also highlighted the Q es disparities between nations. A relationship between Q es and National Power Indicator (NPI) was also explored, indicating the ES saturation in the USA and Japan; and the macro energy-policy instability phenomenon in Nigeria. In addition, a comparison of Q es ranking to those of other scholars’ results was presented. Finally, the macro sustainable energy policy implication is also highlighted.


INTRODUCTION
Global energy system planning is more urgent, mainly due to global problems, including positive population growth, depletion of energy resources, inefficient energy consumption, energy export-transit-import involving many nations, and especially the threat of catastrophic climate change. In that context, energy security (ES) becomes an essential factor for any nation.
In line with these challenges, intensive ES research has carried out. For examples, ES observation based on historical evaluations for 18 nations spread across four continents , ES for resource-poor economies (Li et al., 2016), and ES for European Union nations have been studied (Matsumoto et al., 2018;Obadi and Korcek, 2020). Besides historical evaluations, a prognosis has also been carried out (Augutis et al., 2017). specific focus areas (SFA) including Availability -Affordability -Accessibility -Acceptance (4A) (Ang et al., 2015).
The 4A concept was first put forward by the Asia Pacific Energy Research Center (Intharak et al., 2007). Then (Cherp and Jewell, 2014), with the perspective of securities theory, examined the concept of 4A. They concluded that the concept of 4A was unsatisfactory because it did not answer the three basic questions: security for whom, for which values, and from what threats. Therefore, they proposed a new definition. The definition proposed by (Cherp and Jewell, 2014) by (Azzuni and Breyer, 2018) is considered unclear and too general. Thus, they offered a new definition.
To answer why the diversity of ES definitions occurred, an epistemological review of ES phenomena over the last 100 years, has been done (Nelwan et al., 2017). It was concluded that the diverse ES definition is due to the diversity of formal objects (obiectum formale, Lat.), and material objects (obiectum materiale, Lat.). The research revealed that ES material objects consisted of 4 elements: energy-equipment-humans and ecosystems (abbreviated as EPME). So, it is called EPME Concept. So far, EPME involved various formal objects (or scientific points of view): Politics, Geography, Economics, Technology, Ecology, Social, Culture, Military, et cetera.
The material and formal objects need to be integrated if a universal definition is required. Although concept of integration had been suggested explicitly (Cherp and Jewell, 2011) or implicitly (Zhou et al., 2018), the diversity of ES concepts continues (Jakstas, 2020). The conceptual diverseness was also reflected by assessment methods for obtaining ES indicators (Ang et al., 2015;Azzuni and Breyer, 2018;Narula and Reddy, 2015). Thus in the past 20 years, the concept of ES has not been integrated. Therefore, the research of global concept integration is still widely open.
In this research, we propose the development of the EPME quantification method to produce a macro indicator of a nation's ES (Q es ). In this case, EPME was observed from the perspective of Technology and Ecology. The observations linked to the national power indicator (NPI), introduced by a political scientist (Beckley, 2018). Because the NPI stated in Macroeconomic indicators, this research is relevant to the discipline of Politics and Economics.
Therefore, the objectives of the present research are to (1) determine the Q es quantification formula, (2) assess the nations' ES performance based on Q es , (3) correlate the relationship between Q es and NPI, (4) determine the relationship between Q es and relevant research results, and (5) recommend the macro ES policy implications.
The results of this research expect to contribute to the academic community in formulating international energy policies. We present contributions in the form of unique information on the development of ES in the ten most populous nations in the world from 1990 to 2015. We also expect that information is useful to determine future international macro energy policies. The other contribution is filling the gap of knowledge in ES Science with an alternative method of quantifying ES conditions.

RESEARCH METHODOLOGY
The scientific definition of EPME proposed by (Nelwan et al., 2017), stated: "…energy security is the knowledge of EPME collected from the results of multidisciplinary studies…" Axiologically, this knowledge is useful for enhancing the existence, defense, strength of an entity. In this research, the entity is limited to ten nations.
In this section, an attempt had been made to quantify the condition of EPME. Quantification uses the perspective of Technology and Ecology, which ultimately produces numbers with new units. For quantification purposes, the definition was derived into a specific definition or operational definition, which was in line with the method identified by (Ang et al., 2015).
The quantification of EPME is illustrated in Figure 1, where EPME material is placed on a measuring device -symbolized by a weigher.
The amount of EPME, Q es , can be imagined, analogous (but not the same) as the mass of physical objects (matter). Borrowing terms from Macroeconomics, Q es is categorized as a result of macro ES measurement. The amount of EPME, Q es , hypothetically will tend to grow if the wise decisions or policies resulting from the integration of a multidisciplinary perspective are applied.
The EPME quantity, Q es , is a number that is always determined positive because, physically, the elements of EPME are always "exist." The state of the EPME elements is unified into a quantity by a new formula. It will be presented in sub-section 2.2. The EPME unit [Q es ], is a unit formed by four elements: energy, equipment, human, and ecosystems. Generate a new unit that is: absolute, unlike the relative dimensional aggregate index -because of the process of normalization as done by (Bogoviz et al., 2019;Erahman et al., 2016;Song et al., 2019;Sovacool et al., 2011) and other scholars. The [Q es ] also universal because based on international units, and new in the sense of not yet suggested in previous research, but abstract as will be explained later.

Quantification the Elements of EPME
Q es depends on the magnitude of its elements. The higher the value (number) of Q es , indicating the higher the ES core material.

Energy quantity
The amount of energy, E, is the total primary energy supply (TPES) to the national energy system. The selection of TPES, not the total final energy consumption, is based on several arguments. First, historically energy security was about the security of energy supplies (UNECE, 2007). Second, the ES dimension that was considered the most important was the availability or supply of energy in a nation (Ang et al., 2015). Relationship of Q es with E is directly-proportional; the higher E, then Q es will also be increasingly enlarged. So the basics relation are: (1) The diversity of energy types will be explored in further research (see section 4).

Equipment quantity
The amount of equipment (p) is the amount that represents technological equipment that takes/processes natural energy into primary energy and then becomes the final energy within a national territory. The equipment is a whole device of exploration, exploitation, refinery, conversion, transmission, distribution, consumption and energy control technology, or equipment for transforming natural energy into primary energy, secondary energy, and tertiary energy or final energy. The technological (Azzuni and Breyer, 2018) or infrastructure (Ang et al., 2015) dimension is relevant to p. Considering the limited availability of global data, then the amount that represents p, is restricted to the capability of technological equipment to transform/convert TPES become final energy. Q es relation with the p is directly-proportional; the higher p, the higher Q es .
where i for transformation facilities such as electric power plants, oil refinery, etc. The other properties of the p like reliability, as well as equipment as an economic resource, will measure in further research.

Human quantity
The human (m) magnitude is the number of energy consumers in that nation. So, m is limited to the number of people or population. Because m viewed as a consumer (not a producer), the mathematical relationship Q es with m is inversely-proportional (not directly-proportional); so the more m, the lower Q es (not higher).
There are a lot of magnitudes related to a large number of people gathered in one nation suchlike as qualities involving values, norms, energy-saving culture, productivity, creativity (innovation), even energy justice (Sovacool et al., 2017). It will measure at further research. Also, the quantification of the effect of a small portion m as a regulator (or producer) of E is necessary to calculate.

Ecosystem quantity
The ecosystem element, e, represents by the quantity of energy emission and CO 2 emission that is produced by the energy system, which thrown back to the ecosystem as a waste. Energy emission (e 1 ) increases the ambient entropy, and CO 2 emission (e 2 ) will raise the global warming phenomenon or global catastrophic climate change. Of the many greenhouse gases, CO 2 chose, because based on 2006 IPCC Guidelines for National Greenhouse Gas Inventories - (IPCC, 2006), that: (1) gas emission from the energy sector, 95% in the form of CO 2 , (2) the energy sector contributes typically 90% of total CO 2 . This element is relevant to the environment dimension (Ang et al., 2015;Azzuni and Breyer, 2018).
Q es will increase when: (1) e 1 decreases (reduce), (2) e 2 decreases, Q es will increase. So the equation of the relation Q es with e 1 and e 2 : e = f (e 1 ,e 2 ) (7) where the units of e 1 in [GJ], and e 2 in 1000 [kg CO 2 ] or [tCO 2 ].

Formulating Q es
The Q es formulation states the whole process: energy supply (E) is transformed by equipment (p) to meet human consumption (m) either direct or in-direct. Some of the energy that converted loosen back to the ecosystem (e), as an emissions energy (e 1 ) and CO 2 emission (e 2 ). The whole process wants to be unified (condensed) into one number as a macro indicator for the national energy security condition.
Q es = f (E,p,m,e 1 ,e 2 ) The unification process of 5 variables quantities carried out with the following steps. First, to states that without p, the E as the public commodity cannot be consumed by m, either direct or indirect. E transformed by p, the E value will multiply. Therefore, E and p unified with multiplication operations, not a summation. Refer to equations (2) and (4), Q es become: Looking at the unit of Q es (E,p) in [GJ 2 ] -it appears that it no longer states a real physical-material unit; it is an abstract unit.
In order not to confuse the real physics unit, we define a unique unit in the field of Energy Second, the unification of the Q es (E,p) with m is carried out by the division arithmetic operation. It means ESS is ultimately distributed both direct and in-direct to consumers m at various sectors: industry, transportation, residential, commercial, etc.
Referring to equations (6), (11) and (12) the second unification results: ESS divided by the number of population defined as the average of ES-strength (ESs) given the symbol s. ESs is a compound too.
Third, the unification of e 1 with Q es (E,p,m) is done by reducing e 1 to E. Referring to equations (8) and (13) Equation (14) states that ESs (and also Q es ) will decrease if e 1 enlarges. The higher e 1 means the rejected energy as waste energy into the ecosystem, become enlarger. Physically, (E-e 1 ) is the same as useful energy. So Q es (E,p,m,e 1 ) defined as: 'Useful ESs' (UESs) compounds. Fourth, unification e 2 with the Q es (E,p,m,e 1 ) is carried out by division operation. Refer to equations (9) Equation (15) states e 2 as the ultimate constraint to maximizing UESs (and also Q es ). The more e 2 produced, the higher impact on global warming and increase the climate change catastrophic risk. It has become a threat to national and global sustainable development. The EPME Concept strengthens global sustainable development on earth. The Q es unit named a new term: [Esse], an evocative acronym for energy security for a sustainable earth. So that: At this point, the process of unifying five core variables has produced the core formula (15). Some variations of the core formula will state in the next related sub-section.

Data: Source and Processing Method
The present research is about involving the top ten populous nations in the world: China, India, USA, Indonesia, Brazil, Pakistan, Nigeria, Bangladesh, Russian Federation, and Japan. It reaches around 58% of the world's population. Observations made from 1990 to 2015.
E data stated by TPES obtained from Energy Balance (EB) issued by the International Energy Agency (IEA, 2019). If the TPES data quoted without needing to be processed, then the "p" data for each nation, needs to be processed using the formula: where, i for each transformation facilities: (1) Electricity plants, (2) CHP (combined heat and power) plants, (3) heat plants, (4) gas works, (5) oil refineries, (6) coal transformation, (7)  where j for each transformation facilities, are: (1) Electricity plants, (2) CHP plants, (3) heat plants, (4) gas works, (5) oil refineries, (6) liquefaction plants, (7) transmission and distribution. Data E input and E output each facility cited from (IEA, 2019). Data e 11 is converted to efficiency primary to final energy (η pf ): where, E is TPES, (E-e 11 ) is the final energy. Knowing final to useful energy efficiency (η fu ) then can get primary to useful energy efficiency η pu (or overall energy efficiency): Using the value η pu , then e 1 can be determined: These steps, (18)-(22), were carried out because, as far as our efforts, e 1 data for ten countries from 1990 to 2015 not found from one source. Indeed, the Lawrence Livermore National Laboratory (LLNL), in collaboration with the US Department of Energy (DOE) publishes an energy flow chart (Sankey chart) that contains data (e 1 ) and (E-e 1 ) for many nations (LLNL, 2019). But published data use the assumption that η fu of the consumption sector in 9 countries is almost the same as the USA. Differences are only caused by decimal rounding and statistical differences. Indeed, (Zhang et al., 2011) published estimates related to e 12 for 1990-2009, but only for China in the transportation and residential consumption sector. Likewise, (Amoo and Fagbenle, 2014) published the estimation, which is related to e 12 for 1988-2009, but only for Nigeria in the transportation consumption sector. Also, other research results (about efficiency) only concern one nation and one-two consumptions sector. Finally, we cite efficiency data reported by Professor Nakicenovic and colleagues in several scientific publications. For η fu in 1990 (Gilli et al., 1995;Nakićenović et al., 1996), and η fu in 2005 (Johansson et al., 2012). After knowing η fu , using (20) and (21), η pu was obtained for 1990 and 2005. Then η fu for 2015, was calculated using formula (23), assuming the η pu curve from 1990 to 2015 is linear, obtained: where, t 1 for 1990, t 2 for 2005 and t 3 for 2015. Linear assumptions had made after studying the results of research (Ayres et al., 2003). It revealed the 1900-1998 energy (exergy) efficiency curve for the USA, where the 25-year curve, in general, tends to form a straight line. Assumption strengthened after studying research results, among others (Badmus and Osunleke, 2010;Chowdhury et al., 2019;Chowdhury et al., 2020;Kondo, 2009;Mitra and Gautam, 2014), regarding other efficiencies. The estimation results using formula (23) are then compared with the results of historical efficiency improvements on average: 1 [%/year] (Gilli et al., 1995). Know η pu , e 1 can compute by the formula (22), and vice versa.
The e 21 data quoted from European Commission-Joint Research Centre-Emission Database for Global Atmospheric Research (EDGAR) (Crippa et al., 2019). EDGAR estimates e 21 based on EB-IEA data. CO 2 reduction by carbon capture and storage facilities data, quoted from the Global Carbon Capture and Storage Institute (GCCSI, 2015).

Beckley Method
Historically-ontologically, the concept of ES was born before World War I (Yergin, 2006). Since then, ES has become an important element in national power. Therefore, an examination of the relationship between Qes and the national power indicator (NPI) was carried out. The new NPI calculation method discovered by Michael Beckley (Beckley, 2018). Beckley formulated NPI by explaining that GDP as a macroeconomic and military indicator and GDP/capita is a rough-indicator of economic and military efficiency: Beckley has demonstrated, conclusively, that the NPI (25) can explain the superiority of a nation in competition between nations, from 1839 to 2015.
The NPI, calculated using the formula (25), and using GDP-PPP data (constant 2011 USD) quoted from the World Bank (WB, 2019). Whereas population data from the UN Population Division (UN, 2018). The relationship between Q es and national power indicator (NPI) examined by calculating the correlation coefficient using the Pearson linear method (Dowdy et al., 2004).

Rank Comparison Method (RCM)
RCM is useful for comparing 2 ES rankings, each of which applies different ES concepts and quantification methods. For explanation here, the results of the research  called ES performance (ESP), are taken as a comparison. ESP ranking (covers 18 nations) compared to Q es ranking (covers 10 nations).
The RCM consists of 4 steps. First, the suitability of the assessed nation examined. It turns out that only five nations were suitable, namely: the USA, Japan, Indonesia, China, and India. Second, the ESP ranking number (original) is 1. Japan, 3. USA, 11. Indonesia, 14. China, 17. India; modified to 1. Japan, 2. USA, 3. Indonesia, 4. China, 5. India. Even though the ranking number modified, the rank order has not changed, for example, Japan remains above the USA. Likewise, the ranking number on Q es is modified too. Third, the rank correlation coefficient (r) determined with the Spearman method (Dowdy et al., 2004). Fourth, after r is known, a qualitative check performed to answer why the r values occur.

RESULTS AND DISCUSSION
These results: the quantification formula, the performance of ten countries from 1990 to 2015, the correlation between ESs (also Q es ) and NPI, and the comparison of Q es with other scholar results, are explained and discussed in this section. Also, another point of view, especially Economics, for further research briefly explained.

Formula Results and Discussion
The formula results will be separated into several compounds to obtain more results and more in-depth discussion material. Also, with simple terms, abbreviations, and symbols. Combine the formulas (15) and (16), then formula (26) is generated: The formula (26) is also an operational definition to know the EPME condition quantitatively, which when formulated in a definitive sentence, is: primary energy supply (E) flows continuously through a set of conversion/transformation technology (p) so that it can be consumed either directly or indirectly by humans (m); always produce emission waste (e 1 and e 2 ) which harm the ecosystem (e), which in the medium and long term will threaten the continuity of the flow of E to p and especially to m in the nation; finally throughout the world.
The formula (26) shows that to increase the Q es value is by reducing CO 2 emission, such as the intensification of non-carbon energy resources like hydropower, solar PV, wind power, geothermal, and nuclear. The formula (26) also shows the importance of CO 2 emission reduction to mitigate climate change disaster. The risk of catastrophic climate change is increasing because, in reality, the global facts show that CO 2 emissions from burning fuel continued to enlarge in 2000-2017 (IEA, 2020a).
The Q es unit, [Esse], is an abstract unit, meaning that Q es represents an abstract ES. It is suited to ES as an abstract concept (Jakstas, 2020 Furthermore, referring to formulas (26) and (27), formula (28) can be generated, Then the denominator, symbolized by the letter w, becomes: where w for waste. So, the term for w is ES-waste (ESw) compounds. Define a new unit for w: where W es stands for waste energy security.
Remembering the primary to useful energy or overall efficiency (η pu ) as (31), then the formula (29) becomes (32): Setting w in η pu will be more flexible than using the e1 variable, in the effort to develop methods. Energy Economics combines energy efficiency with the energy effectiveness produces an indicator: energy intensity, which is used by (Brown et al., 2014;Pysar, 2019;Sharifuddin, 2014), as well as many other scholars. Next, the formula (26), when stated in s and w, becomes a much simpler formula: where s stands for ESs, and w stands for ESw.

Energy Security Strength
This sub-section contains ESS compounds, ESs compounds, and ESs correlation to National Power Indicator (NPI). Each part discus the conditions of 10 nations in 1990-2015.

Total strength (ESS)
ESS calculated using equation (11) While the USA, Nigeria, Japan, and Russia fluctuated. The USA initially grew positively until 2005, then negatively. Nigeria initially grew negatively until 2000, then turned positive. Russia grew negatively from 1990 to 2000, after that, it was positive. Until 2005, Japan grew positively, but after that, it experienced negative growth.

ESS for six nations tends to increase, four nations fluctuated.
Inward looking, of course, caused by internal EPME variables: E and p, where E and p influenced by human (m) needs in that nation. Outward looking, of course, there are external variables that lead to the achievement of national ESS, for example, foreign investment security, which preceded by national, regional, and global political stability.

Average strength [ESs]
Table 2 and Figure 2 show the ESs (ESS divided by the total population) results. Figure 2 displays the 1990-2015 ESs with a logarithmic scale to shorten the vertical distance (y-axis). It also displayed the nation ranking in 2015. It appears that the USA has been in the top ranking continuously for 25 years. China, which in 1990 was ranked 4th, surpassed Japan in 2010 and then Russia     ESs is stable or not, caused by the internal variables E,p, and m. Outward looking, there are external variables that affect the achievement of national ESs. Among others, the variable of the nation's interaction between exporters and importers E and or p. Which, based on nations' sovereignty, which is a Political Science, International Relations, and Security Studies research field (Cherp and Jewell, 2011). National Sovereignty is one of the external variables in the EPME Concept.

Relation ESs and NPI
Comparing the formula ESs (27) with NPI (25), it appears that there are at least two similarities between ESs and NPI: (1) formula structure, and (2) abstract measurement units. The abstract unit of measurement, not explicitly stated by Beckley in his paper but rather implicitly. The NPI unit [USD2/Capita], disappear as a result of a division/comparison operation between the two nations (Beckley, 2018). So it can be concluded that NPI with ESs (and also Q es ) generally has a strong correlation over a certain period. So it can also be stated that the NPI-Beckley concept has a strong correlation with the EPME Concept. Because NPI is stated in the quantity of Macroeconomics, EPME related to Economics also. Between NPI and Qes, which one is the independent and dependent variable, will be examined later on.

Energy Security Waste (ESW)
ESw (w) is relevant to the environment and sustainable energy concepts, either nationally or globally. Due to the limitations of e12 (emission in final to useful energy transformation) data for ten nations, observations only made for 1990, 2005, and 2015.

Energy emissions
Energy emissions (e 1 ) after being transformed by formula (31) can be expressed as primary to useful energy efficiency (ηpu).

CO 2 emissions
The estimated CO 2 emission (e 2 ) is displayed in Table 5

Combination of energy and CO 2 emission
The combination of energy emissions and CO 2 emissions forms ESw (ESw), as shown in Table 6. It can be stated that: Brazil, Nigeria, Russia, and Japan succeeded in reducing waste emissions. This indicates the consistency and effectiveness of macro energy policies in the 4 nations in suppressing ESw from 1990 to 2015.
The USA succeeded in suppressing ESw from 2005 to 2015, whereas five other nations: China, India, Indonesia, Pakistan, and Bangladesh, showed a tendency to increase from 1990 to 2015.

Quantity of ES (Q es )
Quantity of ES (Q es ) had obtained by substituting the ESs and the ESw values to formula (33). From the Q es ratio number (r Q ) in Table 7, it appears that China's Q es in 2015 grew 8 times compared to 1990. Conversely, the USA was 78%, which is the same as 22% shrinkage. The other 7 countries have more than doubled in size, while Nigeria has only 153%. If the USA ratio continues to decline, and if the China ratio figure remains 801% in the next 25 years, China may surpass the USA before 2040. This prognosis is very rough, needs to be refined through other research, which is more in-depth about the interaction of internal and external variables. Furthermore, by pulling two benchmark numbers of 100 and 700 [Esse], the ten assessed nations will be divided into three groups: (1) High-level nation, during the year of observation consisting of the USA, Russia, and Japan, (2) Middle-level nation, initially only consist of Brazil, then China, Indonesia, and then India followed in 2015, and (3) Low-level nation, initially consist of 6 nations, then decreased by 2 nations in 2005, and then 2 more in 2015.
Pakistan and Bangladesh remain at a low-level; despite showing improved performance.
The Q es ratio between nations in 1 year provides significant information, as it shows ES disparity between nations. In 1990, the ratio of rank 1 to rank 10 was 195:1, in 2005, it dropped to 89:1, and then in 2015, it dropped to 55:1, respectively. These numbers show that disparity is getting lower, and is a relative number. But in absolute terms, it shows a clear real inequality. This inequality is clearly shown in Figure 3, where the figure was not changed on the logarithmic scale, as it was in Figure 2.
The disparity became disguised and disappeared if the normalization in the aggregation process was done. For example, the normalization method used by   The problem of disparity is evident in that the Russia-USA-Japan Q es value was far superior to that of the Bangladesh-Nigeria-Pakistan. When compared to the value of ESw, Bangladesh-Nigeria-Pakistan is more superior to Russia-USA-Japan. Therefore, to reduce the disparity of Q es and ESw, it is proposed the synergistic cooperation. The form of the cooperation program, for example, has been proposed by (González-Eguino, 2015), namely the improvement of modern energy infrastructure, so that health problems due to the use of traditional biomass are significantly reduced. More specifically, we propose a collaboration (mutually beneficial) to improve traditional biomass processing infrastructure into modern biomass (biofuel). In other terms, it is pertaining to a reduction of global energy poverty.

Comparison to Other Research
This section refers to section 2.5. about the Rank Comparison Method. The ES performance (ESP) of 18 nations, including China, India, USA, Indonesia, and Japan, for the years 1990-2010 was reported by . The modified Q es and ESP rank shows in Table 8.
Using the Spearman method, obtained the correlation coefficient, r = +0.6 (moderate relationship) in the year 1990, and r = +0.     that the correlation in the ranking depends on the number of indicators and the number of nations. And of course, it is determined by the point of view in observing the core material of a national ES.

FUTURE RESEARCH
The concept of EPME will penetrate to the quantification of the discipline-based perspective (D) of science. Dimensions that have so far been developed: such as 7 dimensions (Ang et al., 2015), or 15 dimensions (Azzuni and Breyer, 2018), which developed until 2018, will be regrouped according to the field of science (D).
Also, given the different factors of national interest (exporters, importers, etc.) will be quantified as a weight value (W). Finally, risk factors (R), will be accommodated in the calculation/ aggregation index. Because basically, the word "security" in ES states the conditions that result in responding to various risks. The concept of risk (or threat) in ES already expressed by scholars (Kiriyama and Kajikawa, 2014;Winzer, 2012;Zhiznin et al., 2020), and the response to the risk of supply failure is basically in the keywords: diversity (Yergin, 2006).
Mathematically, the formula for obtaining multidisciplinary macro indicators that have the potential to become universal indicators is stated as follows: where, Uqes: universal Q es (R) 1a : risk matrix, (W) ab : weight matrix, (D) b1 : scientific perspective matrix Q es : quantity of ES material Esse: Q es unit.
The matrix determined by the number of scientific perspectives (b) and the number of risk factors (a). If the aggregation method is chosen with the same weight, then all components of the W matrix, are 1. Future research on D, W, and R, requires collaboration across disciplines, institutions, and nations. Formula (35) can be developed for historical evaluation and prognosis.

CONCLUSION AND POLICY IMPLICATION
This paper has explained the EPME unification process to obtain Q es . Q es is a macro indicator of national energy security conditions.
For macro-level analysis, Q es derived into two compounds: the strength compound (ESs) associated with 'Epm' and the waste compound (ESw) associated with "e 1 e 2 ". Q es , ESs, and ESw have been used to measure the performance of the ten most populous nations in the world from 1990 to 2015.
Based on ESs indicators in 2015, the rankings are the USA, China, Russia, Japan, India, Brazil, Indonesia, Pakistan, Nigeria, and Bangladesh. Since 1990, for 25 years, the USA has continued to rank first. Between 1990 and 2015 China and India had managed to rise in rank, resulting in downgrades in Russia, Japan, and Brazil. While Indonesia, Nigeria, Pakistan, and Bangladesh continue to rank 7 th , 8 th , 9 th , and 10 th .
ESs of 9 nations strongly correlated with National Power Indicators (NPI). The only nation with an unstable correlation is Nigeria, indicating ESs macro policy instability for 25 years. After observing the correlation coefficient between ESs and NPI from 1990 to 2015, it concluded that there was a maximum ESs for the USA and Japan. Below that number, ESs strongly correlated with NPI. After passing that number, the correlation is strong but negative. This empirical fact shows the phenomenon of 'saturation'; in the USA, occurred before 2005, in Japan occurred after 2005. This phenomenon raises a hypothesis about the maximum ESs that is typical in developed countries.
ESs saturation in the USA and Japan, it is necessary to research in-depth the root cause first. 2. To improve the Q es performance in Russia and Brazil are recommended to reduce ESw while maintaining the value of ESs. 3. Based on the Q es value, the difference measured between rank 1 and 10 has been getting smaller over the past 25 years. It shows that the global ES gap relatively reduced. But in absolute terms, it still contrasts. To reduce the inequality it is recommended to increase cooperation between highlevel countries (Russia, US, Japan) and low-level countries (Pakistan, Bangladesh). And also, Nigeria (low middle-level) which has shown energy policy instability in the past.

ACKNOWLEDGMENT
This research was supported by the University of Indonesia (UI) through grant PUTI Doctor 2020 launched by DRPM UI.