Iran’s gas export portfolio risk analysis based on the development of dependency risk framework

Abstract

This study attempts to provide a novel analytical framework for assessing Iran’s gas export portfolio risk considering the aggregate dependency index of natural gas of importing countries and geopolitics factor. After providing indicators related to each of the natural gas transmission methods, the aggregate dependency index of each country is determined using the entropy-based aggregating method. This paper considers pipeline and liquefied natural gas. In this study, 17 countries are selected to evaluate the risk of dependency in natural gas exports. With respect to Iran’s exporting natural gas plans, gas export portfolio risk is calculated based on two proposed scenarios. Considering two methods of transmission, the results show that gas export portfolio risk will be 63.4% at the lowest risk level.

Keywords

Aggregate dependency index Iran’s gas export portfolio entropy-based method gas pipeline liquefied natural gas

Introduction

Natural gas consumption has grown significantly in recent decades. Countries increase their natural gas consumption because of the need for diversification of the energy portfolio to ensure energy security in different demand sectors, current overwhelming concerns about environmental issues, and the desire to enjoy the benefits of natural gas. The average annual growth rate of natural gas consumption was 2.27% from 2005 to 2015, which shows a higher growth than that of other major energy carriers, including crude oil and coal, whose growth rate is 0.97 and 2.06%, respectively.¹ Iran with about 1200 trillion cubic feet of natural gas reserves is considered as the first gas-rich country of the world. Also, Iran with 173.2 million barrels of crude oil equivalent of the production of natural gas is assumed as the fourth country among exporters of natural gas.¹ Thus, it can be one of the most important actors of the world in the future in many aspects.

Studies on energy security and vulnerability have largely focused on energy-importing countries.^2–7 Despite presence of some studies in the literature on gas exporting countries,^8,9 they do not provide any framework on gas export portfolio risk (GEPR). In addition, many studies have been carried out on the risk analysis of energy portfolios. These studies, like energy security and vulnerability studies, have focused also mainly on countries that rely on energy imports and analyzed portfolio risk based on energy price variations.^10,11 Considering the concept of GEPR, the present study proposes a new approach to assess the risk of natural gas exports from the perspective of gas exporting countries. The innovative aspects of this study consist of three components including proposing a conceptual framework for energy dependency risk based on vulnerability concept, applying an aggregated method to evaluate GEPR, and incorporate two mentioned risks to evaluate GEPR. Considering the interests of the natural gas exporting countries, this approach provides the concept of the dependency risk on natural gas imports based on the concept of market risk and vulnerability concept. The proposed framework used in this paper also is different from those studies that analyze portfolio risk based on energy price variations. Afterward, based on the vulnerability concept, the related indexes are introduced. In the next step, the indicators are combined based on the entropy-based method for obtaining the aggregate index. To evaluate the GEPR, different methods of exporting natural gas, including pipeline and liquefied natural gas (LNG) transmission method, are considered in the calculations, followed by calculating the total dependency risk associated with each country. Finally, according to the computed aggregate dependency index (ADI) of each country and geopolitics index (GI), GEPR is evaluated in two scenarios related to Iran’s gas export plans.

Literature review on risk and vulnerability

Winzer¹² believed that the difference in the concept of energy security proposed by researchers is related to a variety of risks which researchers select for analyzing energy security. He argued that risk originates from the technical, human, and natural sources. According to Intharak et al.,¹³ direct or indirect risk sources are related to four dimensions proposed by APERC that analyzes the source of natural and economic risk as acceptability and affordability.

The vulnerability is a concept employed to show various risks affecting the security of a country’s energy. Considering the studies on estimating the vulnerability of the importing country, some researchers have examined two types of risk, including market and supply risks.^4,7 Cherp and Jewell argued that the concept of vulnerability is related to the risk of system resilience. They considered resilience as the ability to maintain the system under conditions of pressure and shock from the outside of the system and return to predisrupted operating conditions, which include a variety of suppliers, emergency plans, and technology diversity.¹⁴

Vulnerability, also, has been studied in the energy, environmental, and ecological areas. Adger studied vulnerability in the field of the natural social system. He argued that some factors, such as exposure to perturbations or external stresses, sensitivity to perturbation, and the capacity to adapt, are related to vulnerability.¹⁵ Gallopín¹⁶ investigated the relationship between vulnerability, resilience, and compatibility capacity in social–ecological systems. Kruyt et al.¹⁷ considered dependency as a combination of the net energy import ratio to total energy supply and diversity (Shannon index). Böhringer and Bortolamedi¹⁸ investigated energy security based on four dependency indicators, including primary energy, external energy imports, energy carrier, and external energy suppliers.

Development of the conceptual framework

Since interests of energy exporting countries are different from those on the interests of energy-importing countries and, on the other hand, the interests of energy-importing countries affect gas exports, it is important to provide a different analytical framework from the perspective of exporters. In this study, we present the concept of gas dependency based on vulnerability. We assume that the dependency on energy imports is important from the perspective of exporters and their interests. In fact, energy-producing countries evaluate their energy export portfolio risk based on the level of energy dependency risk of energy-importing countries. This study considers the development of the conceptual framework by identifying the factors and indicators that affect the risk of dependency of energy-importing countries.

The concept of dependency on energy imports can be developed using the concept of vulnerability. According to the literature on vulnerability proposed by Adger¹⁵ and Gallopín¹⁶ and the vulnerability concepts in the field of energy security proposed by Böhringer and Bortolamedi,¹⁸ adaption capacity, system sensitivity, and GI were considered to develop the proposed conceptual framework.

In addition to the dependency on energy imports, geopolitics is another element of the conceptual framework. Diversity plays a central role in the geopolitics risk that refers to possible ways of exporting natural gas for various export purposes, leading to the reduction of risk exposure (vulnerability) of economic benefits of the export portfolio. In addition to these factors, the political risk of each country affects the geopolitics.^4,6,19 Figure 1 summarizes the above-mentioned proposed framework on the risk of the gas export portfolio.

Figure 1.

Schematic framework of the risk of gas export portfolio. Source: Based on the proposed model. LNG: liquefied natural gas.

The present conceptual model of the dependency risk is proposed based on the adaption capacity and the concept of system sensitivity. Indicators related to the adaption capacity should reflect the ability of the system to adapt to changes, price shocks, and disruption to natural gas imports. For instance, the ratio of domestic gas production to gas consumption is identified as the adaption capacity index. This index states the extent to which the domestic production of natural gas can respond to the primary energy demand when a change is made to the system. The higher the ratio, the greater the adaption capacity of the system will be. Moreover, the diversity of suppliers is one of the indicators of the stability of a system. The greater the diversity of natural gas imports from different countries, the greater the capability of the importing country to switch to other suppliers in order to offset the decline in natural gas consumption.

Indicators related to the sensitivity of a system determine the extent to which changes in the external environment affect the gas supply system. The higher the sensitivity of the system to price changes and reduction in energy imports, the greater the risk and the vulnerability the system face. For example, energy intensity considered as the relevant index is equal to the ratio of the primary energy consumption to GDP. GDP is calculated based on purchasing power parities (PPPs) to estimate the energy intensity. The higher this index, the more the dependency risk on energy import due to low efficient energy structure. Also, CO₂ intensity as another relevant index plays an important role in energy dependency based on the environmental dimension. Given the low carbon dioxide emissions from natural gas, the higher this indicator, the more the countries tend to use natural gas and the higher the dependency on it.

According to Table 1, two groups of subindicators of dependency risk affect natural gas imports via pipeline and LNG transmission methods.

Table 1.

Subindicators of dependency risk affecting natural gas imports via pipeline and LNG.

	Pipeline/LNG index
Adaption Capacity	Domestic alternative energy (including renewables, nuclear, and hydropower energy) to primary energy (PE)
	Energy exports (or imports) to primary energy
	Domestic gas production to gas demand
	Gas supplier diversity
	Gas availability per gas demand
Sensitivity	Energy intensity (MJ/$2011 PPP GDP)
	Gas imports to gas demand
	Share of primary gas consumption of PE
	CO₂ intensity (kg per kg of oil equivalent energy use)

LNG: liquefied natural gas; PPP: purchasing power parity.

In this paper, we suppose that dependency index as an unobserved indicator is linearly related to above indexes along with a term error as follows

A D I_{t, k} = \sum_{i} α_{i, t, k} X_{i, t, k} + є

(1)

where

A D I_{t, k}

is the ADI of country “k” to which gas is exported via transmission method of “t,”

X_{i, t, k}

are proposed indexes of country “k” and gas transmission method of “t,” and

є

is the error term.

GI

This index is used in energy security studies and estimation of energy import vulnerability. It is defined as exposure to the risk of energy supply interruption due to their political instability. This paper evaluates the GI by the combination of the political risk index of the exporting countries by utilizing Herfindahl–Hirschman index,⁶ which is the square of the share of natural gas exports to each country, as follows

G e o p o l o t i c s I n d e x (G I) = \sum_{k} \sum_{T} P_{t, k}^{2} (1 - P R R_{k})

(2)

P_{t, k} = V_{t k} / V_{t o t a l}

(2a)

V_{t o t a l} = \sum_{t} \sum_{k} V_{t k}

(2b)

where P_t,k is the share of gas exports to country “k” based on transmission method “t”, V_total is the total volumetric gas export of Iran to N country, and PRR_k is the political risk of the country “k”. A high value of PRR_k indicates the high political stability of the country “k”.

Aggregate methods

Entropy aggregate method is one of the widely used methods in various studies. However, this method is rarely used in the area of energy security and vulnerability literature. The method is mainly used in studies on the evaluation of the vulnerability of environmental systems such as groundwater, soil, and surface waters.^20–23 It is also used in studies that need to weigh different factors to obtain the desired scale. Al-Abadi et al.²⁴ and Amiri et al.²⁵ used this method to measure the groundwater resources potential. Pei-Yue et al.²⁶ employed this method to estimate groundwater quality in China. These studies have examined the effect of different factors on the index and the different regions are ranked based on the index.

Entropy aggregate method used in this paper to determine coefficients of formula (1) is summarized as follows:

Considering a positive or negative relationship between indexes and the degree of dependency, we normalize the indexes and make the indexes in positive relation to dependency as follows

X_{i k} = \frac{x_{i k} - \min (x_{i k})}{max (x_{i k}) - \min (x_{i k})}

(3a)

X_{i k} = \frac{max (x_{i k}) - x_{i k}}{max (x_{i k}) - \min (x_{i k})}

(3b)

Thus, the matrix element values X_ij are between 0 and 1. Zero indicates the lowest dependency, and one indicates the highest dependency.

The P_ij ratio is formed based on the X matrix, where “i” is country and “j” is index²⁵

P_{i j} = \frac{X_{i j}}{\sum_{i = 1}^{m} X_{i j}}

(3c)

• The information entropy for each of the matrix components is calculated according to the following equation^23,25

e_{j} = \frac{- 1}{L n (m)} \sum_{i = 1}^{m} P_{i j} L n (P_{i j})

(3d)

where “m” is the number of countries under study.

Then, the weight of each index is obtained as follows

w_{j} = \frac{1 - e_{j}}{\sum_{j = 1}^{n} (1 - e_{j})}

(3e)

where W_j is the weight of index of “j” and “n” is the number of indexes under study.

Finally, the ADI for each country “i” is calculated as follows

A D I_{i} = \sum_{j = 1}^{n} w_{j} P_{i j}

(3f)

Evaluation of GEPR

The risk of the natural gas export portfolio is calculated based on the volumetric value of natural gas exported to each country. By assuming that the natural gas importer countries are unrelated, i.e. they have a parallel relationship from the perspective of the exporting country, the GEPR for selected importer countries can be obtained as follows

G E P R = 1 - (1 - G I \underset{\prod_{k} (1 - V_{t o t a l, k} \frac{[1 - \frac{\sum_{t} V_{k, t} A D I_{k, t}}{V_{t o t a l, k}}]}{V_{t o t a l}})}{)}

(4)

V_{t o t a l} = \sum_{t} \sum_{k} V_{t k}

(4a)

V_{t o t a l, k} = \sum_{t} V_{t k}

(4b)

where “k” is the country which imports natural gas from Iran through transmission method “t”, including pipeline, LNG, GTP, and GTL. V_k,t is the volume of natural gas exported to the country “k” by the method “t”. V_total is the total natural gas export from Iran through four transmission methods.

Data and selection of destination countries

Data used to calculate the mentioned indexes are obtained from different sources. The data on GDP of countries were extracted from World Bank statistics based on the 2011 PPP.²⁷ BP Statistics¹ was used to obtain the values of primary energy, natural gas production and consumption, natural gas trading, CO₂ production, alternative energy consumption, and energy intensity. The countries were selected based on current government planning and policies, including current export plans, memorandum of understanding, and future export contracts in which the relevant infrastructure is under construction, as well as countries in the upcoming Iranian exports plan. Table 2 presents Iran’s plan for exporting natural gas during the next five years.²⁸ According to Table 2, the list of destination countries for natural gas exports will include the countries having export contracts with Iran such as Turkey and Armenia besides Iraq, to which gas export currently is underway. Other countries such as Pakistan and Oman are considered as potential gas importer via pipeline in the next few years. India will also be a destination for the upcoming gas exports, both in pipelines and LNG.²⁸ European and Far East Asian countries are also willing to import gas from Iran, and in the future via LNG. In this paper, gas exports to some of these countries are considered as LNG.

Table 2.

Iran’s gas exports plan through pipeline and LNG between 2020 and 2022 (Unit: MMCMD).

Country/Export method	Pipeline	LNG
Turkey	30	–
Iraq	50	–
Oman	25	0.0
Pakistan	25	0.0
Armenia	2	–
Austria	0.0	–
India	25	3.4
UAE	0.0	0.0
China	–	4.1
Italy	0.0	0.9
Spain	0.0	2.1
France	0.0	1.0
Germany	0.0	0.0
Hungary	0.0	0.0
Japan	–	18.6
Korea	–	6.8
SUM	157.0	37.0
Share of export method%	72.7	17.1

LNG: liquefied natural gas; MMCMD: million cubic meters per day.

Source: NIGC²⁸ for Pipeline and BP¹ is used for LNG export calculations.

To export natural gas through LNG, LNG plants are currently under construction in Iran. After completion, LNG will be exported to countries with LNG receiving facilities such as India, China, South Korea, Japan, and some European countries.²⁸ The export plan is estimated to be 157 million cubic meters per day (MMCMD) through the pipeline (81%) and 37 MMCMD (19%) through LNG (Table 2). As presented in Table 2, rates of gas exported via LNG are determined based on the current situation on the amount of imports traded via LNG by different importing countries in 2015.¹

Results and discussion

ADI model results

According to the entropy-based aggregate method, at first step, dependency subindexes of different countries are normalized. In normalizing step, index of the ratio of imports via pipeline to via LNG and the ADI index are considered to be positively related.

After calculating the normalized matrix, the P_ij ratio matrix, the amount of entropy e_j, the weight of the w_j index, and finally ADI were evaluated for each country. The weight values of each indicator for transmission methods are presented in Table 3. The table also shows the share of the adaption capacity and system sensitivity in the ADI.

Table 3.

The weight of indicators of pipeline and LNG method.

Index	Pipeline	LNG
Energy intensity	0.17	0.14
Gas import to gas demand	0.11	0.10
Energy export (or import) to PE	0.07	0.07
Share of domestic alternative energy of PE	0.04	0.03
Share of gas consumption of PE	0.15	0.14
Domestic gas production to gas demand	0.11	0.11
Gas supplier diversity	0.26	0.34
CO₂ intensity	0.06	0.04
Gas availability to gas demand	0.03	0.03
System sensitivity	0.49	0.42
Adaption capacity	0.51	0.58

LNG: liquefied natural gas.

In Table 3, the supplier diversity index indicates the highest impact on the risk of export dependency through pipeline and LNG by 26 and 34%, respectively. Therefore, given the current suppliers of LNG, the diversity of suppliers plays a more important role in gas import dependency. In both methods, energy intensity and share of gas consumption of PE are ranked second. The gas availability to gas demand, CO₂ intensity, and the share of alternative energies have lower weights indicating that three indexes do not play a significant role in country’s gas import dependency. In the LNG and pipeline method, the adaption capacity has a greater weight than the system sensitivity to dependency. Considering the adaption capacity in both transmission methods, especially the LNG method, provides an appropriate perspective for developing appropriate policies to reduce the dependency of the importing countries by increasing their adaption capacity especially via diversifying the gas supplier. As expected, according to the index of a share of gas consumption in PE weight, demand for natural gas plays a significant role in the natural gas import dependency.

What an exporting country is searching for is evaluating ADI, which is an important index to assess its natural gas exports plan. The calculated ADI of chosen countries is normalized between 0 and 1 to compare the different countries in terms of the dependency risk (Figure 2). According to Figure 2, the countries such as Pakistan, India, and Iraq have the lowest dependency on natural gas imports through pipelines and the countries such as Armenia, Spain, Germany, and Turkey have the highest dependency, with Pakistan and India having the lowest value. This pattern varies for imports through LNG, so that most of the countries have the lowest dependency (especially Iraq and Pakistan), other than Japan and South Korea, which have the highest dependency on LNG.

Figure 2.

ADI of the selected countries for two transmission methods. ADI: aggregate dependency index; LNG: liquefied natural gas.

Results of calculations of the GEPR

According to equation (4), the GI and the rate of natural gas exports by each of the transmission methods are required to calculate GEPR. The GI was obtained using political risk data from the PRS Group in 2015.²⁹ In the present study, two scenarios were proposed to evaluate GEPR for transmission methods. In the first scenario, gas exports were carried out through the pipeline. In the second scenario, gas exports were carried out through both the pipeline and LNG. The rates of gas export in both scenarios are based on Table 2. Table 4 presents the results of the calculations for GEPR based on the above scenarios.

Table 4.

Results of GEPR based on two proposed scenarios.

No.	Scenario	GEPR %
1	Pipeline	71.4
2	Pipeline + LNG	63.4

GEPR: gas export portfolio risk; LNG: liquefied natural gas.

As shown in Table 4, the risk of GEPR through pipelines would be 71.4%. GEPR is reduced by 63.4% due to an increase in diversity of natural gas exports resulting from the addition of LNG share.

Conclusions

In the proposed model, the dependency risk indexes are subindexes of the adaption capacity and the system sensitivity. The entropy-based aggregate method is used to calculate the ADI. According to the ADI weights, the diversity index is one of the most effective indicators for the gas import dependency through pipelines and LNG. In these two methods, in particular, the LNG method, the share of adaption capacity was greater than the share of system sensitivity. Therefore, an increase in the adaption capacity based on the diversity of exporters would reduce the dependency risk. Considering the high diversity of suppliers in the LNG method, a proper policy for exporting countries is the construction of LNG infrastructure to increase the gas export through LNG to importing countries.

Also, the entropy-based aggregate method, in addition to calculating the weight of each subindex, provides ADI for each country in two natural gas transmission methods. After normalizing the results between 0 and 1, the ADI of each country can be compared with each other. The results show that Armenia, Spain, and Germany have the highest natural gas dependency on the pipeline transmission method, and Japan and South Korea are the most dependent on LNG imports.

The results of the calculations showed that the pipeline transmission method had the highest value (71.4%) because of the significant share of Iran’s natural gas exports via pipeline. According to the results, the share of gas exports through other transmission methods can reduce the GEPR by 63.4% because of an increase in dependency risk and reduction of GI due to an increase in the export diversity. Therefore, a policy implication is to diversify gas export through different transmission methods and select the countries with higher dependency and lower political risk.

Footnotes

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Ali Nowrouzi obtained his M.S in energy systems engineering from Sharif University of Technology in 2003. He is currently completing his doctoral studies in energy systems engineering at the Faculty of Natural and Environmental Sciences at Islamic Azad University, Science and Research Branch (SRBIAU). His research areas focus on risk assessment in the field of energy exports, energy policy studies and economic analysis of energy exports.

Mostafa Panahi attained his Ph.D. in Natural Resources Engineering/Environmental Economics from University of Tehran in 2005 and MSc in Economic Policy from Fribourg University of Switzerland. His main field of expertise relates to environmental and energy economics and policy. From 2008, he works for Islamic Azad University, Science and Research Branch as assistant professor.

Hamidreza Ghaffarzadeh received his B.A in Economics from University of Tehran in 1971, Diploma in Economics from University of Lancaster in 1974, M.A in Economics from University of Essex in 1975 and Ph.D in Urban Development Studies from University of Aberdeen in 1980. He joined the Ministry of Power in Iran in 1981 and the UNDP in 1986. He served in Iran, Azerbaijan, Kosovo and Kazakhstan in various UN programmes focusing on environment and development. In 2009, he returned to Iran to join the Islamic Azad University, the Science and Research Branch as an assistant professor in Economics and Environment.

Abtin Ataei received his Ph.D. in energy engineering from Science and Research Branch of Azad University (SRBIAU) in 2008. His main areas of research include Sustainable and Renewable Energy Systems, Photovoltaic Design and Optimization, and Pinch and Exergy Technology.

References

BP. BP statistical review of world energy. London: British Petroleum, 2016.

Dastan

Selcuk

Review of the security of supply in Turkish energy markets: lessons from the winter shortages. Renew Sustain Energy Rev 2016; 59: 958–971.

Doukas

Flamos

Psarras

Risks on the security of oil and gas supply. Energy Sources Part B 2011; 6: 417–425.

Gupta

Oil vulnerability index of oil-importing countries. Energy Policy 2008; 36: 1195–1211.

Vivoda

Diversification of oil import sources and energy security: a key strategy or an elusive objective?

Energy Policy 2009; 37: 4615–4623.

Yang

Sun

et al . Measuring external oil supply risk: a modified diversification index with country risk and potential oil exports. Energy 2014; 68: 930–938.

Zhang

H-Y

Fan

An evaluation framework for oil import security based on the supply chain with a case study focused on China. Energy Econ 2013; 38: 87–95.

Shokri Kalehsar

A flexible pipeline dream: Iran’s LNG goals. Energy Environ 2016; 27: 542–552.

Lee

Opportunities and risks in Turkmenistan’s quest for diversification of its gas export routes. Energy Policy 2014; 74: 330–339.

10.

Calvo-Silvosa

Antelo

Soares

Energy planning and modern portfolio theory: a review. Renew Sustain Energy Rev 2017; 77: 636–651.

11.

Gao

Sun

Shen

et al . Optimization of China's energy structure based on portfolio theory. Energy 2014; 77: 890–897.

12.

Winzer

Conceptualizing energy security. Energy Policy 2012; 46: 36–48.

13.

Intharak

Julay

Nakani shi

et al . A quest for energy security in the 21st century. Asia Pacific Energy Research Centre Report, Japan, 2007.

14.

Cherp

Jewell

The concept of energy security: beyond the four As. Energy Policy 2014; 75: 415–421.

15.

Adger

WN.

Vulnerability. Global Environ Change 2006; 16: 268–281.

16.

Gallopín

GC.

Linkages between vulnerability, resilience, and adaptive capacity. Global Environ Change 2006; 16: 293–303.

17.

Kruyt

van Vuuren

De Vries

et al . Indicators for energy security. Energy Policy 2009; 37: 2166–2181.

18.

Böhringer

Bortolamedi

Sense and no (n)-sense of energy security indicators. Ecol Econ 2015; 119: 359–371.

19.

Cabalu

Indicators of security of natural gas supply in Asia. Energy Policy 2010; 38: 218–225.

20.

Chung

E-S

Won

Kim

et al . Water resource vulnerability characteristics by district’s population size in a changing climate using subjective and objective weights. Sustainability 2014; 6: 6141–6157.

21.

Liang

SC.

Application of DRASTIC Model based on entropy weight to the assessment on vulnerability of groundwater in Weining Plain. Adv Mater Res 2012; 518: 4007–4014.

22.

Shao

Sun

Tao

et al . Environmental vulnerability assessment in middle-upper reaches of Dadu river watershed using projection model and GIS. Carpath J Earth Environ Sci 2015; 10: 133–146.

23.

Zhang

Wang

et al . Assessment model of ecoenvironmental vulnerability based on improved entropy weight method. Sci World J 2014; 1-7.

24.

Al-Abadi

Pourghasemi

Shahid

et al . Spatial mapping of groundwater potential using entropy weighted linear aggregate novel approach and GIS. Arab J Sci Eng 2017; 42: 1185–1199.

25.

Amiri

Rezaei

Sohrabi

Groundwater quality assessment using entropy weighted water quality index (EWQI) in Lenjanat, Iran. Environ Earth Sci 2014; 72: 3479–3490.

26.

Pei-Yue

Hui

Jian-Hua

Application of set pair analysis method based on entropy weight in groundwater quality assessment – a case study in Dongsheng City, Northwest China. J Chem 2011; 8: 851–858.

27.

World Bank. World Bank open data. The World Bank Group, http://data worldbank org (2015, accessed 18 September 2017).

28.

NIGC. Strategic and planning documents of Iran’s gas export, http://nigec.ir/Portal/home/ (2015, accessed 2 October 2017).

29.

PRS. International political risk rating (PPR). The PRS Group, http://www.prsgroup.com (2015, accessed 21 August 2017).