Investment risk assessment of coal-fired power plants in countries along the Belt and Road initiative based on ANP-Entropy-TODIM method

Energy 176 (2019) 623e640

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Investment risk assessment of coal-fired power plants in countries along the Belt and Road initiative based on ANP-Entropy-TODIM method Jiahai Yuan a, b, *, Xinying Li a, Chuanbo Xu a, b, Changhong Zhao a, Yuanxin Liu a a b

School of Economics and Management, North China Electric Power University, Beijing, 102206, China Beijing Key Laboratory of New Energy and Low-Carbon Development (North China Electric Power University), Changping Beijing, 102206, China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 26 August 2018 Received in revised form 23 January 2019 Accepted 7 April 2019 Available online 8 April 2019

Coal is an irreplaceable part of the world energy structure and the condition will not be changed in the near future. Under the Belt and Road initiative, more and more Chinese energy enterprises are planning to invest in coal-fired power plant (CFPP) abroad. However, there are multiple potential risks in investing in CFPP overseas. So, assessing the risk level can help the investors avoid too risky countries and at the same time facilitate the government to take measures to reduce the potential risks to attract more investors. To reasonably assess overseas investment risk of CFPP in countries along the Belt and Road initiative, this paper establishes an evaluation criteria system from eight dimensions which consists of a total of 39 criteria. Then, these criteria weights are determined through a combined analytic network process-Entropy method. Furthermore, considering the psychological characteristics of decision-makers, a TODIM (an acronym in Portuguese for Interactive Multi-criteria Decision Making) approach is used to rank the overall risk level of CFPP investment for 23 nations. The findings indicate that the criterion of economic foundation owns the largest weight. Moreover, among these evaluated nations, Singapore have the lowest risk for China’s CFPP investment, followed by New Zealand and Thailand. © 2019 Elsevier Ltd. All rights reserved.

Keywords: Belt and Road initiative Coal-fired power plants Risk assessment Analytic network process Entropy An acronym in Portuguese for interactive multi-criteria decision making

1. Introduction The Belt and Road initiative (BRI), which is short for the Silk Road Economic Belt and the 21st-century Maritime Silk Road, was proposed by Chinese President Xi Jinping in late 2013. International energy cooperation is an important part of this initiative. The development of energy cooperation under the BRI is conducive to the resources exploitation and energy construction of related countries, and thus to add new kinetic energy to the recovery and prosperity of the world economy and the sustainable development of the world economy [1]. According to the forecast by BP world energy outlook 2017, with the growth of world population and the continuous development of emerging economies, world energy demand will increase by 30% by 2035, with coal accounting for 24% of primary energy consumption. Although the share of coal in the world has dropped, it remains the

* Corresponding author. School of Economics and Management, North China Electric Power University, Beijing, 102206, China. E-mail address: [email protected] (J. Yuan). https://doi.org/10.1016/j.energy.2019.04.038 0360-5442/© 2019 Elsevier Ltd. All rights reserved.

cornerstone of fossil energy in many developing countries [2]. As shown in Fig. 1, the general trend of world coal consumption is upward, which means the world is still dependent on coal. In the past three decades, China has achieved rapid economic growth and emerged as the largest electricity production and consumption country in the world [3]. And coal accounts for around 70% of primary energy supply in China, much higher than global average [4]. The phenomenon of coal power overcapacity in China is increasingly severe. However, in the countries along the BRI, the electricity industry is relatively backward. The data of World Bank showed that by 2014, 21 countries still had residents without access to electricity, and with a total population of about 410 million. In view of China’s overcapacity in coal production and other developing countries’ demand for electricity, coal-fired power investments in overseas countries will achieve a win-win situation. More recently, coal-fired power investments along BRI has been included in the Chinese 13 t h Five-Year plan for the development of coal industry. However, CFPP features long construction period, large investment, and a large number of uncertainties which are constantly changing [5]. Thus, the investment of overseas CFPP will

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Fig. 1. World coal consumption from 2004 to 2014.

be accompanied by many risk factors such as economic risk, political risk, and environmental risk. A successful risk management helps to ensuring the safety of capitals so as to obtain maximum benefits. Conversely, inappropriate risk management may lead to the failing of investments. And in all links of risk management, risk assessment is most crucial. Under this circumstance, it is necessary to conduct an CFPP investment risk assessment in BRI’s nations. Nowadays, some valuable researches on energy aspect under the BRI were carried out, which focus on energy consumption [6], energy production competitiveness [7], energy business [8], energy development strategy [9], energy cooperation [10] and so on. However, it is worth to mention that the topic of investment risk assessment in the field of energy for the BRI’s countries is rarely studied. Actually, energy investment risk assessment is one topic which receives much attention. For example, Bartela, Skorek-Osikowska [11] analyzed the investment risk related to the integration of a supercritical coalfired combined heat and power plant with an absorption installation for CO2 separation. Kumar [12] identified, assessed and managed the risk factors of coal-fired power plant. And Gorbacheva and Sovacool [13] reviewed the risks and returns of investing in Russian coal-fired power sector to answer the question about possible investment risks and returns of pursuing the expansion of coal. Moreover, some scholars also pay attention to risk assessment on renewable energy investment [14,15]. Tietjen, Pahle [16] compared investment risks of fossil fuel and renewable energy dominated markets. Fortunately, more recently, Duan, Ji [17] assessed energy investment risk for nations along China’s BRI. A systematic criteria system from six aspects was proposed in this study; and then, a fuzzy integrated evaluation model combined with the Entropy weight was established to rate the energy investment risk for 50 nations along the BRI. The findings indicated that resource potential and Chinese factors are the main determinant of energy investment risk, while environmental constraints and political risk cannot be ignored. NADS [18] established an evaluation criteria system which consists of six dimensions: economic foundation, social risk, political risk, Chinese factor, energy factor and environmental risk; then a linear weighted method was employed to assess the political risk of energy investment under the BRI. Yuan, Zeng [19] proposed a nine-dimensional indicator system for countries along China’s BRI. Moreover, a fuzzy integrated evaluation model ground on the entropy weight was established to evaluate the electric power investment risk of 21 countries along China’s BRI. Different from the above significant researches, the objective of this paper is CFPP rather than generally energy power plants, which

is more targeted. Studying of the evaluation criteria system and methods is the core of risk evaluation. As far as we are concerned, there is no research on the reasonable evaluation criteria and methods for CFPP investment risk assessment in the content of the BRI. Thus, this paper tries to fill this gap in the literature. The contributions of this paper are as follows: 1) a comprehensive evaluation criteria system for coal-fired power plants investment risk is constructed which consists of eight dimensions and 39 criteria. The current evaluation criteria systems for coal-fired power plants investment risk still remain on the level of specific sector or project. Unlike these, this paper takes into account the worldwide scope risk criteria. 2) A hybrid analytic network process (ANP)Entropy-TODIM method is utilized to weight these criteria and rank the countries along the BRI. Although there are many successful applications of ANP-Entropy and TODIM to a wide range of multicriteria decision making (MCDM) problems in various fields, there is no application of the hybrid ANP-Entropy-TODIM method for investment risk assessment problem. The rest of the paper is organized as follows: after the Introduction, we make a literature review in the next Section. In Section 3, an evaluation criteria system for CFPP investment risk assessment is established. Section 4 introduces the basic theory of Entropy, ANP and TODIM. The decision process and results are elaborate in Section 5, while the last section give concludes and policy recommendations. 2. Literature review Investment risk assessment of CFPP across countries is a complex problem which generally involves multiple criteria and numerous alternatives, which can be deemed as a classical MCDM problem. For such problems, there are two key issues need to be solved: criteria weight determination and alternative ranking. For the former issue, there are many methods of determining weight, both quantitative and qualitative. Entropy [20], as a measure of uncertainty in information theory, is a classical quantitative weight determination method. The principle is that the greater the discreteness of the criterion, the greater the impact of the criterion on the comprehensive evaluation, that is, the greater the weight of the criterion. For example, if all alternatives’ values are the same with respect to a specific criterion, then the criterion should be eliminated because it has no division of the alternatives. On the contrary, the criterion that conveys the most amount of information should be assigned with the largest weight. Entropy is applicable in this paper for objective weights determination because criteria values of CFPP investment risk assessment are all quantitative. There are a lot of valuable applications where the Entropy method is successfully applied such as renewable energy sources ranking [21], nuclear power plant location selection [22], sustainable recycling partner selection [23], photovoltaic module selection [24], eco-industrial parks benefit evaluation [25] and so on. Although using the Entropy method to determine the weight is reasonable, it is unable to reflect the knowledge and experience of the decision-makers, and sometimes the weight obtained from the method may not match with the actual situation [26]. Analytic hierarchy process (AHP) [27] is a very important and widely used decision analysis tool in subjective weight determination. Although the AHP method has some advantages such as distinct structures, some limitations still exist. On the one hand, the elements of each criterion are required to be independent of each other; and on the other hand, unidirectional hierarchy relationship is required among different decision levels [28]. However, there are many criteria need to be considered in the process of CFPP investment risk assessment and the relationship between these criteria is more complex, so that it is difficult to meet the above two requirements. In 1996, ANP

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method [29] was proposed by Professor Thomas L. Saaty on the basis of AHP. It takes into account the interdependence between the criteria in same layer and adjacent layer and does not require strict hierarchical relationships like the AHP model. Recently, there have been numerous researches reporting on the application of ANP to variety of MCDM problems such as photovoltaic thermal system prioritization [30]; energy planning [31] renewable energy sources evaluation [32] and power plant evaluation [33] and so on. While the objective and subjective criteria weights can be calculated by Entropy and ANP method respectively, the combination of Entropy and ANP method is feasible and reasonable. Nowadays, the merits of this combination method have been recognized and it has some but not much successfully applications. For example, Mei, Ye [34] made an ANP-Entropy fuzzy comprehensive evaluation of interim product production schemes in oneof-a-kind production. Liu, Deng [35] employed the ANP and Entropy to solved the problems of supplier selection in supplier chain management. Huang [36] applied the ANP and Entropy method and their combination to evaluate the development of intelligent residential community’s development in China at different stages. Inspired by them, this paper determines the criteria synthetical weight using the combined ANP-Entropy method since it not only utilizes the original data and but reflects the decision-makers’ preferences. For the latter issue, many alternative ranking methods have been found in the literature, such as AHP, Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR), Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE), Elimination et Choice Translating Reality (ELECTRE), Multi Attribute Utility Theory (MAUT), Multi Attributive Border Approximation Area Comparison (MABAC) and some aggregation operators. The literature of applying ranking methods to the field of risk assessment have been summarized and listed in Table 1. Most of the above ranking methods assume that decisionmakers are totally rational in the face of investment risks. However, in real-life, the decision-makers are always limited rational because of the complex situation and limited knowledge [51]. Fortunately, based on a large number of experiments for considering decision-makers’ psychological behavior, Tversky and Kahneman [52] originally proposed the prospect theory to overcome this defect. Nevertheless, there is no data correlation between utilities and selections in the analysis process of the prospect theory [53]. Therefore, a more discrete MCDM method named as TODIM was developed on the basis of the prospect theory [54]. The basic principle of TODIM depends on a value function that calculates the global dominance degree of one alternative over the

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others on all criteria [55]. Since it was launched in 1992, the TODIM technique has been applied in various areas such as landfill location selection [56] and industrial robot selection [57], and green supplier selection [58]. Therefore, it is of great significance to introduce the TODIM approach into investment risk assessment of CFPP across countries. To illustrate the novelty of the proposed hybrid ANP-EntropyTODIM methodology, several literature regarding economics and business science aspect are explored. Qinhua and William [59] proposed a data envelopment analysis (DEA) approach to assess the economic risk of China’s Belt and Road Initiative’s sustainable investing. Although the DEA approach does not need to determine the criteria weight, it cannot consider the interdependence between criteria. Zhang, Andam [60] used a multi-fuzzy comprehensive evaluation approach to evaluate the economic, environmental and social risk of overseas investment under the China-Pakistan Economic Corridor. However, the multi-fuzzy comprehensive evaluation approach assesses only one object at a time, which increases computational complexity of the problem. Fan and Zhu [61] applied real options theory to overseas oil investment by adding an investmenteenvironment factor to oilresource valuation. This theory can evaluate and compare the critical value of oil-resource investment in different countries under oil-price, exchange-rate, and investmenteenvironment uncertainties. But the theory ignores the psychological behavior of decision-makers. Compared with the above literature, the advantages of the proposed methodology are two-folds: 1) it can take full advantage of the original data and at the same time consider the interdependence between criteria; 2) it takes the psychological behavior of decision-makers into account.

3. Evaluation criteria system for CFPP investment risk assessment Evaluation criteria system can decompose the overall CFPP investment risk into operational level. Thus, it is crucial to establish an appropriate evaluation criteria system. The report of Political Risk Assessment of Energy Resources Investment Under “the Belt and Road” Strategy [18], released by the National Academy of Development and Strategy (NADS), established an evaluation criteria system in 2018 which consists of six dimensions: economic foundation, social risk, political risk, Chinese factor, energy factor and environmental risk. To analyze the investment risk of CFPP along BRI’s nations, this study renames the criterion of energy factor as coal power revenue to make it more specific. Besides, two new dimensions are added on the basis of [18]’s research: external finance and electricity market. The external finance reflects the

Table 1 Literature on risk assessment topic. Literature

Ranking method

Research topic

Malekmohammadi and Blouchi [37] Chen, Wang [38] Gul and Guneri [39] Rostamzadeh, Ghorabaee [40] Shen, Ma [41] Wang, Zhang [42] Safari, Faraji [43] Amirshenava and Osanloo [44]
ski, Cinelli [45] Jasin Wang, Peng [46] Tian, Yang [47] Wu, Xu [48] Wu, Song [49] Locatelli, Invernizzi [50]

AHP TOPSIS Fuzzy TOPSIS Fuzzy TOPSIS Fuzzy TOPSIS VIKOR Fuzzy VIKOR PROMETHEE and TOPSIS ELECTRE MABAC Weighted ordered weighted operator Cloud Choquet integral operator AHP and grey fuzzy method Real option analysis

Wetland ecosystems Road safety Aluminum industry sustainable supply chain Credit Construction project Enterprise architecture Mine closure Non-fossil mineral resources supply Energy performance contracting project Oil and gas industry safety PPP waste-to-energy incineration projects Charging infrastructure projects Energy storage systems

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degree of openness and investment operation of a country while the electricity market reflects the electrification level and power demand of a country. Both of them are indispensable factors for CFPP risk assessment. Therefore, eight dimensions, namely economic foundation, political risk, Chinese factors, social risk, environmental risk, electricity market, external finance and coal power revenue are identified. Criteria that are associated with the above eight dimensions are determined by the following steps: 1) the academic literature related to the energy investment risk assessment under the BRI are reviewed, and 52 initial criteria are found; 2) on this basis, four other criteria are put forward by authors’ understanding, and thus the initial criteria list is formed (see Appendix A); 3) an online questionnaire survey is sent to the experts in the fields of international energy project management. In this survey, experts are asked to judge the importance of the above 56 criteria using a 5point scale (where 5 ¼ very important, 4 ¼ important, 3 ¼ medium, 2 ¼ less important, 1 ¼ unimportant). A total of 100 questionnaires are sent out, and feedback from 23 experts are received, yielding a response rate of 26.5%, which meets the norm of 20e30% of most questionnaire surveys in the energy industry; 4) those criteria with mean score over 4 are selected as the evaluation criteria for this study. From the above steps, an evaluation criteria system is

established. Furthermore, the specific dimensions and criteria as well as data source are presented in Table 2. The brief explanations of these dimensions and criteria are given as follows: 3.1. Economic foundation [18]. The economic foundation refers to the long-term foundation of a country’s investment, which reflects the stability of a country’s investment environment. A better economic foundation can provide benefits and security for Chinese enterprises to invest overseas. This dimension includes five criteria: economic size [17e19], development level [18,19], economic growth [17e19], inflation index [18,19] and debt level [18,19]. The economic size, development level and economic growth are measured by the gross domestic product (GDP), per capita GDP and GDP growth rate respectively. The inflation index measures the economic performance of a country. The debt level refers to the ratio of public debt to GDP, which measures the debt level of a country’s public and private sectors and reflects the solvency of a country’s economic. 3.2. External finance The external finance reflects the degree of openness and investment operation of a country. Higher foreign investment and

Table 2 Evaluation criteria system for investment risk assessment of CFPP. Dimension

Criteria

Data source

Type

Economic foundation (C1)

Economic size (C11) Development level (C12) Economic growth (C13) Inflation index (C14) Debt level (C15) Financial freedom (C21) Business freedom (C22) Exchange fluctuations (C23) Trade openness (C24) Investment level (C25) Population growth (C31) Urbanization rate (C32) Unemployment rate (C33) Crime index (C34) Educational level (C35) Corruption Control (C41) Government effectiveness (C42) Regulatory quality (C43) Political stability (C44) Law degree (C45) War situation (C46) Emission level (C51) Emission growth (C52) Water stress (C53) PM 2.5 concentration (C54) NDC target (C55) BIT sign (C61) Import and export dependence (C62) Investment dependence (C63) Partnership (C64) Bilateral agreement (C65) Electrified rate (C71) Electrification rate (C72) Power demand growth (C73) Electric power import (C74) Coal surplus (C81) Proportion of electric coal (C82) Coal power planning (C83) Coal power proportion (C84)

WB [62] WB [62] WB [62] WB [62] IEF [63] IEF [63] IEF [63] WB [62] WB [62] WB [62] WB [62] WB [62] WB [62] Numbeo [64] WB [62] WGI [65] WGI [65] WGI [65] WGI [65] WGI [65] Wikipedia [66] IEA [67] IEA [67] WRI [68] WHO [69] UNFCCC [70] MOFCOM [71] NBS [72] & WB [62] Zhang and Wang [73] BDRBRI [74] BDRBRI [74] WB [62] WB [62] WB [62] IEA [67] EIA [75] IEA [67] Christine S et al. [76] WB [62]

Benefit Benefit Benefit Cost Cost Benefit Benefit Cost Benefit Benefit Benefit Benefit Cost Cost Benefit Benefit Benefit Benefit Benefit Benefit Cost Cost Cost Benefit Cost Cost Benefit Benefit Benefit Benefit Benefit Cost Cost Benefit Benefit Benefit Benefit Benefit Cost

External finance (C2)

Social risk (C3)

Political risk (C4)

Environmental risk (C5)

Chinese factors (C6)

Electricity market (C7)

Coal power revenue (C8)

Note: WB (World Bank), IEF (Index of Economic Freedom), WGI (Worldwide Governance Indicators), IEA (International Energy Agency), WRI (World Resources Institute), WHO (World Health Organization), UNFCCC (United Nations Framework Convention on Climate Change), MOFCOM (Ministry of Commerce of China), NBS (National Bureau of Statistics), EIA (Energy Information Administration), BDRBRI (Big data report of BRI).

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financial level can provide a basis for overseas investors’ investment activities. Five criteria are included in this dimension: financial freedom [18,19], business freedom [18,19], exchange fluctuations [18,19], trade openness [18,19], and investment level [18]. Financial freedom and business freedom reflect the level of operation and participation of state investment, respectively. Trade openness represents the proportion of imports of goods and services to GDP, while the investment level represents the proportion of net foreign investment to GDP. 3.3. Social risk The social risk reflects the stability level of social operation. The more stable the social level of the target country is, the more favorable the investment is. Population growth, urbanization rate, unemployment rate [18,19], crime index [18,19] and educational level [18,19] are included in this dimension. The population growth is the ratio of population growth to the total population in a certain period, which reflects the development of the economy, the living standard of the people and the improvement of the medical level. The urbanization rate reflects the potential of economic and social development. The unemployment rate reflects the degree of perfection of the social labor market. And the crime index reflects the stability of society. The higher the crime rate, the worse the social stability, the greater the risk of investment. The education level refers to the proportion of the secondary school population to the total population, which can reflect the degree of social civilization.

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of water resources, the higher the risk of grounding of coal-fired power units. The PM2.5 concentration takes into account the local air pollution situation in the investing countries. The NDC target reflects the contribution mechanism of national emission reduction and measures the target level, scope and indicators of national independent contribution plan to tackle climate change. 3.6. Chinese factors The Chinese factor, that is, the relationship with China, is an important factor in measuring the cooperative relationship between China and foreign investment and trading partners. Other countries’ superior policy toward China can reduce the risk of foreign investment. Five criteria are involved in this dimension: Bilateral Investment Treaty (BIT) sign [18,19], import and export dependence [18,19], investment dependence [18,19], partnership [19], and bilateral agreement [19]. The BIT sign refers to whether a country has entered into a bilateral investment agreement with China and whether the agreement is effective. The import and export dependence reflect the trade dependence between the two countries and China’s trade status in that country. The investment dependency refers to the proportion of bilateral investment between China and one country in the country’s investment. The partnership refers to the partnership established between countries to seek common interests. The bilateral agreement refers to the agreement between two sovereign states to coordinate mutual tax and trade agreements. 3.7. Electricity market

3.4. Political risk The political risk refers to the stability of a state government and the quality and efficiency level of the government’s handling of the affairs. It mainly examines the stability of the domestic and external countries, the quality of service in the domestic society and the regulatory ability of the law. This paper considers the following political risk criteria: corruption control [17e19], government effectiveness [18], regulatory quality [18,19], political stability [18,19], law degree [17e19] and war situation. Corruption control represents the government’s degree of corruption control. Government effectiveness includes multidimensional architectures such as economy, efficiency, effectiveness and fairness. Regulatory quality refers to the supervisory and executive ability of government departments to enterprises. Political stability includes government stability, political violence and terrorism. Law degree consist of the establishment, perfection, implementation and supervision of laws and regulations. The war situation is the number of wars and conflicts in a country. It is well known that the contending countries have great influence on the foreign investment of enterprises. 3.5. Environmental risk The environmental risk measures the importance of a country’s attention to environmental protection in policy and action. The foreign investment of national energy is directly affected by the country’s environmental protection policy. The environmental risk of a country is the possibility of its future environmental protection standards. There are five criteria for the environmental risk: emission level [17e19], emission growth [18,19], water stress [19], PM 2.5 concentration [17,19], and NDC target [18,19]. The emission level is calculated through dividing per capita carbon emissions by per capita levels. The emission growth represents the level of per capita carbon emissions growth. The water pressure refers to the water pressure in the 2030 BAU situation. The greater the pressure

Electricity is an important part of the development of modern society, and provides a continuous source of power for economic development. It provides 20% of the world’s total terminals, and the proportion is rising. The potential of the electricity market has affected overseas energy investment. The electricity market has the following four criteria: electrified rate [19], electrification rate [19], power demand growth and electric power import [19]. The electrified rate reflects the proportion of a country’s population with the service of electricity. The electrification rate refers to the proportion of electricity consumption in the primary energy consumption. The power demand growth rate refers to the annual average growth rate of power demand, the higher the power demand growth rate, the greater the power gap of the investing country, thus the greater the potential of energy investment. The electric power import refers to the proportion of net electricity import to total output. The greater the net electricity import, the greater the demand for electricity abroad, and the greater the domestic electricity market. 3.8. Coal power revenue The coal power revenue is one of the inherent constraints for Chinese overseas enterprises to invest in electricity. It will directly affect the overseas investment decision-making of coal-fired power enterprises. The internal rate of return (IRR) benchmark for overseas investment in coal and electricity is 10e13%. The greater the profit of coal and electricity in investment countries, the greater the attraction to coal power enterprises. This dimension includes four criteria: coal surplus [19], proportion of electric coal [19], coal power planning [19], and coal power proportion [19]. The coal surplus refers to the ratio of coal storage and production. Although the global energy structure is changing from the traditional fossil energy to renewable energy, the dominant position of fossil energy will not change for a long time. The higher coal surplus, the richer the coal resources. And it is beneficial to save the cost of coal

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electricity enterprises to generate electricity. The proportion of electric coal is the proportion of coal used for power generation to coal mining. The coal power planning refers to the ratio between the planning installed capacity of coal power and the present installed power generation capacity. The coal power proportion refers to the proportion of coal-fired power generation to total electricity generation. 4. Research methodology

Table 3 The meaning of 1e9 scale. Value

Meaning

1 3 5 7 9 2,4,6,8 Reciprocal

The two factors are equally important One factor is a little more important than the other One factor is more important than the other One factor is strongly more important than the other One factor is extremely more important than the other The intermediate values of the two adjacent judgments aij is obtained by i compared with j, aji is obtained by j compared with i.

4.1. Entropy Entropy method is an objective weighting method since it only depends on the data itself [77]. The steps of its calculation are as follows: 2 3 x11 x12 ::: x1m 6 x21 x22 ::: x2m 7 7 Let D ¼ 6 4 x31 x32 ::: x3m 5 be a decision matrix, where xij x41 x42 ::: x4m represents the value of alternative i regarding the criterion j. In this study, i refers to the 23 nations along the China’s BRI, and j refers to the selected 39 criteria. Step 1. Calculate the contribution degree pij of each alternative i regarding the criterion j.

, pij ¼ xij

n X

xij

(1)

i¼1

Step 2. Calculate the Entropy ej for the criterion j. ej is the total contribution degree of all alternative to the criterion j.

ej ¼ k

n X

pij ,In pij

(2)

i¼1

where the constant k is generally taken 1=In n. Step 3. Calculate the divergence coefficient gj for the criterion j.

gj ¼ 1  ej

(3)

where gj represents the contribution divergence of each alternative regarding the criterion j. The greater the value, the greater the role of the criterion. Step 4. Determine the objective weights of each criterion

, wj ¼ gj

m X

gj

(4)

i¼1

4.2. ANP ANP is an improved version of AHP to reduce the deficiency of AHP in non-independent hierarchical structure [78]. The interdependence of the selected 39 criteria can be depict by this method. Its architecture includes two layers of control layer and network layer. The method of the ANP can be described as follows. Step 5. Form a judgment matrix by using criteria comparison. This step is to compare the criteria in whole system to form the supermatrix. Invited experts are asked to answer the question “How much importance does a criterion have compared to another criterion?” A scale of 1e9 is adopted to represent the relative importance value from equal importance to extreme importance [27]. The meanings of each scale are presented in Table 3. Assume that the components of control layer in ANP are B1 ; B2 ;

/; Bm , the components of the network layer are C1 ; C2 ; /; Cn , wherein, Cj is composed by cij ðk ¼ 1; 2; /; nÞ. Making comparative analysis of the elements in Ci according to their influence on cij . And then the sort vector ½wi1 ; wi2 ; /; win T can be obtained according to the eigenvalue method. If the eigenvectors above pass the consistency test, it will be written into a matrix form, local weight vector matrices can be obtained.

2

ðj1Þ

wi1

6 ðj1Þ 6 Wij ¼ 6 wi2 4 « ðj1Þ

win

ðj2Þ

wi1

:::

wi2 « ðj2Þ wi1

::: « :::

ðj2Þ

ðjnÞ

wi1

3

ðjnÞ 7 wi2 7 7 « 5 ðjnÞ win

(5)

Step 6. Establish a supermatrix. Compare the internal and external relationships between the elements of other element sets, and then get the weightless super matrix Ws composed of the sorting vectors affected by each element in the network layer.

2

w11 6 w21 Ws ¼ 6 4 « wN1

w12 w22 « wN2

3 w1N w2N 7 7 « 5 wNN

::: ::: « :::

(6)

Each element of the matrix is a matrix, of which the column sum is 1. Nevertheless, the matrix W has not been normalized. To make computation more convenient, it is necessary to normalize the supermatrix, which means that weighting the elements of Ws to obtain a weighted supermatrix. Step 7. Calculate a weighted super matrix. Compare the importance of Ci with Cj in order to obtain a sort vector, which is denoted as Hj ¼ ½h1j ; /; hNj . Based on this, we can further obtain weighted matrix.

2

h11 6 h21 H¼6 4 « hN1

h12 h22 « hN2

::: ::: « :::

3 h1j h2j 7 7 « 5 hN3

(7)

The weighted super matrix obtained by H multiplying W is denoted as W. After that, for the sake of representing the relevance between these elements, we need to deal with the stability of weighted supermatrix W. Stability treatment means calculate the limit relative rank vector of each supermatrix

W ∞ ¼ lim ð1=NÞ k/∞

N X

W

k

(8)

k¼1

The prerequisite for the value of corresponding row of original matrix becoming stable weights of evaluation indicators is that the limit is convergent and unique. And the result calculated by the above formula is the weights of each indicator. Nevertheless,

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because of the complex calculation process, the software of Super Decision is usually employed for this calculation.

decision-making matrix can be obtained, as presented in Table 4. Then, due to different dimensions of the original data, they are normalized using the following:

4.3. TODIM

  8 xij  min xij > >     j2F B > > < max xij  min xij * xij ¼   > > max xij  xij > >     j2F C : max xij  min xij

TODIM is a discrete multi-criteria method which processes the dominance degree of each alternative over others by establishing a multicriteria value function based on the prospect theory [79]. It considers the psychological behavior of decision-makers who want to invest in oversea coal-fired power plant. Step 8. Calculate the scale-weight wjr of each criterion Cj according to the reference criterion Cr , which is expressed as:

 wjr ¼ wj wr

(9)

 where wr ¼ maxfwj j ¼ 1; 2; …; ng. Step 9. Determine the dominance degree of alternative Ai over other alternative Ak on criterion Cj , which as follows:

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 8v , u  n  X > u > > t > wjr xij  xkj if xij > xkj wjr > > > > j¼1 > > < if xij ¼ xkj fj ðAi ; Ak Þ ¼ 0 > > v ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi > , > uX > n   > > 1u > t > wjr if xij < xkj  wjr xij  xkj > : q

(10)

j¼1

Noted here the parameter q implies the losses attenuation factor. 2.25. Step 10. Calculate the global dominance degree of alternative Ai over other alternative Ak in the following formula:

rðAi ; Ak Þ ¼

n X

fj ðAi ; Ak Þ

(11)

j¼1

Step 11. Determine the global prospect value of the alternative Ai according to the formula:

pðAi Þ ¼

rðAi ; Ak Þ  minfrðAi ; Ak Þg i

maxfrðAi ; Ak Þg  minfrðAi ; Ak Þg i

(12)

i

Step 12. Rank the alternatives. The bigger value of pðAi Þ is, the better alternative Ai is.

(13)

where F B and F C are the subsets of benefit attributes and cost attributes. The normalized data are presented in Table 5. In the second phase, a group of authoritative experts in the field of energy risk management are invited to determine the subjective weight of these criteria. According to the previous risk criteria system established in section 3, the interdependency and importance between the criteria are analyzed using Delphi technique, and model of network hierarchy analysis is set up, which is shown in Fig. 3. After inputting the information from expert group into the Super Decision software, the vector of subjective weight Wsub can be obtained as [0.033, 0.031, 0.068, 0.063, 0.029, 0.035, 0.021, 0.016, 0.013, 0.036, 0.059, 0.077, 0.058, 0.015, 0.036, 0.027, 0.030, 0.040, 0.019, 0.023, 0.023, 0.019, 0.004, 0.019, 0.005, 0.013, 0.080, 0.053, 0.002, 0.002, 0.005, 0.017, 0.001, 0.001, 0.003, 0.005, 0.005, 0.010, 0.005]. The vector of objective weights Wobj is calculated using Entropy method based on Eqs. (1)e(4), which are as follows: [0.046, 0.058, 0.016, 0.007,0.009, 0.015, 0.010, 0.005, 0.024, 0.057, 0.013, 0.016, 0.005, 0.012, 0.015, 0.037, 0.016, 0.018, 0.012, 0.022, 0.010, 0.014, 0.011, 0.021, 0.008, 0.011, 0.004, 0.029, 0.082, 0.021, 0.012, 0.119, 0.011, 0.035, 0.012, 0.075, 0.012, 0.087, 0.013]. The combined criteria weight W * can be determined using the following equation:

W * ¼ lWsub þ ð1  lÞWobj

(13)

where l is a subjective preference coefficient, here is a value of 0.5 without loss of generality. The combined criteria weights are calculated, as shown in Fig. 4. In the last phase, we calculate the relative weight vector according to Eq. (9), which is as follows:

w*jr ¼ ð0:586; 0:654; 0:617; 0:521; 0:276; 0:369; 0:226; 0:160; 0:272; 0:686; 0:538; 0:685 0:465; 0:200; 0:376; 0:473; 0:342; 0:427; 0:230; 0:332; 0:247; 0:246; 0:105; 0:297; 0:094; 0:176; 0:622; 0:610; 0:620; 0:168; 0:129; 1:000; 0:093; 0:267; 0:107; 0:587; 0:127; 0:718; 0:133Þ 0:127; 0:718; 0:133ÞT

5. Investment risk assessment of CFPP along the BRI 5.1. Decision-making process and results The decision-making framework proposed in this work combining Entropy, ANP and TODIM method together in a structure framework. The decision-making process which composes of three main phases is depicted in Fig. 2. In the first phase, the original data of the selected criteria are collected firstly, which are shown in Appendix B. So, the initial

Following by this, the dominance degree of alternative Ai over other alternative Ak regarding the eight criteria are calculated using Eq. (10) where the parameter q is assumed to be 2.25, which is determined by Tversky and Kahneman’s empirical research [52]. Then, the global dominance degree of alternative Ai over other alternative Ak are determined using Eq. (11) and listed in Appendix C. Utilizing Eq. (12), the global value for alternative Ai are calculated. The risk levels are divided into five classes according to the global value:

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J. Yuan et al. / Energy 176 (2019) 623e640

Start

Collect date and construction a decision-making matrix

Report and website

Phase 1 Normalize the decision-making matrix

Calculate objective weight

Calculate subjective weight

Phase 2

ANP+Entropy

Determine the combined weights of criteria

Calculate the relative weight of each criterion to the reference criterion Calculate the dominance of each alternative over other alternatives under each criterion Phase 3

TODIM

Calculate the global dominance degree of each alternative over other alternatives Calculate the global prospect value of the alternative

End

Fig. 2. Decision-making process for CFPP investment risk assessment.

method can be observed in Fig. 6. It can be seen that there is a large

ð1Þ f0:0  p < 0:2g3highest risk; ð2Þ f0:2  p < 0:4g3higher risk gap between subjective and objective weight of most criteria, ð3Þ f0:4  p < 0:6g3medium risk; ð4Þ f0:6  p < 0:8g3lower risk which means that the objective weights determined by original ð5Þ f0:8  p < 1:0g3lowest risk data themselves are often deviate from decision-makers’ mental The risk levels of each countries are depicted in Fig. 5 vividly. 5.2. Discussion The weight results show that the economic foundation (C1) has become the main determinant of CFPP investment risk. This is because this criterion reflects the stability of a country’s investment environment and it has great influence on other risk criteria. So, this criterion should be taken seriously. While the social development (C3), political risk (C4), Chinese factors (C6) should be also emphasized. On the contrary, environmental risk (C5), electricity market (C7) are assigned with small weights. Moreover, the comparative criteria weights determined by ANP and Entropy

expectations. This phenomenon is particularly obvious in the criteria C6, C7, and C8. Therefore, it is reasonable to adopt a combined weight to avoid being too objective and subjective. From the assessment results, it can be seen that there are 3 lowest risk nations, 6 lower risk nations, 5 medium risk nations, 2 higher risk nations, and 5 highest risk nations. In generally, most evaluated countries have high investment risks. It can be also found that Singapore owns the lowest risk for CFPP investment, followed by New Zealand and Thailand. Furthermore, in a similar manner, the performances of each alternative on the eight criteria can be also calculated, shown in Table 6. It can be found that the main reason for the top ranking in Singapore is that it performs excellent on the criterion of external finance (C2) and performs well on some

Table 4 Initial decision-making matrix.

C11 C12 C13 C14 C15 C21 C22 C23 C24 C25 C31 C32 C33 C34 C35 C41 C42 C43 C44 C45 C46 C51 C52 C53 C54 C55 C61 C62 C63 C64 C65 C71 C72 C73 C74 C81 C82 C83 C84

A1

A2

A3

A4

A5

A6

A7

A8

A9

A10

A11

A12

A13

A14

A15

A16

A17

A18

A19

A20

A21

22.775 1178.798 3.385 0.546 64.400 40 61.2 0.073 0.164 0.833 1.999 39.224 4.044 56.63 44.387 0.761 0.661 0.624 2.544 0.788 0.1 0.8 0 2.31 59.8 20 1 0.405 0.04 5 3 99.147 8.213 2.718 0.408 0.736 1.846 12385 0.156

57.206 15065.971 2.909 0.416 51.300 70 67.8 0.119 0.478 3.555 0.043 60.531 4.955 31.41 106.61 0.58 0.8 0.997 0.874 0.797 1 7.3 0 11.9 24.1 40 1 0.083 0 3.5 4 100 13.564 1.98 0.202 0.179 67.763 9090 82.993

130.47 25458.887 2.365 3.610 35.900 70 90.6 0.15 0.5 0.858 1.137 82.592 3.755 14.31 100.2 0.489 1.026 1.156 0.099 0.955 1 11.6 2.655 11.76 26.6 37 1 0.458 0.01 0.5 2 100 16.762 4.052 0 0.185 69.724 7359 42.41

165.44 11279.625 0.408 1.659 17.700 30 74.8 0.166 0.176 2.536 1.148 74.101 5.173 47.31 104.4 0.863 0.183 0.522 1.049 0.72 0.6 10.2 0.971 13.18 14.8 27.5 1 0.238 0.01 4.5 4 100 11.283 1.778 1.092 0.431 46.518 720 14.901

28.448 2753.350 5.264 1.807 37.100 60 62.6 0.078 0.421 2.617 1.96 44.289 2.784 37.61 88.335 0.43 0.107 0.037 0.838 0.345 0.1 1 0 8.8 22.2 70 1 0.527 0.01 0.5 2 90.982 12.401 5.19 0 0.045 79.038 12141 42.782

18.831 10582.497 0.322 3.456 23.300 50 74.5 0.359 0.312 12.365 0.451 53.229 4.86 45.88 109.45 0.756 0.053 0.025 0.098 0.368 1 12.8 3.759 1.38 15.4 15 1 0.322 0.03 3.5 3 100 10.667 6.238 0.004 0.239 54.707 636 71.948

1.701 1079.114 5.292 1.236 33.600 50 29.6 0.011 0.784 11.426 0.17 20.945 0.216 48.17 60.52 1.044 0.69 0.48 0.098 0.92 1 0.5 25 18.32 23 26.73 1 0.33 0.15 4 3 49.771 5.499 18.312 34.705 0 97.785 3325 28.211

23.134 21894.113 2.396 1.951 40.900 80 67.2 0.082 0.751 3.327 1.564 72.98 3.063 32.06 105.05 0.391 1.051 1.085 0.96 1.123 1 9.4 0 12.81 20.1 40 1 0.076 0 2.5 2 100 13.503 0.554 14.918 0.079 72.737 660 51.456

34.405 11031.822 2.669 3.556 57.400 50 90.8 1.211 0.621 4.558 1.416 75.37 3.415 68.55 84.973 0.284 0.964 0.772 0.191 0.574 1 7.3 1.351 14.76 14.8 35 1 0.454 0.01 3.5 3 100 13.083 4.938 0.007 0.07 89.21 2600 37.857

16.777 1029.578 5.963 7.011 34.000 30 53.4 0.096 0.21 1.051 1.565 35.035 4.367 68.56 63.415 0.875 0.728 0.929 1.155 0.7 0.9 0.4 0 18.88 83.5 5 0 0.321 0.01 2 2 75.92 11.798 8.454 0 0.315 11.498 21998 1.97

7.447 1408.141 4.908 1.778 32.000 20 50.1 0.088 0.001 5.185 0.192 34.65 0.794 45 43 0.894 1.238 1.262 1.17 1.221 0.1 0.5 25 19.32 51 27 1 0.84 0.13 4 1 57.009 4.924 17.992 0 0.009 15.73 2030 2.02

41.950 7488.990 1.017 6.728 50.100 50 62 0.12 0.303 0.762 1.499 65.295 27.718 78.43 102.76 0.042 0.265 0.304 0.176 0.056 1 7.8 2.5 7.84 27.4 6.92 1 0.392 0.02 3.5 2 84.2 13.155 0.514 0.621 0.138 64.002 11892 93.005

40.642 5901.884 2.929 13.638 43.100 60 69.9 0.078 0.575 0.753 1.08 51.54 1.083 42.56 120.63 0.4 0.357 0.299 0.956 0.106 0.9 3.6 0 12.39 24.6 20 1 0.305 0.01 3.5 3 100 10.891 4.414 6.833 0.168 51.874 5256 21.643

112.25 14116.980 1.573 8.098 32.600 60 64.3 0.122 0.231 1.545 0.915 73.887 11.263 39.43 103.05 0.115 0.233 0.33 1.276 0.06 0.6 4.1 2.5 3.59 33.5 21 1 0.093 0 4 2 100 15.282 5.946 1.505 0.256 53.905 42890 30.268

12.400 2905.857 2.721 17.142 80.200 30 62.1 0.372 0.449 3.689 1.301 69.915 9.455 51.27 96.786 0.98 0.514 0.58 1.933 0.801 0.6 4.2 19.231 7.6 16.4 40 1 0.136 0 2.5 2 100 12.37 0.934 0.825 1.13 59.435 1320 38.736

29.495 52600.641 0.682 1.435 98.200 80 95.1 0.094 1.686 20.741 0.299 100 2.024 15.81 108.13 2.13 2.252 2.263 1.237 1.882 1 8 3.614 0 17 36 1 0.153 0.02 4 3 100 14.586 3.666 0 0 62.899 0 1.098

17.140 36521.959 0.913 2.417 30.400 80 91.8 0.083 0.322 1.046 1.573 86.322 4.881 36.37 114.75 2.291 1.89 1.948 1.491 2.021 1 6.7 2.899 17.52 5.2 70 1 0.247 0 3.5 3 100 16.72 0.136 0 2.235 32.968 0 4.505

28.781 33677.462 2.074 0.843 64.600 70 69.9 0.298 0.344 3.746 0.331 92.205 4.271 30.63 102.49 0.894 1.375 1.27 1.118 1.168 0.9 7.4 1.333 0.02 19.1 26 1 0.124 0 0.5 2 100 20.183 1.946 8.092 0 96.797 0 49.555

246.5 1861.491 5.884 3.612 67.200 40 52.8 0.65 0.209 1.964 1.297 33.136 3.523 46.59 73.979 0.38 0.101 0.391 0.921 0.057 0.6 1.6 0 5.36 62.4 34 1 0.151 0.01 0.5 2 84.52 10.675 7.432 0.007 0.162 67.033 131359 75.075

103.77 3974.058 3.828 2.450 27.300 60 49.1 0.126 0.193 0.479 1.06 54.466 4.283 46.97 87.299 0.454 0.225 0.211 0.596 0.413 0.9 1.7 0 8.18 14.4 29 1 0.329 0.01 3.5 2 97.62 7.759 7.89 0.006 0.065 76.602 37905 52.649

16.410 1735.291 5.091 1.111 59.300 40 61.2 0.276 1.16 6.138 2.099 34.236 2.053 55.69 55.69 0.447 0.077 0.496 0.011 0.269 1 1.8 12.5 15.93 25.7 20 1 0.536 0.02 4 2 100 16.333 10.624 1.032 0.09 52.897 46525 24.528

632

Table 5 Normalized decision-making matrix. A1

A2

A3

A4

A5

A6

A7

A8

A9

A10

A11

A12

A13

A14

A15

A16

A17

A18

A19

A20

A21

0.086 0.003 0.631 0.893 0.420 0.333 0.482 0.949 0.097 0.017 0.959 0.231 0.861 0.340 0.018 0.085 0.165 0.181 0.000 0.133 0.100 0.968 0.565 0.120 0.303 0.769 1.000 0.431 0.267 1.000 0.667 0.017 0.784 0.142 0.309 0.329 0.000 0.094 1.000

0.227 0.272 0.562 0.900 0.583 0.833 0.583 0.910 0.283 0.152 0.119 0.501 0.828 0.733 0.819 0.487 0.584 0.641 0.847 0.622 1.000 0.444 0.565 0.616 0.759 0.462 1.000 0.008 0.000 0.667 1.000 0.000 0.434 0.101 0.297 0.080 0.687 0.069 0.108

0.526 0.474 0.485 0.728 0.774 0.833 0.931 0.884 0.296 0.019 0.604 0.780 0.871 1.000 0.737 0.460 0.649 0.686 0.655 0.671 1.000 0.097 0.505 0.609 0.727 0.508 1.000 0.500 0.067 0.000 0.333 0.000 0.224 0.215 0.301 0.083 0.708 0.056 0.545

0.669 0.199 0.087 0.833 1.000 0.167 0.690 0.871 0.104 0.101 0.609 0.672 0.820 0.485 0.791 0.054 0.302 0.210 0.371 0.155 0.600 0.210 0.587 0.682 0.877 0.654 1.000 0.211 0.067 0.889 1.000 0.000 0.583 0.090 0.279 0.193 0.466 0.005 0.841

0.109 0.033 0.900 0.825 0.759 0.667 0.504 0.944 0.249 0.106 0.943 0.295 0.907 0.637 0.584 0.184 0.385 0.348 0.423 0.270 0.100 0.952 0.565 0.455 0.783 0.000 1.000 0.589 0.067 0.000 0.333 0.180 0.510 0.278 0.301 0.020 0.805 0.092 0.541

0.070 0.185 0.100 0.737 0.930 0.500 0.685 0.710 0.185 0.587 0.322 0.408 0.831 0.508 0.856 0.086 0.340 0.351 0.606 0.263 1.000 0.000 0.650 0.071 0.870 0.846 1.000 0.322 0.200 0.667 0.667 0.000 0.624 0.336 0.301 0.107 0.551 0.005 0.227

0.000 0.001 0.904 0.856 0.802 0.500 0.000 1.000 0.465 0.540 0.206 0.000 1.000 0.472 0.226 0.000 0.157 0.222 0.606 0.093 1.000 0.992 0.000 0.948 0.773 0.666 1.000 0.332 1.000 0.778 0.667 1.000 0.962 1.000 1.000 0.000 1.000 0.025 0.698

0.088 0.405 0.489 0.818 0.712 1.000 0.574 0.940 0.445 0.141 0.780 0.658 0.896 0.723 0.799 0.430 0.656 0.666 0.868 0.723 1.000 0.274 0.565 0.663 0.810 0.462 1.000 0.000 0.000 0.444 0.333 0.000 0.438 0.023 0.000 0.035 0.739 0.005 0.447

0.134 0.194 0.528 0.731 0.507 0.500 0.934 0.000 0.368 0.201 0.719 0.688 0.884 0.154 0.541 0.398 0.631 0.577 0.678 0.554 1.000 0.444 0.596 0.764 0.877 0.538 1.000 0.494 0.067 0.667 0.667 0.000 0.465 0.264 0.301 0.031 0.911 0.020 0.594

0.062 0.000 1.000 0.545 0.798 0.167 0.363 0.929 0.124 0.028 0.780 0.178 0.849 0.154 0.263 0.051 0.146 0.095 0.344 0.161 0.900 1.000 0.565 0.977 0.000 1.000 0.000 0.320 0.067 0.333 0.333 0.479 0.550 0.458 0.301 0.141 0.101 0.167 0.980

0.023 0.007 0.849 0.827 0.822 0.000 0.313 0.936 0.000 0.232 0.215 0.173 0.979 0.521 0.000 0.045 0.000 0.000 0.341 0.000 0.100 0.992 0.000 1.000 0.415 0.662 1.000 1.000 0.867 0.778 0.000 0.856 1.000 0.982 0.301 0.004 0.145 0.015 0.980

0.164 0.125 0.000 0.561 0.598 0.500 0.495 0.843 0.179 0.014 0.753 0.561 0.000 0.000 0.770 0.300 0.431 0.444 0.587 0.394 1.000 0.403 0.622 0.406 0.716 0.970 1.000 0.413 0.133 0.667 0.333 0.315 0.461 0.021 0.288 0.062 0.648 0.091 0.000

0.159 0.094 0.565 0.189 0.684 0.667 0.615 0.944 0.341 0.013 0.581 0.387 0.968 0.559 1.000 0.193 0.457 0.443 0.394 0.344 0.900 0.742 0.565 0.641 0.752 0.769 1.000 0.299 0.067 0.667 0.667 0.000 0.609 0.235 0.438 0.075 0.521 0.040 0.769

0.452 0.254 0.371 0.487 0.815 0.667 0.530 0.907 0.137 0.053 0.513 0.670 0.598 0.608 0.774 0.279 0.421 0.452 0.314 0.358 0.600 0.702 0.509 0.186 0.639 0.754 1.000 0.022 0.000 0.778 0.333 0.000 0.321 0.320 0.331 0.115 0.543 0.327 0.676

0.044 0.036 0.536 0.000 0.224 0.167 0.496 0.699 0.266 0.158 0.672 0.619 0.664 0.424 0.693 0.019 0.207 0.194 0.151 0.129 0.600 0.694 1.000 0.393 0.857 0.462 1.000 0.078 0.000 0.444 0.333 0.000 0.512 0.044 0.284 0.506 0.600 0.010 0.584

0.114 1.000 0.243 1.000 0.000 1.000 1.000 0.931 1.000 1.000 0.260 1.000 0.934 0.977 0.839 0.952 1.000 1.000 0.937 0.957 1.000 0.387 0.647 0.000 0.849 0.523 1.000 0.101 0.133 0.778 0.667 0.000 0.367 0.194 0.301 0.000 0.636 0.000 0.990

0.063 0.688 0.277 0.793 0.842 1.000 0.950 0.940 0.191 0.028 0.783 0.827 0.830 0.656 0.924 1.000 0.896 0.911 1.000 1.000 1.000 0.492 0.631 0.907 1.000 0.000 1.000 0.224 0.000 0.667 0.667 0.000 0.227 0.000 0.301 1.000 0.324 0.000 0.953

0.111 0.633 0.443 0.877 0.417 0.833 0.615 0.760 0.203 0.161 0.000 0.901 0.853 0.745 0.766 0.581 0.749 0.718 0.353 0.737 0.900 0.435 0.595 0.001 0.822 0.677 1.000 0.063 0.000 0.000 0.333 0.000 0.000 0.100 0.138 0.000 0.990 0.000 0.468

1.000 0.016 0.989 0.728 0.385 0.333 0.354 0.467 0.123 0.073 0.670 0.154 0.880 0.497 0.399 0.199 0.384 0.247 0.402 0.359 0.600 0.903 0.565 0.277 0.269 0.554 1.000 0.098 0.067 0.000 0.333 0.308 0.623 0.401 0.301 0.073 0.679 1.000 0.193

0.417 0.057 0.694 0.791 0.881 0.667 0.298 0.904 0.114 0.000 0.573 0.424 0.852 0.491 0.571 0.177 0.290 0.298 0.483 0.249 0.900 0.895 0.565 0.423 0.883 0.631 1.000 0.331 0.067 0.667 0.333 0.047 0.814 0.427 0.301 0.029 0.779 0.289 0.435

0.060 0.014 0.875 0.863 0.483 0.333 0.482 0.779 0.688 0.279 1.000 0.168 0.933 0.355 0.163 0.179 0.377 0.217 0.633 0.294 1.000 0.887 0.283 0.825 0.738 0.769 1.000 0.601 0.133 0.778 0.333 0.000 0.252 0.577 0.321 0.040 0.532 0.354 0.738

J. Yuan et al. / Energy 176 (2019) 623e640

C11 C12 C13 C14 C15 C21 C22 C23 C24 C25 C31 C32 C33 C34 C35 C41 C42 C43 C44 C45 C46 C51 C52 C53 C54 C55 C61 C62 C63 C64 C65 C71 C72 C73 C74 C81 C82 C83 C84

J. Yuan et al. / Energy 176 (2019) 623e640

633

Fig. 3. ANP model of CFPP risk assessment.

Fig. 4. Combined criteria weights of these dimensions and criteria.

other criteria such as social development, political risk and coal power revenue. Even so, the scores of some risk factors are still low. For example, the environmental risk (C5) in Singapore belongs to medium risk. Some improvements should be adopted to reduce

this risk. Only in this way, overseas investment can better attract investors’ attentions. By contraries, the investment risks in the country of Burma, Ukraine, Bangladesh, India and Pakistan are very high. Investors should be very prudent to invest in these countries.

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J. Yuan et al. / Energy 176 (2019) 623e640

Fig. 5. Risk level of countries along the BRI.

Fig. 6. Comparative criteria weights determined by ANP and Entropy method.

Table 6 Performances of each alternative on the eight criteria. Countries

C1

C2

C3

C4

C5

C6

C7

C8

Pakistan Poland Korea Russia Philippines Kazakhstan Cambodia Czech Malaysia Bangladesh Burma South Africa

0.614 0.887 0.961 0.908 0.918 0.677 0.851 0.853 0.660 0.724 0.855 0.372

0.384 0.709 0.712 0.371 0.642 0.487 0.464 0.802 0.236 0.263 0.156 0.397

0.205 0.616 0.946 0.717 0.747 0.569 0.105 1.000 0.431 0.121 0.132 0.000

0.041 0.876 0.876 0.328 0.359 0.614 0.448 0.904 0.834 0.320 0.000 0.729

0.246 0.524 0.089 0.782 0.391 0.473 0.464 0.495 0.896 0.462 0.000 0.697

1.000 0.687 0.266 0.914 0.280 0.854 0.989 0.261 0.864 0.000 0.527 0.677

0.702 0.492 0.404 0.542 0.669 0.728 1.000 0.166 0.609 0.762 0.936 0.491

0.359 0.195 0.783 0.690 0.804 0.095 1.000 0.558 0.886 0.444 0.262 0.000

J. Yuan et al. / Energy 176 (2019) 623e640

635

Table 6 (continued ) Countries

C1

C2

C3

C4

C5

C6

C7

C8

Thailand Turkey Ukraine Singapore New Zealand Israel India Indonesia Vietnam

0.498 0.745 0.000 0.444 0.854 0.723 0.738 1.000 0.695

0.689 0.531 0.269 1.000 0.769 0.559 0.000 0.337 0.471

0.811 0.596 0.488 0.926 0.989 0.677 0.418 0.530 0.307

0.605 0.498 0.190 0.999 1.000 0.772 0.473 0.520 0.634

0.965 0.315 0.803 0.407 0.592 0.519 0.073 1.000 0.627

0.805 0.522 0.301 0.828 0.725 0.061 0.130 0.632 0.788

0.715 0.529 0.492 0.498 0.303 0.000 0.782 0.804 0.522

0.717 0.816 0.740 0.932 0.667 0.661 0.435 0.703 0.796

6. Conclusions and policy recommendations This paper put forward a hybrid evaluation model for CFPP investment risk in countries along the Belt and Road initiative based on analytic network process, Entropy and TODIM method. A combined analytic network process-Entropy is used to determine the weights of criteria since it not only considers the interdependence between criteria but combines subjective and objective. While the TODIM is used to rate the risk level of these countries since it takes the psychological characteristics of decision-makers into account. After that, some conclusions can be drawn. Firstly, among all the criteria, the weight of economic foundation is the largest and has the greatest influence on the results of the investment risk evaluation. This demonstrates that a nation, when selecting another country to invest CFPP, will give priority to a consideration of the economic size, development level, economic growth, inflation index, and debt level. Besides this criterion, the social development, political risk, Chinese factors are also important factors that must be considered in conducting energy investment. Secondly, the assessed results show that most countries along the BRI have high investment risks for CFPP. Fortunately, China still has many suitable investment choices in some countries such as Singapore, New Zealand and Thailand. However, these countries perform poorly in some risk factors. Thus, to further attract investors, more efforts should be made to mitigate these risks. In addressing the possible risk faced by China in foreign CFPP investment, we propose some policy recommendations: (1) comprehensively consider the various aspects of risk; When Chinese enterprises conduct in overseas CFPP investment, it should not

only focus on a single risk. In order to maintain sustainable development, various kinds of risks must be taken into account simultaneously; (2) further improve the construction of energy cooperation mechanism. Building a dual multilateral cooperation mechanism to support the BRI energy cooperation and promoting the establishment of regional energy market is necessary. In the dual multilateral energy cooperation, we can improve our country’s power of discourse on global energy management, improve the level of regional energy security and improve the ability of optimizing the allocation of energy resources in the region; (3) establish a national risk early warning mechanism to regularly publish risk reports on overseas investment. Because of the asymmetric information, most Chinese enterprises lack a reasonable estimate of the overseas investment market, mainly in terms of laws and regulations. Therefore, China should improve its overseas investment information database and regularly issue risk assessment reports to form a risk early warning mechanism to help enterprises to reduce the overseas investment risk. Acknowledgements Project supported by the Fundamental Research Funds for the Central Universities (No. 2018ZD14), the funding of National Natural Science Foundation of China (71673085) and Beijing Social Science Fund (16YJB027). Appendix A

No.

Criteria

Sources

Mean scores

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

Economic size Development level Economic growth Inflation index Exchange fluctuations Investment level Trade openness Debt level Poverty index Educational level Crime index Unemployment rate Financial freedom Business freedom Labor freedom Corruption control Government effectiveness Political stability Regulatory quality Law degree Expressions and accountability

[17e19] [18,19] [17e19] [18,19] [18,19] [18] [18,19] [18,19] [18] [18,19] [18,19] [18,19] [18,19] [18,19] [18] [17e19] [18] [18,19] [18,19] [17e19] [17,18]

4.56 4.63 4.29 4.10 4.27 4.09 4.31 4.24 3.88 4.31 4.40 4.17 4.32 4.23 3.49 4.40 4.12 4.26 4.19 4.30 3.63 (continued on next page)

636

J. Yuan et al. / Energy 176 (2019) 623e640 (continued ) No.

Criteria

Sources

Mean scores

22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56

BIT sign Bilateral agreement Partnership Enforcing contracts Resolving insolvency Import and export dependence Investment dependence Investment loss Energy endowment Energy surplus Energy trade dependence Among of energy investment Emission level Water stress PM2.5 concentration Emission growth Energy efficiency NDC target Environmental governance level Forest area Electrified rate Electrification rate Electric power import Coal surplus Proportion of electric coal Coal power planning Coal power proportion Coal power growth Renewable generating capacity Planning renewable machine Growth rate of PV power generation Population growth Urbanization rate War situation Power demand growth

[18,19] [19] [19] [17] [17] [18,19] [18,19] [18] [18] [18] [18] [18] [17e19] [19] [17,19] [18,19] [18] [18,19] [18] [17] [19] [19] [19] [19] [19] [19] [19] [19] [19] [19] [19] Proposed Proposed Proposed Proposed

4.88 4.63 4.72 3.33 3.41 4.75 4.67 3.68 3.51 3.48 3.44 3.51 4.95 4.37 4.51 4.26 3.49 4.85 3.28 3.35 4.06 4.17 4.21 4.92 4.29 4.56 4.09 3.77 2.63 2.84 2.85 4.07 4.18 4.37 4.46

by by by by

authors authors authors authors

Appendix B Original data of the selected criteria.

Table B1 Countries

Economic size (bn dollars)

Development level (dollars)

Economic growth (%)

Inflation index (%)

Debt level (%)

Pakistan Poland Korea Russia Philippines Kazakhstan Cambodia Czech Malaysia Bangladesh Burma South Africa Thailand Turkey Ukraine Singapore New Zealand Israel India Indonesia Vietnam

22.775 57.206 130.466 165.443 28.448 18.831 1.701 23.134 34.405 16.777 7.447 41.950 40.642 112.248 12.400 29.495 17.140 28.781 246.493 103.769 16.410

1178.798 15065.971 25458.887 11279.625 2753.350 10582.497 1079.114 21894.113 11031.822 1029.578 1408.141 7488.990 5901.884 14116.980 2905.857 52600.641 36521.959 33677.462 1861.491 3974.058 1735.291

3.385 2.909 2.365 0.408 5.264 0.322 5.292 2.396 2.669 5.963 4.908 1.017 2.929 1.573 2.721 0.682 0.913 2.074 5.884 3.828 5.091

0.546 0.416 3.610 1.659 1.807 3.456 1.236 1.951 3.556 7.011 1.778 6.728 13.638 8.098 17.142 1.435 2.417 0.843 3.612 2.450 1.111

64.400 51.300 35.900 17.700 37.100 23.300 33.600 40.900 57.400 34.000 32.000 50.100 43.100 32.600 80.200 98.200 30.400 64.600 67.200 27.300 59.300

J. Yuan et al. / Energy 176 (2019) 623e640

637

Table B2 Countries

Financial freedom (0e100)

Business freedom (0e100)

Exchange fluctuations

Trade Openness (%)

Investment level (%)

Pakistan Poland Korea Russia Philippines Kazakhstan Cambodia Czech Malaysia Bangladesh Burma South Africa Thailand Turkey Ukraine Singapore New Zealand Israel India Indonesia Vietnam

40 70 70 30 60 50 50 80 50 30 20 50 60 60 30 80 80 70 40 60 40

61.2 67.8 90.6 74.8 62.6 74.5 29.6 67.2 90.8 53.4 50.1 62 69.9 64.3 62.1 95.1 91.8 69.9 52.8 49.1 61.2

0.073 0.119 0.150 0.166 0.078 0.359 0.011 0.082 1.211 0.096 0.088 0.120 0.078 0.122 0.372 0.094 0.083 0.298 0.650 0.126 0.276

0.164 0.478 0.500 0.176 0.421 0.312 0.784 0.751 0.621 0.210 0.001 0.303 0.575 0.231 0.449 1.686 0.322 0.344 0.209 0.193 1.160

0.833 3.555 0.858 2.536 2.617 12.365 11.426 3.327 4.558 1.051 5.185 0.762 0.753 1.545 3.689 20.741 1.046 3.746 1.964 0.479 6.138

Table B3 Countries

Population growth (%)

Urbanization rate (%)

Unemployment rate (%)

Crime index (0e100)

Educational level (%)

Pakistan Poland Korea Russia Philippines Kazakhstan Cambodia Czech Malaysia Bangladesh Burma South Africa Thailand Turkey Ukraine Singapore New Zealand Israel India Indonesia Vietnam

1.999 0.043 1.137 1.148 1.960 0.451 0.170 1.564 1.416 1.565 0.192 1.499 1.080 0.915 1.301 0.299 1.573 0.331 1.297 1.060 2.099

39.224 60.531 82.592 74.101 44.289 53.229 20.945 72.980 75.370 35.035 34.650 65.295 51.540 73.887 69.915 100.000 86.322 92.205 33.136 54.466 34.236

4.044 4.955 3.755 5.173 2.784 4.860 0.216 3.063 3.415 4.367 0.794 27.718 1.083 11.263 9.455 2.024 4.881 4.271 3.523 4.283 2.053

56.63 31.41 14.31 47.31 37.61 45.88 48.17 32.06 68.55 68.56 45.00 78.43 42.56 39.43 51.27 15.81 36.37 30.63 46.59 46.97 55.69

44.387 106.610 100.197 104.397 88.335 109.450 60.52 105.047 84.973 63.415 43 102.754 120.632 103.050 96.786 108.13 114.746 102.487 73.979 87.299 55.69

Table B4 Countries

Corruption Control (2.5 e2.5)

Government effectiveness (2.5 Regulatory quality (2.5 e2.5) e2.5)

Political stability (2.5 e2.5)

Law degree (2.5 e2.5)

War situation (0 e1)

Pakistan Poland Korea Russia Philippines Kazakhstan Cambodia Czech Malaysia Bangladesh Burma South Africa Thailand Turkey Ukraine Singapore New Zealand Israel India Indonesia Vietnam

0.761 0.580 0.489 0.863 0.430 0.756 1.044 0.391 0.284 0.875 0.894 0.042

0.661 0.800 1.026 0.183 0.107 0.053 0.690 1.051 0.964 0.728 1.238 0.265

0.624 0.997 1.156 0.522 0.037 0.025 0.480 1.085 0.772 0.929 1.262 0.304

2.544 0.874 0.099 1.049 0.838 0.098 0.098 0.960 0.191 1.155 1.170 0.176

0.788 0.797 0.955 0.720 0.345 0.368 0.920 1.123 0.574 0.700 1.221 0.056

0.1 1 1 0.6 0.1 1 1 1 1 0.9 0.1 1

0.400 0.115 0.980 2.130 2.291

0.357 0.233 0.514 2.252 1.890

0.299 0.330 0.580 2.263 1.948

0.956 1.276 1.933 1.237 1.491

0.106 0.060 0.801 1.882 2.021

0.9 0.6 0.6 1 1

0.894 0.380 0.454 0.447

1.375 0.101 0.225 0.077

1.270 0.391 0.211 0.496

1.118 0.921 0.596 0.011

1.168 0.057 0.413 0.269

0.9 0.6 0.9 1

638

J. Yuan et al. / Energy 176 (2019) 623e640

Table B5 Countries

Emission level (t)

Emission growth (%)

Water stress (0e20)

PM 2.5 concentration (ug/m3)

NDC target (%)

Pakistan Poland Korea Russia Philippines Kazakhstan Cambodia Czech Malaysia Bangladesh Burma South Africa Thailand Turkey Ukraine Singapore New Zealand Israel India Indonesia Vietnam

0.8 7.3 11.6 10.2 1 12.8 0.5 9.4 7.3 0.4 0.5 7.8 3.6 4.1 4.2 8 6.7 7.4 1.6 1.7 1.8

0 0 2.655 0.971 0 3.759 25 0 1.351 0 25 2.5 0 2.5 19.231 3.614 2.899 1.333 0 0 12.5

2.31 11.9 11.76 13.18 8.8 1.38 18.32 12.81 14.76 18.88 19.32 7.84 12.39 3.59 7.6 0 17.52 0.02 5.36 8.18 15.93

59.8 24.1 26.6 14.8 22.2 15.4 23 20.1 14.8 83.5 51 27.4 24.6 33.5 16.4 17 5.2 19.1 62.4 14.4 25.7

20 40 37 27.5 70 15 26.73 40 35 5 27 6.92 20 21 40 36 70 26 34 29 20

Table B6

Table B7 (continued )

Countries

BIT Import and sign export dependence

Investment dependence

Partnership Bilateral (0e5) agreement (0 e5)

Pakistan Poland Korea Russia Philippines Kazakhstan Cambodia Czech Malaysia Bangladesh Burma South Africa Thailand Turkey Ukraine Singapore New Zealand Israel India Indonesia Vietnam

1 1 1 1 1 1 1 1 1 0 1 1

0.405 0.083 0.458 0.238 0.527 0.322 0.330 0.076 0.454 0.321 0.840 0.392

0.04 0 0.01 0.01 0.01 0.03 0.15 0 0.01 0.01 0.13 0.02

5 3.5 0.5 4.5 0.5 3.5 4 2.5 3.5 2 4 3.5

3 4 2 4 2 3 3 2 3 2 1 2

1 1 1 1 1

0.305 0.093 0.136 0.153 0.247

0.01 0 0 0.02 0

3.5 4 2.5 4 3.5

3 2 2 3 3

1 1 1 1

0.124 0.151 0.329 0.536

0 0.01 0.01 0.02

0.5 0.5 3.5 4

2 2 2 2

Table B7 Countries

Electrified rate (%)

Electrification rate (%)

Power demand growth (%)

Electric power import (%)

Pakistan Poland Korea Russia Philippines Kazakhstan Cambodia Czech Malaysia Bangladesh Burma South Africa Thailand Turkey

99.147 100 100 100 90.982 100 49.771 100 100 75.92 57.009 84.2

8.213 13.564 16.762 11.283 12.401 10.667 5.499 13.503 13.083 11.798 4.924 13.155

2.718 1.98 4.052 1.778 5.19 6.238 18.312 0.554 4.938 8.454 17.992 0.514

0.408 0.202 0 1.092 0 0.004 34.705 14.918 0.007 0 0 0.621

100 100

10.891 15.282

4.414 5.946

6.833 1.505

Countries

Electrified rate (%)

Electrification rate (%)

Power demand growth (%)

Electric power import (%)

Ukraine Singapore New Zealand Israel India Indonesia Vietnam

100 100 100

12.370 14.586 16.720

0.934 3.666 0.136

0.825 0 0

100 84.52 97.62 100

20.183 10.675 7.759 16.333

1.946 7.432 7.89 10.624

8.092 0.007 0.006 1.032

Table B8 Countries

Coal surplus

Proportion of electric Coal power coal (%) planning

Coal power proportion (%)

Pakistan Poland Korea Russia Philippines Kazakhstan Cambodia Czech Malaysia Bangladesh Burma South Africa Thailand Turkey Ukraine Singapore New Zealand Israel India Indonesia Vietnam

0.736 0.179 0.185 0.431 0.045 0.239 0 0.079 0.070 0.315 0.009 0.138

1.846 67.763 69.724 46.518 79.038 54.707 97.785 72.737 89.210 11.498 15.730 64.002

12385 9090 7359 720 12141 636 3325 660 2600 21998 2030 11892

0.156 82.993 42.410 14.901 42.782 71.948 28.211 51.456 37.857 1.970 2.020 93.005

0.168 0.256 1.130 0 2.235

51.874 53.905 59.435 62.899 32.968

5256 42890 1320 0 0

21.643 30.268 38.736 1.098 4.505

0 0.162 0.065 0.090

96.797 67.033 76.602 52.897

0 131359 37905 46525

49.555 75.075 52.649 24.528

Appendix C. Global dominance degree decision matrix

A22

40.18 0.00 19.31 26.33 31.37 27.66 27.82 15.45 19.97 42.39 43.53 33.44 21.02 31.72 38.85 12.28 16.49 27.15 39.56 25.36 32.61 40.18 0.00 0.00 22.53 26.28 20.92 19.48 22.21 18.70 27.40 23.26 26.66 25.24 28.58 15.08 23.19 31.20 20.05 20.25 31.61 27.08 18.09 18.74 0.00 22.53 32.09 23.34 24.91 28.22 24.80 25.66 18.69 24.85 24.13 34.86 35.89 28.31 18.74 24.42 38.11 17.17 22.42 34.42 33.40 24.00 0.00 32.09 23.34

A21 A20

33.58 23.08 21.68 23.67 20.35 23.50 20.53 22.65 21.25 33.04 34.87 28.76 17.29 24.16 37.83 21.86 19.68 29.65 30.45 0.00 22.85 33.58 23.08 25.91 17.60 15.82 21.32 14.32 17.25 18.45 17.72 16.68 25.42 30.56 21.69 14.06 20.37 28.01 18.84 19.87 22.09 0.00 10.54 17.69 25.91 17.60

A19 A18

35.46 17.07 15.00 25.49 25.29 24.51 25.26 17.69 18.72 35.97 40.99 29.51 19.21 26.13 32.18 6.39 11.79 0.00 35.23 22.72 25.41 35.46 17.07 45.24 32.78 35.66 38.40 41.46 38.23 38.03 34.41 34.35 48.68 49.45 46.33 33.58 42.72 49.45 16.31 0.00 44.01 53.68 39.52 40.35 45.24 32.78

A17 A16

47.20 36.81 39.13 42.40 47.70 39.42 39.63 39.78 35.57 54.71 53.13 46.99 37.10 45.58 51.20 0.00 26.10 46.24 52.98 41.35 44.55 47.20 36.81 24.80 14.55 16.27 11.64 17.09 16.15 16.20 14.70 12.68 25.71 30.93 20.89 8.73 16.01 0.00 13.03 14.26 22.23 22.82 11.31 15.16 24.80 14.55

A15 A14

33.01 16.65 16.16 19.04 22.53 22.71 23.60 17.11 21.00 31.66 37.67 24.65 11.69 0.00 30.48 13.19 14.69 24.53 28.60 17.75 21.40 33.01 16.65 35.51 21.66 24.99 25.93 28.64 24.36 22.92 24.70 21.89 37.52 41.01 31.26 0.00 27.40 42.27 19.77 19.30 35.01 38.26 22.36 27.56 35.51 21.66

A13 A12

32.19 9.65 14.09 21.30 20.43 16.45 21.33 14.07 11.48 31.42 38.63 0.00 14.37 19.89 29.15 13.06 11.69 25.04 31.40 17.40 21.22 32.19 9.65 25.27 29.32 29.89 23.90 25.37 27.68 10.26 30.68 31.32 26.27 0.00 34.34 22.61 25.40 36.88 23.58 24.47 35.59 32.94 22.64 20.76 25.27 29.32

A11 A10

20.40 23.98 24.14 19.89 20.20 19.81 17.24 23.08 21.46 0.00 24.00 24.46 15.67 23.98 30.87 20.31 16.65 32.05 23.29 14.84 18.10 20.40 23.98 38.40 21.96 23.76 28.55 32.56 26.82 23.74 25.17 0.00 40.82 41.91 33.89 23.21 33.68 40.28 15.79 15.97 32.50 40.25 26.32 27.35 38.40 21.96

A9 A8

39.29 16.62 20.01 26.50 28.40 30.06 27.47 0.00 19.00 39.75 43.56 33.20 22.86 32.04 34.28 10.19 13.39 22.31 41.34 25.54 30.14 39.29 16.62 43.25 38.26 40.94 37.30 40.69 35.40 0.00 39.30 36.90 45.27 35.23 46.37 33.22 41.79 51.56 31.27 34.71 45.62 50.09 34.26 32.35 43.25 38.26

A7 A6

33.74 19.21 20.78 21.27 28.11 0.00 22.11 23.26 17.61 40.29 39.93 26.50 17.01 25.54 35.99 12.88 14.82 30.51 35.18 18.47 25.89 33.74 19.21 31.72 19.32 17.02 24.02 0.00 24.32 20.62 17.77 18.31 31.16 33.93 28.86 14.78 23.45 30.95 17.64 17.55 25.52 28.14 16.27 22.51 31.72 19.32

A5

42.06 20.86 0.00 28.87 27.54 31.83 28.67 17.43 18.38 39.07 44.78 34.80 24.08 30.03 39.95 12.24 14.35 27.50 38.14 26.35 27.94 42.06 20.86

A4 A3

40.18 0.00 19.31 26.33 31.37 27.66 27.82 15.45 19.97 42.39 43.53 33.44 21.02 31.72 38.85 12.28 16.49 27.15 39.56 25.36 32.61 40.18 0.00 0.00 22.53 26.28 20.92 19.48 22.21 18.70 27.40 23.26 26.66 25.24 28.58 15.08 23.19 31.20 20.05 20.25 31.61 27.08 18.09 18.74 0.00 22.53 A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15 A16 A17 A18 A19 A20 A21 A22 A23

A1

A2

31.71 21.61 24.72 0.00 29.58 20.41 26.18 26.12 22.17 34.92 37.57 31.54 17.98 26.54 34.39 17.66 16.95 31.20 32.74 21.11 27.23 31.71 21.61

A23

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639

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