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1 Appled Energy 165 (216) Contents lsts avalable at ScenceDrect Appled Energy journal homepage: Features and evoluton of nternatonal fossl fuel trade network based on value of emergy Weqong Zhong, Hazhong An, We Fang, Xangyun Gao, D Dong School of Humantes and Economc Management, Chna Unversty of Geoscences, Bejng, Chna Key Laboratory of Carryng Capacty Assessment for Resource and Envronment, Mnstry of Land and Resources, Bejng, Chna Lab of Resources and Envronmental Management, Chna Unversty of Geoscences, Bejng, Chna hghlghts Number of trade relatons and trade quanttes follow power law dstrbuton. The pattern of top relatons s dversfed. The trade densty of fossl fuel s ncreasng. Coal s the cheapest fuel measurng by energy cost and s most wdely traded. Countres wth more than 2 trade relatonshps tend to have herarchy structure. artcle nfo abstract Artcle hstory: Receved 2 July 215 Receved n revsed form 8 December 215 Accepted 17 December 215 Avalable onlne 12 January 216 Keywords: Fossl fuel Internatonal trade Emergy Complex network Fossl fuel s crucal to the development of modern socety. The major types of fossl fuel are coal, crude ol and natural gas. The uneven dstrbuton of the producton and consumpton of fossl fuel makes the fossl fuel flows between countres by nternatonal trade. Ths study ams to quanttatvely analyse the features and evoluton of the nternatonal trade of fossl fuel by complex network and emergy. We transform the trade quantty of coal, crude ol and natural gas nto emergy by transformty and the sum of the three emerges s the emergy of fossl fuel. The complex network models of the ntegrated fossl fuel trade as well as the trade of coal, crude ol and natural gas are bult up based on the value of emergy. We analyse the trade relatonshps, trade quantty, trade densty, and herarchy structure of the networks. We fnd that the number of trade relatonshps and the trade quanttes follow the power law dstrbuton; countres wth many export relatonshps tend to have many mport relatonshps; the centralzaton of trade quantty s becomng more ntense for fossl fuel, crude ol and coal, but less ntense for natural gas; the pattern of top relatonshps s dversfed; the trade densty of fossl fuel s ncreasng; and countres wth more than 2 trade relatonshps tend to have a herarchy structure. Our fndngs mplcate that as the herarchy structure s becomng more ordered, the statuses of the countres are clearer, and thus t s easer for polcy makers to dentfy the roles of ther own countres or the roles of other countres. Coal s the cheapest fuel measurng by energy cost and s the most wdely traded type of fossl fuel. When two countres exchange fossl fuel and money n the nternatonal trade, they should look further nto the energy cost of them and reconsder the effectveness of the trade. Our study can also reveal the trade strategy of the countres. Ó 215 Elsever Ltd. All rghts reserved. 1. Introducton The nternatonal trade of fossl fuel s an ntegrated system wth three major commodtes: coal, crude ol and natural gas. Correspondng author at: School of Humantes and Economc Management, Chna Unversty of Geoscences, Bejng, Chna. Tel.: ; fax: E-mal address: ahz369@163.com (H. An). Accordng to the statstcs of U.S. Energy Informaton Admnstraton, the three major types of fossl fuels account to 86% of the world total prmary energy consumpton n There are numerous countres and complcated relatonshps n the nternatonal trade of fossl fuel whch form a huge and complex system. A better understandng of the characterstcs of ths ntegrated /Ó 215 Elsever Ltd. All rghts reserved.

2 W. Zhong et al. / Appled Energy 165 (216) complex system can help us understand the nternatonal fossl fuel market [1]. Prevous studes on nternatonal fossl fuel trade focused on energy securty [2], trade patterns [3] and poltcal factors [4]. Ths study ams to quanttatvely analyze the features and evoluton of nternatonal fossl fuel trade by combnng network analyss and emergy transformty. It provdes a new perspectve for the study of nternatonal trade of fossl fuel. Complex network modelng has the advantage of analyzng the complex system of nternatonal trade. In 23, Serrano et al. [5] ntroduced complex network model nto the study of nternatonal trade. Then Garlaschell et al. [6] studed the ftness-dependent topologcal propertes of the nternatonal trade network. The study of Fagolo et al. [7,8] provded a detaled quanttatve analyss of the trade lnks and the role of the countres topologcally and dynamcally. Vdmer et al. [9] appled lnk predcton algorthms to predct the future evoluton of the nternatonal trade network. In recent years, some scholars used complex network to analyss the nternatonal trade of energy. For example, Geng et al. [1] studed the structure and the ntegraton of the nternatonal natural gas market by complex network. Üster et al. [11] desgned an ntegrated large-scale mxed-nteger nonlnear optmzaton model to analyze the natural gas transmsson network. Zhong et al. [12] constructed weghted and unweghted complex network models to study the evoluton of communtes n the nternatonal ol trade. J et al. [13] ntroduced a global ol trade core network to analyze the overall features, regonal characterstcs and stablty of the ol trade. Zhong et al. [13] and Zhang et al. [14] ntroduced complex network to analyze the competton between countres n the ol trade. However, as far as we know, most of the prevous studes on nternatonal energy trade are based on sngle commodty. Our study provdes an ntegrated vew of the nternatonal trade of fossl fuel by consderng the trade quantty of coal, crude ol and natural gas together n the model, and reveals features of the ntegrated system. A unfed unt to measure the commodtes of coal, crude ol and natural gas s needed because they are n dfferent forms and qualtes. Tradtonally, money s appled to measure the ntegrated trade volume, however the fluctuatng prce and exchange rate [15] wll affect the results. The unt of joule can be used to measure energy content of the fuels, however t only measures the ablty to cause work. Exergy s another concept whch measure the maxmum useful work of the fuels [16]. These methods cannot reflect the cost of the energy whch means how much energy s needed n order to produce a certan amount of fossl fuel. The man dea of Emergy s energy cost whch regards the dfference of energy qualty and the accumulatve cost of energy [17]. It measures the values of resources n common unts of the solar energy used to make them (n unt of sej) [18,19]. Transformty (n unt of sej/j) can be used to transform the trade quantty of coal, crude ol and natural gas nto emergy. The sum of the three emerges can be used to measure the emergy flow of fossl fuel. If a country exports fossl fuel, t not only exports the energy currently exstng n the commodtes, but also exports the energy consumed n formng, mnng and producng the commodtes. If a country mports fossl fuel, t also mports the emboded energy cost n the commodtes. As far as we know, most of the prevous studes of nternatonal energy trade use money, energy or exergy as trade quantty. Our study goes further n consderng the accumulatve amount of solar energy (Emergy) as trade quantty. In ths study, we desgn the ntegrated complex network model of fossl fuel as well as the sngle commodty network models of coal, crude ol and natural gas based on the emergy flows among countres. The characterstcs of the nternatonal fossl fuel trade can be reflected by network analyss. Secton 2 ntroduces the data and the process of modelng. Four ndexes of network analyss are ntroduced: degree and strength are ndcators of the ndvdual countres, and network densty and herarchy structure are ndcators of the whole network. Secton 3 s the analyss of trade relatonshps, trade quantty, trade densty, and herarchy structure of the network. Secton 4 s the dscusson and concluson remarks. 2. Data and method 2.1. Data and transformty The data of nternatonal trade of coal, crude ol and natural gas s from the webste of UN Comtrade whch contans all the export and mport flows among 226 countres. The trade volumes are measured by klogram. We selected the annual data of all the avalable countres from 2 to 213. We transformed the trade quanttes of the three fuels nto emergy and the sum of them s the emergy of fossl fuel. The descrpton of the data source, the energy content of the commodtes and the transformty of coal, crude ol and natural gas are shown n Table 1. In our data, only crude ol s ncluded n the HS Code 279, and there are several categores of coal n the HS Code 271. We use the average energy content and the average emergy transformty of crude ol and coal n our study. The total emergy n fossl fuel trade ncreased durng 2 28 as the world economy grew, and the total emergy declned n 29 after the US mortgage subprme crss. 2 The majorty of fossl fuel trade emergy was contrbuted by crude ol, coal contrbuted the least emergy, and natural gas contrbuted a lttle more than coal (please see Fg. 1) Internatonal trade network model The complex network model G =(V, E) contans the nodes V and the edges E, where V ={v : =1,2,..., n}, n s the number of nodes, E ={e : =1,2,..., m}, and m s the number of edges. In our model, the nodes are the countres, the edges are the trade relatonshps, the drectons of the edges are the drectons of the emergy flows, and the weghts of the edges are the value of emerges. We constructed network models of the ntegrated fossl fuel trade as well as the sngle commodtes based on the transformed data. An example of the ntegrated fossl fuel trade network n 212 s shown n Fg. 2. We fltered the network wth trade quantty n order to make t more readable by showng the top 5 countres n trade quantty n the network. The sze of the node s the total trade quantty of the country. The larger the node s, the more emergy the country has trade n ths year. The wdth of the edge s the value of the emergy of ths trade lnk. The wder the edge s, the hgher value of emergy ths trade lnk has Degree: the range of the drect mpact Degree s the number of drect trade relatonshps of a country. It reflects the range of a country s drect mpact. The out-degree s the number of export lnks a country has wth others, and the ndegree s the number of mport lnks. The hgher value of outdegree or n-degree ndcates a wder range of the country s drect mpact. These values are computed by [21] k out ðtþ ¼ Xn d j ðtþ j¼1 k n ðtþ ¼ Xn j¼1 d j ðtþ 2 The U.S. subprme mortgage crss was a natonwde bankng emergency that concded wth the U.S. recesson of December 27 June 29 (explanaton from Wkpeda). ð1þ ð2þ

3 87 W. Zhong et al. / Appled Energy 165 (216) Table 1 Data descrpton, energy content and transformty. Commodty HS code Descrpton Energy content [2] Transformty [19] Coal 271 Coal; brquettes, ovods and smlar sold fuels manufactured from coal 2.94E4 J/g 8.17E4 sej/j Crude ol 279 Petroleum ols and ols obtaned from btumnous mnerals, crude 4.337E4 J/g 1.48E5 sej/j Natural gas 271,111 Natural gas, lquefed (LNG) 271,121 Natural gas n gaseous state (NG) 3.883E7 J/m E5 sej/j Note: Data source s In the data source, the unt of the commodtes s klogram, thus we convert the unts by 1 kg of NG = 14 L of NG, 1 kg of LNG = 2.35 L of LNG, and 1 L of LNG = 6 L of NG. The geobosphere baselne s 15.2E24 sej/yr. The transformty of coal s the average of hard coal and soft coal accordng to [19]. where f country exports ol to country j durng year t, a lnk from to j s drawn, and d j (t) = 1. Otherwse, no lnk s drawn, and d j (t)=. The out-degree k out ðtþ of country n the year t s the sum of d j (t), and the n-degree k n ðtþ of country n the year t s the sum of d j (t). If the network has a degree dstrbuton that can be ft wth a power law dstrbuton (4), t mples that the network s a scalefree network, where c s the power law ndex and k s the degree of the nodes [1]. PðkÞ k c Coal Crude ol Natural gas ð3þ Strength: the quantty of emergy The total trade quantty of emergy of a country can be measured by strength n the network. The out-strength s out ðtþ and n-strength s n ðtþ of country reflect a node s mportance n the network consderng both relatonshps and quanttes of emergy. The hgher the value s, the more mportant the country s. s out ðtþ and s n ðtþ are computed by [21] s out ðtþ ¼ Xn d j ðtþw j ðtþ j¼1 s n ðtþ ¼ Xn j¼1 d j ðtþw j ðtþ ð4þ ð5þ Total Emergy (sej) 2.5E+25 2.E E+25 1.E+25 5.E+24.E+ Year Fg. 1. Total emergy n the 4 types of trade networks. where w,j (t) s the weght of d j (t), whch s the total amount of emergy that country exports to country j durng the year t Network densty: the tghtness of relatonshps among countres Network densty can be used to measure the tghtness of the trade relatonshps among the countres n the fossl fuel trade network. It equals to total number of relatonshps that actually exst dvded by maxmum number of relatonshps that theoretcally can exst. If the number of actual relatonshp s m, the number of nodes s n, then the network densty s [1]: D ¼ 2m nðn 1Þ ð6þ Fg. 2. Fltered network model of nternatonal fossl fuel trade n 212.

4 W. Zhong et al. / Appled Energy 165 (216) Herarchy structure: the order of the trade network Clusterng coeffcent of a country s the probablty of trade relatonshp exstng between the countres connectng to ths country n the network. It reflects the connectvty of the neghborng countres of ths country. If a country s neghbors are closely related, the country has a hgher clusterng coeffcent; on the contrary, f a country s neghbors are loosely related, the clusterng coeffcent of ths country s lower. If nodes wth the same degree have smlar clusterng coeffcent, the herarchy structure of the network s more ordered because smlar roles have smlar connectvty. The clusterng coeffcent C of node wth degree k s computed by: C ¼ n =k ðk 1Þ where n s the number of the edges among the neghbors of node. 3. Results and analyss 3.1. Trade relatons Degree s the number of edges of a node n the network. It s an ndex measurng how many countres have trade relatonshps wth a gven country. It ndcates the actveness of a country n the network. Countres wth hgher degrees possess mportant roles, because they have wder range of trade, and ther mpacts can drectly reach more partners. The trade relatonshps n the 4 types of networks each year follow power law dstrbuton. A small number of countres own many trade partners and most of the countres own a few trade partners (the fgures of 2, 26 and 213 are shown n Fg. 3). The top 1 countres n number of mport relatonshps of ð7þ fossl fuel are shown n Table 2. We can see that the top 1 countres were manly from North Amerca, Europe and East Asa area. From 2 to 29, the USA was the country wth the largest number of mport relatonshps. However, n 21 and 213 Chna became No. 1, and n 211 and 212 Inda was No. 1 n mport relatonshp. The top 1 countres n number of export relatonshps of fossl fuel are shown n Table 3. An nterestng phenomenon s that many mportng countres were also wth hgh out-degrees. The USA ranked No. 1 n the number of export relatonshps through the whole observaton perod. The rank of Chna was also ncreasng, and became No. 2 snce Trade quantty The trade quanttes of emergy are carred by the trade lnks (edges), thus we analyzed the accumulatve dstrbutons of the weghts of the edges n the 4 types of networks each year. The results of 3 years (2, 26, and 213) are shown n Fg. 4. We should focus on the gaps of the curves whch were movng toward up left corner. Ths mples that the trade of coal, crude ol and fossl fuel were becomng more concentrated from 2 to 213. However, the tendency of natural gas was n opposte drecton. It was less concentrated. The proportons of edges shoulderng 8% of the trade quantty are shown n Fg. 5. A small number of trade lnks shoulder a large part of the trade quanttes. Ths phenomenon s less obvous n the network of crude ol, and s more obvous n the network of natural gas. We can see that less than 8% of the trade relatonshps contan up to 8% of the trade quantty of emergy n the fossl fuel trade network. The top 1 countres n mportng emergy of fossl fuel are shown n Table 4. We can see that the top 1 countres are manly ln (k) ln (p(k)) y = x R² =.7843 ln (k) -2-4 ln (p(k)) y = x R² =.7389 ln (k) -2-4 ln (p(k)) y = x R² = Fg. 3. Power law of the number of trade partners. Table 2 Top 1 countres n n-degree n fossl fuel trade. Year Rank USA Italy France Germany UK Span Chna Netherlands South Korea Sngapore 21 USA France UK Italy Span Germany Chna Netherlands South Korea Thaland 22 USA Germany France Span Italy Netherlands Chna South Korea UK Canada 23 USA Chna Germany Span Italy France UK Canada Netherlands Belgum 24 USA France Germany Chna Span UK Italy South Korea Canada Netherlands 25 USA France Germany Chna UK Span Netherlands Italy Canada South Korea 26 USA Chna Germany France UK Span Canada Netherlands South Afrca Italy 27 USA Germany Chna UK France Italy Span Canada Netherlands Inda 28 USA Germany Chna France Inda Canada Netherlands Span UK Italy 29 USA Chna Inda Germany UK Span France South Korea Canada Netherlands 21 Chna USA Inda UK France Italy Germany Canada Span South Korea 211 Inda USA Chna UK Germany France Italy Netherlands South Korea Span 212 Inda Chna Netherlands USA France Germany South Korea Italy UK Japan 213 Chna Netherlands USA Germany South Korea Inda France UK Canada Italy

5 872 W. Zhong et al. / Appled Energy 165 (216) Table 3 Top 1 countres n out-degree n fossl fuel trade. Year Rank USA South Afrca Russa UK Germany Chna France Netherlands Australa Italy 21 USA UK Russa South Afrca Germany Chna Australa Italy France Netherlands 22 USA South Afrca UK Russa Germany Italy Chna Australa UAE Indonesa 23 USA UK Russa South Afrca Germany Chna Australa Indonesa Italy Netherlands 24 USA UK Russa Chna South Afrca Germany Australa Italy Netherlands France 25 USA South Afrca Chna Germany UK Russa Italy Ukrane Australa France 26 USA Chna South Afrca UK Germany Russa Netherlands Italy UAE Indonesa 27 USA Chna South Afrca UK Germany Inda Russa Italy France Netherlands 28 USA Chna South Afrca UK Germany Russa France UAE Australa Netherlands 29 USA Chna UK South Afrca Ngera Russa Germany UAE Italy Australa 21 USA Chna UK South Afrca Russa Germany Ngera Ukrane Italy Netherlands 211 USA Chna South Afrca Russa UK Germany Colomba Netherlands Ukrane Italy 212 USA Chna South Afrca UK Russa Germany France Netherlands Italy Ukrane 213 USA Chna UK South Afrca Russa Netherlands Germany France Italy Ukrane Fg. 4. Accumulatve dstrbuton of the weghts (emergy). from North Amerca, East Asa and Europe. The USA ranked No. 1 n most of the years, and Chna became No. 1 mportng country n 213. Japan ranked No. 2 n most of the years except n 212. The top 1 countres n exportng emergy of fossl fuel are shown n Table 5. Russa replaced Saud Araba and became the No. 1 exportng country snce 21, and Saud Araba had been No. 2 ever snce. Top 1 trade relatonshps n value of emergy n fossl fuel trade are shown n Table 6. We can see that the trade relatonshp wth hghest value of emergy s from Canada to the USA. In the early years, the flows from North Amerca and South Amerca to the USA were the top flows wth hgh emergy. However, as the mports of the USA and some other developed countres decreased and the mports of some developng countres ncreased, the ranks of top 1 relatonshps were changng Trade densty The trade densty of the fossl fuel trade was ncreasng from 2 to 213 (please see Fg. 6). The trade denstes of coal and crude ol were at the same level, whle the trade densty of natural gas was much lower than the others. Ths may due to the

6 W. Zhong et al. / Appled Energy 165 (216) Propor on 14% 12% 1% 8% 6% 4% 2% % Fossl Coal Ol Gas second most, and the natural gas was the least (please see Fg. 7 (a)). Although the number of countres was ncreasng slghtly, the number of trade lnks among them was ncreasng obvously. As a result the trade densty was ncreasng. Consstent wth the feature of the number of countres, the number of trade lnks of coal, crude ol and natural gas had the same features. The number of trade lnks of coal was the most, the number of trade lnks of crude ol was the second most, and the number of trade lnks of natural gas trade was much less than the others (please see Fg. 7(b)) Herarchy structure Year Fg. 5. Proporton of edges shoulderng 8% of emergy. restrcton of transportaton. Ths s because the majorty of natural gas was transported by ppelne and LNG tankers. The cost of transportaton made t s harder for natural gas to be wdely traded between dstant countres. To look further nto the trade densty, we plotted the number of countres and the number of trade relatonshps of the 4 types of networks. There were around 2 countres partcpatng n the fossl fuel trade durng the observaton years. The total numbers of countres were slghtly ncreasng n the four types of networks durng the observaton perod. The numbers of countres n the three types of sngle commodtes were smlar. The crude ol trade was the Degree ndcates the drect mpact of a country. Countres wth hgher degree play mportant roles, because they drectly affect more countres. Clusterng coeffcent reflects the connectvty of the neghborng countres of ths country. If nodes wth the same degree have smlar clusterng coeffcent, the herarchy structure of the network s more ordered because smlar roles have smlar connectvty. We plotted the degree and the clusterng coeffcent of all the countres n scatter dagrams chronologcally n Fg. 8. The abscssa s degree and the ordnate s the value of clusterng coeffcent [22]. From the dstrbuton of the ponts we can see that countres wth hgher degree tend to have lower clusterng coeffcent. The centralty of the ponts n Fg. 8 reflects the herarchy structure of the network. We can see that the herarchy structure of fossl fuel trade network s not obvous because nodes have smlar degree Table 4 Top 1 countres n n-strength n fossl fuel trade. Year Rank USA Japan South Korea Germany Italy France Netherlands Span Chna UK 21 USA Japan South Korea France Germany Italy Netherlands Span UK Ukrane 22 USA Japan South Korea Germany France Netherlands Italy Span UK Ukrane 23 USA Japan South Korea Germany France Italy Netherlands Chna Span Belgum 24 USA Japan South Korea France Germany Italy Netherlands Chna Span UK 25 USA Japan France Italy South Korea Germany Netherlands Chna Belgum Span 26 USA Japan France South Korea Italy Netherlands Chna Germany Belgum UK 27 USA Japan Italy France South Korea Chna Germany Inda Netherlands Span 28 USA Japan France Chna South Korea Italy Inda Germany Netherlands Span 29 USA Japan Chna South Korea Inda Italy France Germany Netherlands UK 21 USA Japan Chna Italy South Korea Inda Netherlands France Germany UK 211 USA Japan Chna South Korea Inda Italy France Netherlands Germany UK 212 USA Chna Japan Inda South Korea Italy Netherlands Germany UK Span 213 Chna Japan USA Inda South Korea Germany Italy Croata France UK Table 5 Top 1 countres n out-strength n fossl fuel trade. Year Rank Saud Araba Russa Norway Canada Algera UK UAE Iran Indonesa Australa 21 Russa Saud Araba Norway Canada UK Venezuela Australa UAE Iran Mexco 22 Russa Saud Araba Norway Canada UK Australa Mexco Algera UAE Indonesa 23 Russa Saud Araba Canada Norway Algera Iran Australa UAE Mexco UK 24 Russa Saud Araba Canada Norway Algera Iran Ngera UAE Australa Venezuela 25 Russa Saud Araba Canada Norway Algera UAE Iran Venezuela Australa Ngera 26 Russa Saud Araba Canada Norway Algera Ngera UAE Iran Australa Venezuela 27 Russa Saud Araba Canada Algera Norway Iran Ngera UAE Australa Indonesa 28 Russa Canada Saud Araba Norway Qatar Algera UAE Australa Ngera Iran 29 Russa Saud Araba Norway Canada Australa Indonesa Algera Iran Ngera UAE 21 Russa Saud Araba Norway Canada Algera Qatar Indonesa Australa Ngera Iran 211 Russa Saud Araba Qatar Norway Canada Indonesa Australa Ngera Algera Kazakhstan 212 Russa Saud Araba Norway Indonesa Australa Qatar Canada UAE Ngera Kazakhstan 213 Russa Saud Araba Qatar Norway Indonesa Australa Canada UAE Kazakhstan Netherlands

7 874 W. Zhong et al. / Appled Energy 165 (216) Table 6 Top 1 trade relatonshps n value of emergy n fossl fuel trade. Rank Exporter Importer Fossl emergy (sej) Rank Exporter Importer Fossl emergy (sej) Canada USA 1.9E+24 1 Canada USA 1.24E+24 2 Saud Araba USA 4.45E+23 2 Saud Araba USA 5.14E+23 3 Venezuela USA 4.43E+23 3 Venezuela USA 4.65E+23 4 Mexco USA 4.17E+23 4 Mexco USA 4.42E+23 5 UAE Japan 3.8E+23 5 UAE Japan 3.68E+23 6 Saud Araba Japan 3.45E+23 6 Saud Araba Japan 3.49E+23 7 Ngera USA 3.3E+23 7 Australa Japan 3.8E+23 8 Australa Japan 3.E+23 8 Norway UK 3.6E+23 9 Norway UK 2.95E+23 9 Ngera USA 2.8E+23 1 Norway Germany 2.69E+23 1 Norway Germany 2.78E Canada USA 1.19E+24 1 Canada USA 1.46E+24 2 Saud Araba USA 4.77E+23 2 Saud Araba USA 5.55E+23 3 Mexco USA 4.73E+23 3 Mexco USA 4.98E+23 4 Venezuela USA 4.55E+23 4 Venezuela USA 4.55E+23 5 UAE Japan 3.45E+23 5 UAE Japan 3.62E+23 6 Saud Araba Japan 3.39E+23 6 Norway UK 3.54E+23 7 Norway Germany 3.26E+23 7 Saud Araba Japan 3.53E+23 8 Australa Japan 3.6E+23 8 Russa Ukrane 3.25E+23 9 Norway UK 3.5E+23 9 Norway Germany 3.22E+23 1 Russa Ukrane 2.88E+23 1 Australa Japan 3.14E Canada USA 1.52E+24 1 Canada USA 1.94E+24 2 Venezuela USA 5.17E+23 2 UAE Japan 6.1E+23 3 Mexco USA 4.97E+23 3 Venezuela USA 5.12E+23 4 Saud Araba USA 4.84E+23 4 Mexco USA 4.88E+23 5 UAE Japan 3.77E+23 5 Saud Araba USA 4.64E+23 6 Norway UK 3.67E+23 6 Saud Araba Japan 4.14E+23 7 Saud Araba Japan 3.59E+23 7 Norway UK 3.97E+23 8 Russa Ukrane 3.54E+23 8 Netherlands Belgum 3.92E+23 9 Ngera USA 3.48E+23 9 Australa Japan 3.58E+23 1 Australa Japan 3.31E+23 1 Ngera USA 3.58E Canada USA 1.74E+24 1 Canada USA 1.86E+24 2 Mexco USA 5.3E+23 2 Algera Italy 5.67E+23 3 Venezuela USA 4.67E+23 3 Belgum France 5.28E+23 4 Saud Araba USA 4.52E+23 4 Mexco USA 4.65E+23 5 Saud Araba Japan 4.36E+23 5 Venezuela USA 4.61E+23 6 Norway UK 4.24E+23 6 Saud Araba USA 4.53E+23 7 Netherlands Belgum 4.16E+23 7 Norway UK 4.39E+23 8 Belgum France 3.88E+23 8 Netherlands Belgum 4.9E+23 9 UAE Japan 3.85E+23 9 Australa Japan 4.1E+23 1 Australa Japan 3.72E+23 1 Saud Araba Japan 3.79E Canada USA 2.55E+24 1 Canada USA 8.58E+23 2 Belgum France 5.7E+23 2 Norway UK 4.2E+23 3 Saud Araba USA 4.8E+23 3 Venezuela USA 3.94E+23 4 Norway UK 4.5E+23 4 Australa Japan 3.71E+23 5 Netherlands Belgum 4.25E+23 5 Saud Araba Japan 3.68E+23 6 Venezuela USA 4.22E+23 6 Mexco USA 3.42E+23 7 Australa Japan 4.1E+23 7 Russa Netherlands 3.39E+23 8 Mexco USA 3.87E+23 8 Russa Italy 3.29E+23 9 Saud Araba Japan 3.83E+23 9 Saud Araba USA 3.27E+23 1 UAE Japan 3.66E+23 1 UAE Japan 2.91E Canada USA 1.7E+24 1 Canada USA 1.15E+24 2 Algera Italy 4.81E+23 2 Mexco USA 4.63E+23 3 Mexco USA 4.55E+23 3 Norway UK 4.38E+23 4 Norway UK 4.55E+23 4 Australa Japan 3.94E+23 5 Australa Japan 4.19E+23 5 Saud Araba USA 3.83E+23 6 Russa Netherlands 3.84E+23 6 Saud Araba Japan 3.73E+23 7 Venezuela USA 3.58E+23 7 Venezuela USA 3.41E+23 8 Saud Araba Japan 3.54E+23 8 Qatar Japan 3.33E+23 9 Saud Araba USA 3.47E+23 9 Saud Araba Chna 3.23E+23 1 Ngera USA 3.23E+23 1 UAE Japan 3.9E Canada USA 6.91E+23 1 Canada USA 7.9E+23 2 Australa Japan 4.37E+23 2 Australa Japan 4.76E+23 3 Norway UK 4.32E+23 3 Mozambque South Afrca 4.28E+23 4 Saud Araba USA 4.27E+23 4 Hungary Croata 4.26E+23 5 Saud Araba Japan 3.82E+23 5 Qatar Japan 4.25E+23

8 W. Zhong et al. / Appled Energy 165 (216) Table 6 (contnued) Rank Exporter Importer Fossl emergy (sej) Rank Exporter Importer Fossl emergy (sej) 6 Saud Araba Chna 3.46E+23 6 Saud Araba USA 3.96E+23 7 Russa Netherlands 3.44E+23 7 Norway UK 3.77E+23 8 Mexco USA 3.7E+23 8 Saud Araba Japan 3.66E+23 9 UAE Japan 3.1E+23 9 Norway Germany 3.65E+23 1 Netherlands Belgum 2.84E+23 1 Saud Araba Chna 3.46E+23 Densty Fossl Crude ol Coal Natural gas 4. Dscusson and concluson In ths paper, we constructed the ntegrated trade network of fossl fuel based on emergy value, as well as the sngle commodty networks of coal, crude ol and natural gas. The trade quantty of coal, crude ol and natural gas were transformed nto emergy and the sum of them was the emergy of fossl fuel. We studed trade relatonshps, trade quantty, trade densty and herarchy structure of the networks. These ndexes reflected the ndvdual and entre features of the fossl fuel trade network. Our observaton perod was from 2 to 213, we looked further nto the evoluton of these features over tme. Our conclusons and dscussons are as follows: Year Fg. 6. Trade densty of the 4 types of networks. appear to have varous clusterng coeffcent. However, we can stll fnd some clues of herarchy structure when we observe the low degree and hgh degree separately. Take the year 213 as an example. The R 2 of lnear regresson of all the nodes was.649 (please see Fg. 9(a)), whch ndcates that there was no lnear relaton between degree and clusterng coeffcent. However, as we deleted the nodes wth low degree, the R 2 of lnear regresson s ncreasng. When there were nodes wth degree above 17, the R 2 of lnear regresson was.6174 (please see Fg. 9 (b)), whch ndcates that there was lnear relaton between degree and clusterng coeffcent. We recorded the R 2 when we deleted nodes wth degree from 1 to 5 n the year 2, 26 and 213 (please see Fg. 1). The R 2 reached.6 when there were nodes wth degree more than about 2. It was faster for R 2 to reach.6 from 2 to 213, whch means the herarchy structure was becomng more ordered. (1) The numbers of trade relatonshps of the sngle countres follow power law dstrbuton. A small number of countres own many trade partners and most of the countres own a few trade partners. The top 1 countres wth the largest number of mport or export relatonshps are manly n North Amerca, Europe and East Asa area. Countres wth hgh n-degree also tend to have hgh out-degree, for example the USA, Chna and Germany. The number of trade relatonshps can reflect a country s actveness n nternatonal trade. If a country s actve, although t s a net mportng country, t wll stll have many export flows wth small value of emergy to other countres especally to ts neghborng countres. Also, these countres tend to have many bg energy companes, whch not only target to domestc market, but also target to the world market. (2) A small number of trade lnks shoulder most of the trade quanttes. Less than 8% of the trade relatonshps contan up to 8% of the trade quantty of emergy n the fossl fuel trade network. The centralzaton of trade quantty of fossl fuel was becomng more ntense, however, natural gas had Fossl Coal Fossl Coal Crude ol Natural gas Crude ol Natural gas 25 3 Total number of nodes Total number of edges Year (a) Year (b) Fg. 7. Total number of countres and trade lnks of the 4 types of networks.

9 876 W. Zhong et al. / Appled Energy 165 (216) Fg. 8. Scatterplots of degree and clusterng coeffcent of fossl fuel trade network. 1.2 k>.8 k>17 Clusterng coeffcent y = -.2x R² =.649 Clusterng coeffcent y = -.29x R² = Degree (k) (a) Degree (k) (b) Fg. 9. The lnear regresson of degree and clusterng coeffcent n fossl fuel trade network n 213. R Degree Fg. 1. R 2 of the lnear regresson of degree and clusterng coeffcent n fossl fuel trade (the abscssa s the threshold of degree, countres wth degrees equal to or hgher than the threshold s reman n the regresson). an opposte tendency. The centralzaton of trade quantty of natural gas was less ntense. The ncreasng of producton, especally the development of unconventonal gas (such as shale gas n the USA), reshaped the supply pattern of natural gas and made t less centralzed to a small number of trade relatonshps. (3) The trade densty of fossl fuel s ncreasng, and coal s the cheapest energy measured by energy cost whch s beng most wdely traded. Due to the globalzaton of fossl fuel trade, more countres were partcpatng n the world fossl fuel trade, and more relatonshps among countres were bult up. In the tradtonal study of sngle fuel, we can only analyse the features of one part of the fossl fuel market, our study can reveal the tendency of the ntegrated market and t also easly to compare the features of dfferent fuels. In our results, an nterestng phenomenon s that although crude ol contrbutes the most to the total emergy of fossl

10 W. Zhong et al. / Appled Energy 165 (216) fuel trade, the number of countres and the number of relatonshps of crude ol s not the largest. Coal contrbutes the least emergy to the total, however, t has the largest number of countres and relatonshps. Ths phenomenon ndcates that when consderng the energy cost of the geobosphere and the producng process, coal s the cheapest energy that s beng traded most wdely. Ths concluson s based on the concept of emergy and cannot obtaned by the tradtonal study based on money or exergy. When we look further nto the calculaton of emergy values of the three types of fuels, we can see that the energy content of coal s not that small, however, when transformed nto emergy, ts emergy value s much less than crude ol and natural gas. Ths means that coal consumed less energy n the geobosphere process and the producng process. Ths s the reason for the small contrbuton of coal n the total emergy of fossl fuel. Another pont s that the energy cost n the trade quantty should be consdered when makng nternatonal trade polces. Emergy flows contan the energy cost n the producng process of the exportng countres. At the same tme, the money of a country also contans emergy n t. Both of the energy costs n fossl fuel and money can reflect the energy cost of ths country. Thus when two countres exchange fossl fuel and money n the nternatonal trade, they should look further nto the energy cost of them and reconsder the effectveness of the trade. (4) The evoluton tendency of the trade densty of natural gas s denser, and accordng to the accumulatve dstrbuton of trade quantty the trade of natural gas s becomng more dversfed. Ths mplcates that more trade relatonshps of natural gas wll be bult and the trade volume wll not be concentrated n a few trade lnks. Thus, for exportng countres t s an opportunty to extend ther sales markets and enhance ther status. For mportng countres, t s also an opportunty to seek for more mportng sources and ncrease the mportng volume of the exstng trade lnks. More ppelnes are needed to be bult, and the progress of the technology of lquefacton and regasfcaton wll extend the trade scale of LNG. (5) Countres wth more than 2 trade relatonshps tend to have a herarchy structure, whch means countres wth smlar roles tend to have smlar connectvty. Ths phenomenon was becomng more pronounced durng the observaton perod. Another nterestng fndng s that countres wth more trade relatonshps tend to have lower connectvty among ts neghborng countres. Ths s because countres wth hgh degree have wder trade ranges. Ther trade partners are located all over the world, thus the probablty of buldng up trade relatonshps between neghborng countres s lower. As the herarchy structure of the nternatonal fossl fuel trade network s becomng more ordered, the statuses of the countres are clearer. It s easer for polcy makers to dentfy the roles of ther own countres or the roles of other countres. The mpact of a country s spreadng faster when ths country has better connectvty. (6) Our results can also reveal the trade strategy of the countres. For example, n the early years, USA and Japan were the man exportng target of Saud Araba. In recent years, the emergy amounts of Saud Araba s trade flow to Chna were ncreasng fast due to the boomng of Chnese economy and Chna s rocketng demand for fossl fuel. Another example s that although Indonesa was one of the top 1 countres n exportng fossl fuel, t was not on the lst of top 1 relatonshps. Mexco was one of the top 1 countres only n 21 23, however, t was on the lst of top 1 relatonshps n most of the years. Ths s because Indonesa equally exported ts fossl fuel to Southeast Asan countres such as Inda, Chna, Japan and South Korea. Although the value of the sngle emergy flow was not hgh, the total amount of ts export was large. 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