基于复杂网络理论的原油贸易空间格局差异分析

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关于2002 - 2013年国际石油贸易相互关联和演化特征的复杂网络观点

A complex network perspective on interrelations and evolution features of international oil trade,2002–2013qRuijin Du a ,⇑,Ya Wang a ,Gaogao Dong a ,⇑,Lixin Tian a ,b ,Yixiao Liu a ,Minggang Wang b ,Guochang Fang caCenter of Energy Development and Environmental Protection,Jiangsu University,Zhenjiang 212013,China bSchool of Mathematical Sciences,Nanjing Normal University,Nanjing 210046,China cSchool of Economics,Nanjing University of Finance and Economics,Nanjing 210023,Chinah i g h l i g h t sThe top 1network model can well reﬂect countries’preference in choosing oil trade partners.The modiﬁed closed-system input-output method can reﬂect the direct and indirect inﬂuences among oil trading countries. The interrelation and evolution features of international oil trade network have been discussed. The result shows us how to make a rational decision for a country to develop oil trade relations.a r t i c l e i n f o Article history:Received 1October 2016Received in revised form 29November 2016Accepted 8December 2016Available online 20December 2016Keywords:International crude oil trade Complex network Top networkModiﬁed closed-system input-output analysisInterrelationsEvolution featuresa b s t r a c tIn this paper,a directed and weighted world crude oil trade network is constructed.Based on the built network,we apply top network method and modiﬁed closed-system input-output method to assess the relative importance of countries in the international oil trade.The top 1network consisting of top trade relations,can well reﬂect countries’preference in choosing oil trade partners.As a simpliﬁed net-work model of international oil trade network,the top 1network has been analyzed the structure and evolution.In order to identify important oil trading countries,the modiﬁed closed-system input-output analysis has yielded some promising results,which show countries who play key role in interna-tional oil trade,are trading large oil volumes.In addition,by describing the inﬂuences of countries on each other,we ﬁnd major oil importers have a much effect on other countries,including major oil expor-ters.The evolution analysis obtained by these two methods coincide with each other.To conclude,some suggestions are given according to the results.Ó2016Elsevier Ltd.All rights reserved.1.IntroductionCrude oil is still the principal fuel source in the world,it accounts for nearly 32:9%of global energy consumption in 2015.As a national strategic resource,crude oil plays a pivotal role in economic development and national security.Due to the unbal-ance distribution of oil production and consumption,international oil trade gives a big push for the cross-border oil ﬂows,which bind the whole world into a global oil trade network.A detailed understanding of oil trading-based network is meaningful forgovernments because they are eager to understand the global oil trade in order to avoid oil supply risk.Actually,oil trade ﬂows reﬂect the relationships among coun-tries,which form a network where the countries are taken as the nodes and the trade relationships as the edges.Thus,the develop-ment of ‘‘the new science of networks”[1,2]has offered an effec-tive tool when analyzing the trading patterns.The complex network approach has been proved fruitful and shed new light on international oil trade [3–7].A directed oil trading-based net-work model was established by An et al.to study the relationship between countries with common trade partners [3].Wang et al.employed a complex network approach to research the interaction patterns among the crude oil import dependency countries in the global oil trade network [4].Zhong et al.studied the evolution of communities of the world oil trade network by setting up un-weighted and weighted oil trade network models using data from 2002to 2011,and analyzed their evolutionary features and/10.1016/j.apenergy.2016.12.0420306-2619/Ó2016Elsevier Ltd.All rights reserved.qThe short version of the paper was presented at CUE2016on June 13–15,Jinan,China.This paper is a substantial extension of the short version of the conference paper.⇑Corresponding authors.E-mail addresses:dudo999@ (R.Du),gago999@ (G.Dong).Applied Energy 196(2017)142–151Contents lists available at ScienceDirectApplied Energyj o ur na l h o me pa ge :w w w.e ls e v ie r.c o m/lo c a t e/ap en e rgystabilities over the time[5].Zhang et al.studied the competition among oil importers using complex network theory,combined with several alternative measures of competition intensity,to ana-lyze the evolution of the pattern and transmission of oil-trading competition[6].Du et al.studied the overall topological structure properties of global oil trade network,and by applying random matrix theory,explored the complex spatiotemporal dynamic from the country level andﬁtness evolution of the global oil market from a demand-side analysis[7].Existing network studies have greatly contributed to our under-standing of international oil trade.However some shortages are still existed.First,in the above global oil trade network,a country’s trade partners are treated equally.However,not all the trading partners are equally important to a country.A country is concen-trated in its trade with a few partners.This concentration is espe-cially notable for developing countries,as most peripheral countries’foreign trade is heavily dependent on particular core countries,according to world system and dependency theories [8–10].Thus it is necessary to distinguish a country’s top trade partners from nonessential ones and to study the speciﬁc trade network based on these top trade relations.By introducing the def-inition of‘export intensity’,Ji et al.constructed a global oil trade core network,which is a simpliﬁed model of the whole interna-tional oil trade,and analyzed the overall features,regional charac-teristics and stability of the oil trade by using complex network theory[11].Second,the question of the relative importance of oil trading countries and inﬂuences between them has rarely been studied. Even if there existed study of identifying the centrality of nodes, they were often limited to some traditional indicators,such as degree,strength,betweenness,etc.[12–14].In fact,the actual international oil trade network is both directed and weighted sys-tem,and trade relationship between two countries are presented as direct or indirect connections.In the former study,the indirect relation of oil exporters and oil importers has been neglected.For example,if there is an oil importer C,which heavily relies on oil exporter B,which in turn imports oil from another country A,then it is clear that even if there are no direct connections from A to C,A is a major contributor to C.Therefore,it would be signiﬁcant to have a means of reﬂecting trade direction,trade volume,direct as well as indirect trade relations.In light of these gaps,we speciﬁcally construct a top1import network and a top1export network based on top trade relations. The simpliﬁed network model can describe countries’preference in choosing trade partners well,and also reﬂect the positions of oil trading countries and evolution features well.We further develop a modiﬁed closed-system input-output analysis method, which not only contains the direction and the intensity of oil trade relations,but also considers the direct and indirect inﬂu-ences among oil trading countries,to discuss the relative impor-tance of these countries and how this evolves.The results of the above two methods are compared.In this paper,Section2intro-duces the model and methodology.Section3is empirical analysis for global oil trade network.Section4is conclusions and suggestions.2.Model and methodology2.1.The global oil trade network modelingWe construct a model of directed and weighted global oil trade network,whose nodes are the nations,and edges are the oil trade relationships between nations.The oilﬂow out of and into a coun-try can be presented by an edge with direction.The weight of the edge is measured by the trading volumes.Since the international oil trade includes import and export ﬂows,the network can be divided into oil import network,in which the in-degree and in-strength of nodes are only considered, and oil export network,in which the out-degree and out-strength of nodes are only considered.The in-degree k in of a node(country) measures the number of countries,which export oil to the country. The out-degree k out measures the number of countries,which import oil from the country.The in-strength s in of a country means its total imports,and the out-strength s out is its total exports.Fig.1 is an example of the directed and weighted network.Fig.1is an example of the directed and weighted network.Sup-pose there are crude oil trade relationships among four countries named A,B,C and D.Take node B for example,if B imports5tons of crude oil from A,the weight of the directed edge,which repre-sents the oilﬂow from A to B,is5.Thus,for B,k in¼1;s in¼5.At the same time,B exports2tons of crude oil to D,and exports1 ton crude oil to C.The weights of the two directed links connecting B to D,and B to C,are2and1separately.Thus,for B,we have k out¼2;s out¼3.2.2.Top network analysis methodBased on the above international oil trade network,we further build a simpliﬁed network to capture most important relations in the oil trade by using the core idea of Ref.[10].The speciﬁc simpli-ﬁed network is on the base of top oil trading relations,that is,if country j is country i’s top trade partner,country i is linked to country j;otherwise,there is no tie between i and j.Particularly, we consider oil import network.If country i imports oil from j and other countries,we rank the import trade relations of i with other countries by importing volumes.Thus,the top1oil import network is constructed by including each country’s topmost import trade relation only.In the same way,by ranking the export trade relations of each country with other countries by exporting volumes,the top1oil export network is built by including each country’s topmost export trade relation only.If each country’s top two importing or exporting trade relations are kept,the resul-tant network is called top2import or export network.Further,we can go down the ranking of trade relations and obtain the top net-works of the selected standard.One key characteristic of the top import/export network is that all nodes have an in-degree/out-degree not exceeding the selected standard,but the out-degree/ in-degree varies acrossnodes.R.Du et al./Applied Energy196(2017)142–1511432.3.Modiﬁed closed-system input-output analysis methodThe input-output theory was ﬁrst proposed in 1936by Ameri-can economist Leontief,and answered two typical questions:ﬁrst,what happens if the ﬁnal demand increases?Second,which eco-nomic sector is the most important for the whole economy [15]?Tang et al.established an input-output model to calculate oil embodied in the international trade of China [16].Shen et al.revealed the interrelations among scientiﬁc ﬁelds and their relative inﬂuences by an input-output analysis,in which the element x i j of input-output matrices reﬂected a citation received by paper in ﬁeld i from paper in ﬁeld j [17].Because of including both direct and indirect connections,the relationships among countries can be revealed deeper with the input-output analysis method,than with the traditional measure of complex network by calculating degrees and strengths.Assume the global oil trade network has N countries.Denote the weighted adjacent matrix as W ,which is also called the input-output matrix,whose element x ij represents the volume of oil ﬂow-ing from country i to country j .Let A be the direct input-output coefﬁcients matrix,the element of which isa ij ¼x ijx j;i ;j ¼1;2;...;N ;ð1Þwhere x j ¼Pk x jk .With these elements a ij ,we obtainx i ¼XjA ij x j )X ¼AX :ð2ÞEq.(2)means that X is an eigenvector of matrix A corresponding to the eigenvalue 1,the largest eigenvalue of matrix A .For simplicity,the eigenvector corresponding to the largest eigenvalue is called the largest eigenvector.A ðÀj Þis what remains of matrix A after its j th row and j th column are removed.Denote the largest eigenvalue of matrix A ðÀj Þby k ðÀj Þ.If country j has hardly any trading connections to other countries,i.e.,the values in the j th row and/or column are very small compared with other elements of A ;k ðÀj Þis very close to 1.Otherwise,if country j relates closely to other countries,k ðÀj Þis much smaller than 1.Therefore,the input-output factor on the input side (In-IOF)is given [17]S j IO ¼1Àk ðÀj Þð3Þto calculate the relative importance of country j .The practical inter-pretation of the largest eigenvector is:the vector X can be regarded as the speciﬁc combination of oil from different countries,that results in well operating of oil trading system.The k th element of X ,denoted as h k j X i ,can be interpreted as the supplying of the kth144R.Du et al./Applied Energy 196(2017)142–151country to the global oil trade network.h k j k ðÀj Þi ,the k th element of the largest eigenvector of matrix A ðÀj Þ,can be understood as the con-tribution of country k for the oil trade system without country j .Thus,to reﬂect the inﬂuence of country j to country k ,we pro-pose the input-output inﬂuence on the input side (In-IOI)deﬁnedbyR.Du et al./Applied Energy 196(2017)142–151145D j k ¼1Àh k j kðÀjÞih k j X i;ð4ÞObviously,for a given country j;D jkdescribes if j is removed from the global oil trading network,how much the oil imports/exports ofcountry k changes,directly and indirectly.When D jk(0,countryk relies strongly on country j,and when D jk)0,country k can be regarded as a competitor of country j.The above analysis are applicable to the import side.For the same reason,when we transpose the input-output matrices ðx ijÞNÂN,by using the above two equations,we can get the input-output factor(Out-IOF)and input-output inﬂuence(Out-IOI)on the export side.The modiﬁed closed-system input-output analysis method can show the characteristics of the real oil trade, since it is based on the weighted adjacent matrix of global oil trade network,which not only mirrors the direction of oilﬂow,but also reﬂects the quantity of oil trade.3.Empirical analysis for global oil trade network3.1.DataWe employ bilateral tradeﬂows data from the United Nations Commodity Trade Database(UN Comtrade;http://comtrade.un. org).We build a balanced panel of N¼183countries and regions for which we have commodity-speciﬁc imports and exportsﬂows from2002to2013(T¼12years).The used code is HS270900: crude petroleum oils.3.2.Top1oil trade network:revealing the structure and evolutionThe top oil trade networks extract the most important relation-ships from the global oil trade network.Fig.2shows the trade vol-ume percentages of various import and export top networks in the overall international oil trade.According to Fig.2(a)and(b),oil ﬂows in the top1import and export networks make up more than 30%of total imports and exports,respectively.The top2,top6and top10networks account for approximately50%,80%and90%of total global oil trade.These percentages have almost been stable over time.This illustrates top trade relations can indeed reﬂect the global oil trade network well.Fig.2(c)and(d)further shows the relationship between top networks and their percentages in total oil trade in the years2002,2008,2010and2013.All curves start above30%,then rise up and approach100%as more trade relations are brought into the top networks.However,the trend of the percentages appears a convex increasing parabolic curve, thus the growth rate is decreasing.Since top1relations are included by all types of top networks,and the top1network alone accounts for more than30%of the overall oil trade,in this study we analyze the structure and evolution of top1import and export networks.Fig.3visualizes the top1oil import and export networks in 2013.In the top1oil import network,each major oil exporter,Rus-sia,Saudi Arabia,United Arab Emirates,United States of America, etc.,leads a large cluster.Although the total exports of United States of America are not much,but the number of countries USA exporting oil to are relative larger,and many of these countries have USA as their largest import source.Russia,Saudi Arabia,Uni-ted Arab Emirates are countries with larger exports,and are the top sources of many oil importers.In the top1oil export network,each major oil importer,China,United States of America,India,Japan, etc.leads a large cluster.Besides the big clusters,there also exist some small clusters surrounding around some local centers.In order to better understand the hierarchical structure of the top1oil trade networks,we apply the degrees to reﬂect the status of countries.In the top1oil import network,all countries have in-degree1or0,but have different out-degrees.The out-degree can determine the status of countries in the network.Similarly,in the top1oil export network,all countries have out-degree1or 0,but different in-degrees,which can reﬂect the position of coun-tries in the network.Table1shows the top10countries in the top 1oil import network between2002and2013.From Table1,the ten countries can be roughly divided into three levels.The central Status of Russia and Saudi Arabia have been stable over time.They can be regarded as theﬁrst level.The second level consists of four members:United Arab Emirates,Nigeria,South Africa and United States of America.The average out-degree of each member isTable1Out-degree for major countries in top1oil import network,2002–2013.Country200220032004200520062007200820092010201120122013Russia202023252023222222232221 Saudi Arabia121312131613151513141112 United Arab Emirates559571011971289 Nigeria866565687564 South Africa765767786364 USA534437634547 Norway655563333233 Kazakhstan314233444465 Libya333334332021 Iran233343214310Table2In-degree for major countries in top1oil export network,2002–2013.Country200220032004200520062007200820092010201120122013USA17141615161614141416127 China15548866781013 Japan976566555455 Germany334356655574 Australia455544554453 Italy655444533463 India000022483796 Korea713122344433 Sweden452531222431 Canada104572511201 146R.Du et al./Applied Energy196(2017)142–151R.Du et al./Applied Energy196(2017)142–151147between5and8,and their out-degrees are all above3in the whole time period.Positions of Norway and Iran decrease gradually.It means many countries who before had the two countries as their top oil import sources have shifted to other countries.In contrast, the position of United Arab Emirates increases obviously.The third level includes four members:Norway,Kazakhstan,Libya and Iran. The average out-degree of each member is between2and4.Table2shows the in-degree for major countries in the top1oil export network from2002to2013.The10countries can be roughly divided into four levels.Theﬁrst level only has United States of America,whose average in-degree is above14.This illus-trates the United States of America is the biggest oil export market for many countries.The second level consists of two members: China and Japan.The average in-degree of each member is between148R.Du et al./Applied Energy196(2017)142–1515and 7.The third level includes ﬁve members:Germany,Australia,Italy,India and Korea.The average in-degree of each member is between 3and 5.The fourth level contains countries as Sweden and Canada.The central position of the United States of America has remained stable during the 12years.The status of Japan has shown a gradual decline over the 12years.This means many coun-tries who before had Japan as their export market have shifted to other countries.On the contrary,the trends of China and India’s positions have been on the rise year by year.It shows an increasing diversity of their import sources.3.3.Modiﬁed closed-system input-output analysis:revealing the interrelation and evolutionWe construct the input-output network (matrices)from 2002to 2013by extracting 46major oil trading countries (the crude oil trading volume of each country is above 100million tons in 2013).They are Algeria,Angola,Australia,Azerbaijan,Belarus,Bel-gium,Brazil,Canada,China,Colombia,Congo,Ecuador,Finland,France,Germany,Greece,India,Indonesia,Iran,Iraq,Italy,Japan,Kazakhstan,Korea,Kuwait,Libya,Malaysia,Mexico,Netherlands,Nigeria,Norway,Oman,Poland,Portugal,Qatar,Russia,Singapore,South Africa,Spain,Sweden,Chinese Taipei,Thailand,UnitedKingdom,United States of America,Saudi Arabia,United Arab Emi-rates.The closed-system input-output method is applied to ana-lyze the relative importance of and inﬂuences among these major oil trading countries.First,we examine the correlation of the relative importance,as measured by IOF,degree and strength.Fig.5(a)shows the in-degree and in-strength rankings of USA,India,Netherlands,etc.are the same.There also exist countries of which the two rankings are different obviously.For example,the in-degree rankings of Sin-gapore and Thailand are in the top,while the in-strength rankings are a little lower.Their imports are nearly 0.12billion tons less than that of Japan,but their importing countries are more than Japan’s.This effectively spreads the imports risk,and reduces the dependency on exporters.Fig.5(b)shows the correlation of in-IOF ranking with in-degree ranking is very similar with Fig.5(a).We examine the correlation between in-IOF and in-strength.The countries are almost on the diagonal line in Fig.5(c).From Fig.5(d),we ﬁnd countries like Libya,Angola,Algeria,etc.,whose exports are about 0.3billion tons less than Saudi Arabia,but the out-degrees of which are very close to that of Saudi Arabia.It means that Libya,Algeria,etc.have a wider export market.Fig.5(e)shows the correlation of out-IOF ranking and out-degree rank-ing is very similar with Fig.5(d).From Fig.5(f),we ﬁnd theout-IOFFig.8.A comparison of different methods to measure the countries who play more important role in global oil trade.R.Du et al./Applied Energy 196(2017)142–151149and out-strength rankings of top ten countries are almost the same.Almost all the countries are distributed around the diagonal line.Besides,we perform comparisons of degree,strength and IOF rankings on the input and output sides in2002and2008 separately,as shown in Fig.6.These all illustrate that countries with large inﬂuences,whatever on the input side or on the output side,are with large strengths,that are large importers or exporters.Fig.7shows the evolution of In-IOF and Out-IOF.In Fig.7(a),we ﬁnd the United States of America played the most important role on the import side over a period of12years,from2002to2013. It’s worth mentioning that China and India played increasingly important roles in international oil trade.However,the importance of Japan was decreasing gradually.In Fig.7(b),Saudi Arabia and Russia lied in essential positions on the export side during the whole sample period.The importances of United Arab Emirates and Iraq have strengthened from2002through2013.But Norway and Iran became continually less important.This result coincides with that of Top network analysis.Besides comparing the evolution trend,the following Fig.8per-forms a comparison about the relative importance of countries in 2013based on the above metrics methods.The result shows that the measurement results obtained by the three indicators: strength,IOF and degree of top network,are very close,especially by strength and IOF.Pursuing stable oil supply has been a great concern for both pol-icy makers of most industrialized nations and scholars especially since the break of two global oil crises of the1970s.It is generally known that oil-exporting countries have played an essential role in international oil-trading network.OPEC(Organization of the Petro-leum Exporting Countries)has played a dominant role in the globalinﬂuence on the input side in2002,2008and2013. 150R.Du et al./Applied Energy196(2017)142–151oil sector by coordinating and unifying petroleum policies among its member countries.As of2015,the14member countries accounted for an estimated43%of global oil production and73% of the world’s‘‘proven”oil reserves.Sudden change in oil-exporting countries will causeﬂuctuation of oil supply.Much work has been done on the supply side[18–21].In Fig.9,we report inﬂu-ences among major oil importers and exporters by using a heat-map,in which the color corresponds to the IOI from the row to the column countries.From Fig.9,weﬁnd that the major impor-ters,such as United States of America(USA),China(CHN),Japan (JPN),United Kingdom(UK),Singapore(SIN),Netherlands(NED), Korea(KOR),Germany(GER),France(FRA),Spain(ESP),and Aus-tralia(AUS),etc.have great inﬂuence on other countries,including major oil exporters.Therefore,while special attention is paid to the oil supply side,the demand side of the international oil trade should also be concerned with by policy makers and scholars.4.Conclusions and suggestionsIn this paper,we tried to answer the question:how important is the country in the international crude oil trade?The global crude oil trade network model is built from crude petroleum data from2002 to2013.We applied the modiﬁed closed-system input-output analysis method and top network analysis method to reveal the structural features and evolution of the global oil trade system.Not all relations in the international oil trade network are of the same importance.Although the top1network is a simpliﬁed model, which only consists of countries’topmost trade relations,but it can well reﬂect countries’preference in choosing oil trade partners.By the structural analysis of top1oil import or export network,weﬁnd the whole network is separated by clusters,most of the countries in which are surrounding around global or local centers,that is,major oil exporters,such as Russia,Saudi Arabia,United Arab Emirates, etc.,or major oil importers,such as China,United States of America, India and Japan,etc.These global and local centers are regarded as the main oil importing sources or exporting destinations.Further, the evolution features of major countries in top networks are ana-lyzed.The result offers a way to make a rational decision for a coun-try to develop oil trade relations.The modiﬁed closed-system input-output analysis method has considered both direct and indirect effects in measuring which country contributes more in the global oil trade and thus may be traded preferentially.Weﬁnd that a country with greater inﬂu-ence,is also with larger oil trading volume.The evolution of IOF analysis follows the same trend with that of top network analysis. Moreover,the heatmap of IOI describes the inﬂuences of countries on each other.Particularly,weﬁnd major oil importing countries have great inﬂuence upon not only the marginal countries,but also major oil exporting countries.Therefore,when people are paying continual attentions to the issues related to major oil exporting countries,they also should concern the series of problems brought by oil importing countries.In addition,the above two methods are applicable to more energy systems that have input-output relations among their ele-ments.When combined with time-series data,the methods can also be adopted to track the evolution of the elements.AcknowledgementsThe authors thank Prof.Jinshan Wu and Dr.Zhesi Shen from School of Systems Science,Beijing Normal University for their valuable discussions.This work is supported by National Natural Science Foundation of China(Grant Nos.61403171,71403105, 71303095,71303205,71503132,71673116),Major Research plan of the National Natural Science Foundation of China(No. 91546118),Natural Science Foundation of Jiangsu Province(Grant Nos.BK20140569,14KJB120001),Jiangsu Postdoctoral Science Foundation(Grant Nos.1402077B,1501100B),China Postdoctoral Science Foundation(Grant Nos.2015M581738,2015M80519) and Senior talents Foundation of Jiangsu University(Grant Nos. 14JDG143,14JDG144).References[1]Barabási AL.Linked:the new science of networks.Cambridge,MA:PerseusPublishing;2002.doi:/10.1119/1.1538577.[2]Watts DJ.The‘new’science of networks.Annu Rev Sociol2004;30:243–70.doi:/10.1146/annurev.soc.30.020404.104342.[3]An HZ,Zhong WQ,Chen YR,Li HJ,Gao XY.Features and evolution ofinternational crude oil trade relationships:a trading-based network analysis.Energy2014;74:254–9.doi:/10.1016/j.energy.2014.06.095.[4]Wang MG,Tian LX,Du RJ.Research on the interaction patterns among theglobal crude oil import dependency countries:a complex network approach.Appl Energy2016;180:779–91.doi:/10.1016/j.apenergy.2016.08.026.[5]Zhong WQ,An HZ,Gao XY,Sun XQ.The evolution of communities in theinternational oil trade network.Physica A2014;413:42–52.doi:http://dx.doi.org/10.1016/j.physa.2014.06.055.[6]Zhang HY,Ji Q,Fan petition,transmission and pattern evolution:anetwork analysis of global oil trade.Energy Policy2014;73:312–22.doi: /10.1016/j.enpol.2014.06.020.[7]Du RJ,Dong GG,Tian LX,Wang MG,Fang GC,Shao S.Spatiotemporal dynamicsandﬁtness analysis of global oil market:based on complex network.PloS ONE 2016;11:e0162362.doi:/10.1371/journal.pone.0162362. 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基于复杂网络视角全球贸易网络演化特征分析

基于复杂网络视角全球贸易网络演化特征分析作者：方靖仪来源：《经济研究导刊》2019年第01期摘要：世界各国经济水平大有不同以及地理位置导致各国资源分布不均，使得国家之间存在着复杂的贸易关系。

将各国之间的贸易关系以复杂网络的形式表现出来，在此基础上分析研究1948—2000年间贸易网络的具体特征和网络的演化规律。

同时，通过用不同的高阶组织来揭示不同的聚类，从而可以展现世界贸易网中不同的贸易模式。

关键词：复杂网络;世界贸易;网络模体;聚类分析中图分类号：F740; ; ; ; 文献标志码：A; ; ; 文章编号：1673-291X（2019）01-0159-03引言经济全球化让世界各国在经济上相互联系与依存、相互竞争与制约、相互渗透，无论哪个国家都难以离开其他国家的影响而独立地运转，全球经济逐渐形成一个互相依赖、不可分割的整体。

经济学家很早以前就将国际贸易作为一个网络来研究，Higerdt利用被社会学家Jacob L.Moreno称为“社群图”的图来表示第二次世界大战前国际贸易情况[1～2] 。

Serrano研究发现，国际贸易网络具有无标度和小世界的特征[3]。

这些研究引起了国际经济学家的强烈兴趣，并产生了大量关于国际贸易网络的研究。

后来的研究不再简单地用0或1来表示贸易存在与否，因为这样做忽略了国际贸易网络中重要贸易流量特征及各条链接的异质性，毕竟各个经济体之间的贸易量大小对世界贸易网的结构特征性不可能没有影响。

而权重就是在网络中给节点之间的链接赋予一个数值，它反映了两点之间关系的强弱程度。

国际经济学家Garlaschelli D and Loffredo M的研究集中利用国家之间的贸易数据构建世界贸易网络，描述其网络统计特征及其随时间的演化[4～7]。

从时间维度来考察世界贸易网络，这种研究可以分析那些网络因素或属性发生了变化。

还有关于国际贸易网络分析法的具体应用方面，例如：Reyes和Faigolo通过测算拉美国家和东亚国家在WTN中的随机游走中心度，来说明二者在WTN中的地位重要性的变化，试图对两个经济体不同的经济发展表现给出解释[8];Li Xiang收集了IMF的数据，针对在2000年互相间的贸易额在100万美元以上的国家间的贸易关系建立国际贸易网络，讨论了国际贸易网络的无标度属性与经济周期同步性的相关关系[9]。

基于复杂网络的全球集成电路贸易结构演化分析

基于复杂网络的全球集成电路贸易结构演化分析
何小燕;杨朝静;邵颖丽
【期刊名称】《内蒙古财经大学学报》
【年(卷),期】2024(22)2
【摘要】集成电路是电子信息技术产业的核心部分,也是当前国际科技和经济博弈的焦点。

近年来,中国在全球集成电路产业贸易网络的地位不断提升,但是也受到一些国家的出口管制。

为了分析当前全球集成电路贸易的基本格局和结构演变以及中国在其中的地位变化,本文基于2002年、2012年和2022年全球集成电路进出口贸易的数据,利用复杂网络分析方法构建了全球集成电路贸易的关联演化网络,并对网络的空间结构演化机制和国际贸易位置变动情况进行了分析。

研究发现:全球集成电路贸易呈现出复杂网络的特征;全球集成电路贸易“东升西降”格局特征显著;部分发展中国家有较高的贸易额,但其网络位置却未显著提高;全球集成电路贸易网络的社区数量随着时间的推移有显著变化。

最后给出了中国集成电路产业的发展启示。

【总页数】8页(P59-66)
【作者】何小燕;杨朝静;邵颖丽
【作者单位】内蒙古财经大学统计与数学学院
【正文语种】中文
【中图分类】F752.65
【相关文献】
1.基于复杂网络视角全球贸易网络演化特征分析
2.基于复杂网络的全球铅矿贸易格局演化特征分析
3.基于复杂网络的全球镍贸易网络结构及演化趋势
4.全球集成电路贸易网络的空间结构演化与关系升级
5.基于复杂网络的全球稻米贸易格局演化及其启示
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我国原油进口贸易空间格局演进影响因素与趋势分析

我国原油进口贸易空间格局演进影响因素与趋势分析作者：郑云芳来源：《中国化工贸易·下旬刊》2018年第08期摘要：运用定性分析影响我国原油贸易格局演进的因素，分析中国原油贸易格局形成，及其得以强化、延续及变迁的进程中，原油价格、资源察赋、运输途径、运输环境与国家政治等的空间作用，探讨各个影响要素是怎样作用于我国原油进口贸易空间格局的演变的，为我国原油进口风险规避提供基础。

关键词：原油进口；空间格局；影响因素；原油贸易我国的原油进口空间格局己经基本形成，虽然当前格局在很长一段时间是稳定的，但是还存在着很多问题。

开展我国原油进口贸易空间格局演进影响因素与趋势分析，对保障国家能源安全有比较重要的现实意义。

1 我国原油进口贸易空间格局演进1.1 中东和亚太地区占优势的1996-1999年这一时期主要从亚太和中东进口原油为主，从中东和亚太进口的原油的比重一般都超过了80%有些年份甚至更高。

虽然两者在对华出口中都占据了重要的位置，但是竞争关系表现得十分突出。

从出口国来看，这一时期内以阿曼、卡塔尔为代表的中东产油国和以印度尼西亚、文莱为代表的亚太产油国均为中国重要的原油来源国。

这一时期另外一个鲜明的特点就是在亚太地区经济腾飞的大背景下，各个亚太国家对原油的需求持续上涨，对中国原油的出口将会受到极大的影响。

在金融危机和经济增长的双重作用下在这一时期内，亚太地区对华原油的出口一年比一年更低，而且经常出现负增长。

而中东地区不但原油储量多，而且由于受到人口、社会经济水平的制约，对原油的消费不大，导致了中东地区一直是我国原油进口的首选地域。

1.2 中东主要地位强化、非洲比重不断上升的2000-2005年由于这一时期初期中东原油价格的飘升，所以中国对中东地区原油的进口没有多大增加。

中东对华原油出口在后期一直保持着稳定的主导地位。

在2000年，非洲对华出口原油量首次超过了亚太，为中国第二大原油进口地域。

非洲的原油生产在这一时期得到了长足的进步，而且由于非洲地区的经济水平较为低下生产结构比较粗放，非洲大部分国家对原油的消费并不多。

世界原油贸易格局分析

世界原油贸易格局分析1 世界原油贸易新格局和趋势分析1.1 世界原油贸易格局2017年，原油国际贸易持续上涨，贸易总量达到21.84×108t，相比2016年增长了6800×104t。

在储量方面，新增储量达到新低。

2017年全球已探明原油储量为2308×108t，相比2016年减少了6530×104t，为2000年以来首次下降。

根据挪威独立咨询机构Rystad Energy发布的数据，新发现原油储量仅为9.12×108t，为70年来最低水平。

从2017年BP发布的数据来看，全球主要的储量分布在中东、中南美洲和北美地区，三地合计占到全球总储量的80%（见图1）。

中东原油储量最多，已探明储量达到1099×108t，远远高于其他地区，占全球总储量的48%；中南美洲原油储量为449×108t，仅次于中东地区，占全球总储量的20%；北美地区储量达到308×108t，占全球总储量的13%；欧洲及欧亚大陆、非洲和亚太地区分别占9%、7%、3%。

图1 2017年全球原油储量分布占比数据来源：BP世界能源统计年鉴在供应方面，中东产量全球第一，北美地区的产量持续增加。

201 7年全球原油产量约为43.87×108t，从产量分布看，中东地区原油产量最高，达到14.81×108t，占比33.76%；北美地区产量达到9.2×1 08t，占比20.9%；欧洲及欧亚大陆达到8.6×108t，占比19.65%；非洲、中南美洲及亚太分别占比8.74%、8.39%、8.56%（见图2）。

尽管中东地区的原油产量远高于其他地区，但近年来产量增幅有限，从20 07年的12.1×108t增加到2017年的14.8×108t，年均增速仅约为2. 03%；而同期北美地区则因为页岩油的开采，产量从6.3×108t增长到9.2×108t，年均增速达到3.86%；其余地区近年来产量并没有增长，反而呈现出略微下降的趋势。

复杂网络的国际贸易模型构建与演化分析

复杂网络的国际贸易模型构建与演化分析随着全球化的进程不断加速，国际贸易在世界经济中的地位日益重要。

然而，国际贸易涉及许多因素，如国际合作、市场竞争和政策制定等，其模型构建和演化分析是一个具有挑战的研究领域。

为了更好地理解和预测国际贸易的模式和趋势，研究者开始利用复杂网络理论来构建和分析国际贸易的模型。

在复杂网络的国际贸易模型构建中，首先需要建立一个合适的网络结构来表示各国之间的贸易关系。

常见的网络结构包括全局网络和局部网络。

全局网络可以将所有国家都连接起来，而局部网络则根据贸易关系的密切程度将国家进行分组。

通过对国际贸易数据的分析，可以确定网络的节点和边的属性，进而建立贸易网络模型。

在这个模型中，节点代表国家，边代表国家之间的贸易关系。

国家节点的属性可以包括经济规模、地理位置、产业结构等。

而贸易边的属性可以包括贸易规模、贸易关系的密切程度等。

通过对节点和边属性的量化，可以更好地描述和分析国际贸易的模式和特征。

在国际贸易模型的演化分析中，研究者可以利用各种算法和模拟方法来模拟和预测国际贸易的发展趋势。

例如，可以使用社区发现算法来发现具有类似贸易模式的国家群体，从而揭示贸易关系的集聚效应。

此外，还可以利用模拟方法来预测不同贸易政策的效果，为政策制定者提供决策支持。

通过复杂网络的国际贸易模型构建和演化分析，我们可以更好地理解和解释国际贸易的现象和规律。

例如，通过对网络的拓扑结构和节点属性的分析，可以揭示贸易关系的不平等性和集聚效应等现象。

此外，通过模拟和预测国际贸易的发展趋势，可以为政策制定者提供参考和建议，以促进国际贸易的平衡和可持续发展。

然而，复杂网络的国际贸易模型构建和演化分析还面临一些挑战和限制。

首先，贸易数据的获取和处理是一个复杂和耗时的过程。

其次，网络模型的建立和参数的选择也需要较高的专业知识和技术支持。

此外，模型的精确性和预测能力也需要进一步的改进和验证。

综上所述，复杂网络的国际贸易模型构建和演化分析是一个富有挑战性和前景的研究领域。

中国原油进出口贸易现状及对策

中国原油进出口贸易现状及对策在原油进出口贸易中，我国属于是原油进口大国，原油进口量呈每年不断增长的趋势进口量呈逐年上涨趋势，并在2018年首度超过美国成为世界第一大原油进口国，尤其是近年以来，国内大批民营企业异军突起，炼化产能大幅增加，2019年国内炼油一次加工能力扩至8.47亿吨，逼近9.0亿吨大关而我国的原油出口量却在不断减少。

，国内原油产能不足，原油进口成为必然选择。

本文针对我国原油进出口贸易中存在的问题进行分析，并针对问题提出几点有效的解决对策，希望通过本文的分析能够进一步促进我国原油贸易的发展。

标签：中国原油;能源安全;战略;进出口贸易进口贸易;现状;对策随着对石油资源的不断开发和利用，我国的石油储存资源越来越少，为了满足我国经济发展对石油资源的需求，我国开始向其他国家进口原油，极大的推动了我国原油贸易行业的发展极大地推动了我国原油贸易行业的发展，但是我国的原油进出口贸易中存在有很多问题，不利于我国原油贸易的发展，因此国家需要对原油进出口贸易进行加强重视，提高对原油资源的有效利用。

一、我国原油进口贸易的现状我国的原油进口贸易有一个曲折的发展过程，在建国初期到60年代之间，由于石油开采技术水平差，开采量低，还处于向其他国家进口石油的时期;从1960年到1979年之间，我国的石油开采技术不断提高，加大了石油开采规模，提高了石油开采量，基本上实现了石油资源的自给自足，并且在这段时间内，还向朝鲜、菲律宾等出口部分石油资源;但是从1980年到2000之前，由于国内消费量快速增长，国内石油勘探开发已不能满足日常生产生活需要，石油资源进口不断提高，并在1993年成为石油净进口国;2000年世界金融危机爆发，导致国际原油贸易量不断降低，到2002年我国原油贸易量才有所回升。

从2002年至今，我国一直都处于原油进口的地位，并且进口量不断扩大，年均增长率超过13.0%，其中2017年同比增长65万桶/日，增量较2016年提高一倍，占世界石油需求增量的41.0%，并在2018年跃居世界第一大原油进口国，约为日本的三倍、印度的两倍。

贸易网络地位与出口技术复杂度——基于跨国面板数据的实证分析

出口技术复杂度作为外贸发展方式转型升级的重要内容，被认为是衡量一国或地区出口产品技术水平和商品结构的有效指标，并在一定程度上反映了该国或地区的国际分工地位。⑷一国出口技术复杂度的提升是一个系统性工程，受诸多因素影响。要素禀赋结构［5］，基础设施水平［6］,人力资本积累［7］,金融体系的发展［8］等都构成了一国出口技术复杂度提升的动力机制。随着全球价值链分工体系的深入发展, 出口技术复杂度的贸易因素引起了国内外学者的广泛关注。Geref；（2016）认为一国出口技术复杂度的提
现有文献对于贸易网络与出口技术复杂度的研究大都从两个平行维度出发，鲜有文献将两者放在同一框架内进行研究。本文采用社会网络分析方法构建全球贸易网络，刻画网络的整体概况及各国在贸易网络中的相对地位和网络特征。在此基础上，实证考察一国在全球贸易网络中的地位对出口技术复杂度提升的作用程度，为我国对外贸易战略转型提出政策建议。本文的创新点在于:第一，从贸易网络的视角出发,分析全球贸易网络的整体格局和国家的贸易网络地位及特征;第二，实证检验一国贸易网络地位对其出口技术复杂度的影响，并且进一步分析在出口技术复杂度条件分布不同的情况下，一国贸易网络地位对其的差异性影响。第三，结合门槛分析方法进一步考察了贸易自由化对一国贸易网络地位与出口技术复杂度之间关系的调节作用,并就中国如何在国际贸易摩擦加剧的背景下实现出口技术复杂度的攀升提出政策建议"
优势，并且在产品创新及开发方面也具有更多的比较优势。&12'—方面，中心性较高的国家通过选择与价值链高端的国家建立紧密贸易关联，可以获取高端技术外溢和市场规模开拓，促进其在全球价值链分工中地位的攀升;&13'另一方面，由于网络中其他国家对该国贸易具有较强的依赖性，中心性较高的国家可以通

基于混沌理论视角的国际油价波动复杂性分析

的系统是可以自我调整的，种调整不是被动的，这而且这种现象对世界具有极大的影响效果。
素（或子系统）的非线性关联使运动轨迹（系统的其他层次或其他部分又可能是混沌或无序的。复杂或
第３卷
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顿大学著名管理专家威廉・哈拉尔所指出的：复杂 “ 过自上而下的中心控制体制对复杂的环境加以控
摘要：于复杂系统混沌理论的视角，基分析了国际原油价格波动及国际原油市场运行系统的复杂性，为国际油价认的波动是国际经济、融、油生产、易、费及地缘政治等多因素构成一个相互影响、互作用的非线性系统网金石贸消相
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石油贸易格局的网络结构分析

石油贸易格局的网络结构分析作者：卜晶晶王博君来源：《当代经济管理》2018年第03期摘要：演以丝绸之路经济带沿线带的核心区、扩展区、辐射区共25个国家为研究对象，根据其研究数据建立石油贸易网络并采用社会网络分析法进行综合分析，进一步采用QAP 模型提出影响石油贸易网络的重要因素。

研究结果表明：（1）丝绸之路经济带沿线国家石油贸易联系在2005～2014 年期间整体上保持不变，石油贸易量逐年增长，石油贸易网络结构呈非典型的无标度网络。

石油贸易出口为有序的星型结构，石油出口的控制权掌握在少数国家，而石油进口的网络结构较为无序。

（2）石油贸易网络的脆弱性增强，石油贸易网络存在较强的异质化程度，越来越少的核心国家承担石油贸易。

（3）沿线国家的人口差距、替代能源比重差距对石油贸易网络存在负面影响，对比而言，各国家之间的经济发展水平差距、陆地相邻程度对石油贸易网络的影响不大。

（4）石油贸易网络还受沿线国家的交流语种和物流绩效的影响，贸易联系更容易在不同语言的国家之间建立，难以在物流绩效水平差距较大的国家之间进行建立。

关键词：演丝绸之路经济带；石油贸易；社会网络；QAP模型[中图分类号] F416.22 [文献标识码] A [文章编号]1673-0461（2018）03-0063-08一、引言古丝绸之路作为亚欧国家文化交流和贸易物流大通道，其发展可以快速推动沿线区域国家经济的发展，对亚欧国家开展合作具有深远影响。

近年来，伴随着世界经济发展全球化和区域经济一体化，丝绸之路经济带沿线国家的经贸往来和经济合作不断加强，各国家之间的竞争也日趋激烈，古丝绸之路的没落使得全球经济增长乏力，中国经济发展也存在下行压力。

为了进一步加强与亚欧国家的密切合作，创造互利共赢的经济共同体，形成具有一定地缘优势的贸易主体，中国作为古丝绸之路的起点和主要国家，迫切需要重塑商贸物流大通道的辉煌进行丝绸之路经济带建设推进沿线区域国家的经济合作进程，这成为丝绸之路经济带的提出背景。

原油行业大数据报告分析

原油行业大数据报告分析引言原油作为全球最重要的能源资源之一，对全球经济和能源市场具有重要影响力。

随着信息技术的发展，原油行业也越来越注重数据分析，以便更好地了解市场动态和预测未来走势。

本报告旨在通过分析原油行业的大数据，提供对该行业的深入洞察。

数据来源和方法本报告的数据来源主要包括国际能源署（IEA）、能源信息管理局（EIA）、国际原油交易所（ICE）等权威机构的公开数据，以及原油期货和现货市场的交易数据。

数据的分析方法主要包括统计学指标、图表展示、趋势分析等。

原油市场概览根据数据统计，2019年全球日均原油产量达到约100万桶。

其中，美国是全球最大的原油生产国，2019年平均日产量达到约1300万桶。

其次是沙特阿拉伯和俄罗斯，平均日产量分别为约1100万桶和约1000万桶。

全球最大的原油消费国是美国，其次是中国和印度。

原油价格受供需关系和地缘政治因素的影响，价格波动较为频繁。

市场趋势分析通过对过去几年的数据分析，可以看出几个市场趋势。

首先，随着新能源技术的发展，很多国家开始逐渐减少对化石燃料的依赖，这对原油需求产生了一定影响。

其次，供应方面，美国的页岩油生产发展迅猛，成为全球产量增长的主要推动力量。

另外，地缘政治影响也是原油市场价格波动的重要原因。

例如，中东地区的冲突和紧张局势常常导致原油价格的剧烈波动。

数据分析与预测通过对历史数据的分析，可以得出一些对未来的预测。

首先，随着全球经济的不断发展，对原油的需求仍将保持增长趋势。

据预测，到2030年，全球原油需求将达到1300万桶/日。

其次，新能源技术的不断发展可能会对原油市场产生一定冲击，需求可能略有下降。

另外，地缘政治风险仍然是原油价格波动的主要因素之一，因此需要关注全球地缘政治形势的变化，以及其对原油市场的潜在影响。

结论通过大数据的分析，我们能够更好地了解原油行业的发展趋势和市场动态。

原油市场受供需关系、地缘政治因素等多个因素的影响，价格波动频繁。

关于跨区域原油贸易的具体分析

关于跨区域原油贸易的具体分析【摘要】跨区域原油贸易在全球经济中扮演着重要角色，对国家间的经济发展和能源安全都有着重要影响。

本文旨在深入探讨跨区域原油贸易的现状、影响因素、发展趋势、挑战和机遇，并通过案例分析展示其具体情况。

在结论部分将再次强调跨区域原油贸易的重要性，提出未来发展方向，并总结展望未来的发展潜力。

通过本文的研究，可以更好地了解跨区域原油贸易的现状与未来趋势，为相关政策制定和企业战略规划提供有益参考。

【关键词】跨区域原油贸易、重要性、研究目的、现状分析、影响因素、发展趋势、挑战和机遇、案例分析、结论、发展方向、总结、展望1. 引言1.1 跨区域原油贸易的重要性跨区域原油贸易的重要性在全球贸易中占据着重要地位。

原油是世界上最主要的能源资源之一，而跨区域原油贸易则是实现原油资源有效配置和满足各国能源需求的重要途径。

由于各国原油资源分布不均，有的国家拥有丰富的原油资源，而有的国家却缺乏原油资源，因此跨区域原油贸易能够满足各国的能源需求。

跨区域原油贸易还可以带动相关产业的发展，促进经济增长。

原油是许多工业生产的基础原料，而且在现代社会中，能源需求对国家经济增长至关重要。

通过跨区域原油贸易，不仅可以实现资源的互补和优势互补，还可以促进相关产业的发展，增加就业机会，提高经济效益。

跨区域原油贸易还可以促进国际合作与交流，推动全球经济一体化进程。

在全球化的背景下，各国间的经济联系日益紧密。

通过跨区域原油贸易，各国能够更好地合作共赢，实现资源共享，促进和谐发展。

跨区域原油贸易不仅对各国能源安全和经济增长至关重要，还对国际合作与发展具有重要意义。

在当前全球化的大背景下，跨区域原油贸易的重要性将愈发凸显，各国应加强合作，共同推动跨区域原油贸易的发展。

1.2 研究目的研究目的的核心在于深入了解跨区域原油贸易的现状情况，探讨其影响因素和发展趋势。

通过分析跨区域原油贸易所面临的挑战和机遇，为相关部门和企业提供决策参考。

国际石油贸易的社会网络分析

论文编号：外交学院本科毕业论文论文标题：国际石油贸易的社会网络分析作者：叶玉平学号：200915001系别：国际经济学院专业：国际经济与贸易班级：2009512导师及职称：曹胜玉讲师最终成绩：论文打印日期：2013年5月12 日摘要石油贸易是国际大宗商品交易中最重要的组成部分，参与国家多、涉及范围广，关乎到全球市场的繁荣和稳定。

石油大规模广泛的跨国和越洋贸易以及政治、经济以及国家战略多层次的因素使得国际石油贸易关系非常复杂，从研究贸易网络的整体结构以及一些主要贸易国在网络中的地位这个角度，网络分析方法为研究国际石油贸易提供很好的工具。

本文根据联合国统计的2011年的石油贸易数据，分析全球了石油贸易的整体网络、次级网络的网络结构包括节点度、点权、密度等特征，并对国际中的核心贸易国家在网络中的角色影响进行具体观察和研究，结合了石油安全问题，对不同国家采取的石油战略做了一些初步的探讨。

一些结果表明，石油贸易网络整体稀疏且差异性大，节点度与点权有很高的相关性，石油贸易在核心网络中呈现一定方向性，贸易互惠程度比较低，核心国家在网络中地位不同，石油贸易策略也有差异。

这些研究使对国际石油贸易的认识更加深入，特别是，核心国家的相互比较，对中国维护石油战略安全有重要意义。

关键词：国际石油贸易社会网络分析整体网络个体网络AbstractThe oil trade, which involves many countries all over the world, is the most important part of international commodity trading and it’s essential to the prosperity and stability of global market. Oil trading in massive volume across the borders and oceans and political, economic, and other nationally strategically multi-sphere factors have made oil trading network extremely complex. Network analysis is a good method in studying global oil trading about the overall network structure and the status of major players in oil trading. Based on 2011 statistics from the UN, this article analyze the characteristics of overall trading network and sub-networks including the node degree, node weight, density, etc., and specifically observes and researches some core trading countries, and also makes some preliminary exploration about oil strategies corresponding to oil security. The results show that it’s of sparsity and great disparity in the whole network and only has a few reciprocal ties between different parties, and node degree is highly related to node weight, and there is a directional trade flow in the core network. With different status in the core network, core players have different oil strategy. This article gives us a profounder understanding about international oil trade, and especially the comparison among core countries is of great importance in upholding China’s oil security.Key words:International oil trade Social Network analysis Overall network Ego-network目录引言 (1)一、网络分析方法 (1)（一）网络分析方法的基本介绍 (1)（二）网络分析方法在国际贸易研究中的应用 (2)二、数据与网络模型 (3)（一）数据搜集处理 (3)（二）模型构建与基本方法 (3)三、国际石油贸易的整体网络特征 (4)（一）不加权的网络分析 (4)1、节点度与度分布 (4)2、密度 (6)3、有向关系网络的凝聚子群 (6)（二）加权的网络分析 (8)1、点权 (8)2、点权与节点度 (9)四、主要石油贸易国的比较分析 (10)（一）贸易核心网络 (10)1、核心网络的构建 (10)2、核心网络的分析 (11)（二）贸易次级网络 (14)1、次级网络的基本特征 (15)2、中心度 (16)3、中介性 (17)五、结论 (19)参考文献 (20)附录 (21)引言石油作为目前人类最重要的常规能源，具有重要的战略意义，石油贸易、能源安全始终是大家关注的热点问题。

基于不确定理论的原油海运网络连通可靠性研究

Connectivity Reliability of Maritime Transportation Network for Crude Oil Based on Uncertainty Theory 作者：吕靖;王爽
作者机构：大连海事大学交通运输工程学院,辽宁大连116026
出版物刊名：运筹与管理
页码： 85-94页
年卷期： 2018年第5期
主题词：原油海运网络;连通可靠性;不确定理论;最可靠路径选择
摘要：原油海运网络是原油进口国的海上生命线,为科学衡量网络中节点受到突发事件影响
后的原油海运网络的连通可靠性,本文采用不确定变量来描述突发事件发生后各节点的连通性,引入不确定理论对原油海运网络连通可靠性进行评估,并建立了不确定原油海运网络的最可靠路径
选择模型。

本文不确定变量的引入不再依赖较多的历史数据去描绘节点失效的概率分布,而且提
出的最可靠路径选择模型可以确保突发事件发生后原油的及时运输。

本文还提出了α-最可靠路
径和最大测度最可靠路径选择问题,给出不确定原油海运网络最可靠路径风险值的不确定分布,为突发事件发生后决策者的路径选择提供依据。

本文以中国进口原油海运网络为例作案例分析。

关于跨区域原油贸易的具体分析

关于跨区域原油贸易的具体分析随着全球化进程的不断加速，跨区域原油贸易已经成为了世界能源格局中不可或缺的一部分。

在经济全球化的大背景下，跨区域原油贸易呈现出了多元化的特点，主要是因为原油价格的不断变动以及各国政治、经济利益的博弈。

本文将从跨区域原油贸易的定义、形成原因、贸易主体和贸易方式等几个方面进行详细的分析。

跨区域原油贸易是指跨越不同国家或地区进行的原油贸易活动。

这种贸易不同于国内原油贸易，因为它不仅涉及了两个不同的国家或地区，而且还需要在国际贸易法律框架下进行。

二、形成原因1.资源分布不均衡各国原油资源的分布不均，导致一些国家需要向其他国家进口原油。

例如，中国、日本等国排放压力巨大，需要大量进口原油。

这种进口需要以足够的量和较低的价格实现，跨区域原油贸易就成为了必然的选择。

2.需求增长随着经济的快速发展和人民生活水平的提高，原油需求也不断增长。

然而，一些国家当地的资源限制了它们的产量，增加了其进口原油的量。

因此，政治不稳定和市场变化等问题都对跨区域原油贸易做出了贡献。

3.稳定供应为确保经济运转的稳定性，一些国家必须保持其依赖性国家的资金供应。

原油在这方面特别重要，因为它是工业生产和交通运输的关键因素。

因此，跨区域原油贸易因稳定供应而成为可能。

三、贸易主体1.生产国由于一些国家原油资源丰富，他们可以成为原油的生产国。

例如，OPEC成员国中的沙特阿拉伯和伊朗，是全球两大原油生产国。

为了赚取大量的外汇，这些生产国可能会向其他国家销售其原油。

2.进口国由于一些国家缺乏自身原油，它们需要从原油生产国进口其原油。

例如，中国是一位高消耗的进口国，它在2018年通过进口原油满足45%的能源需求。

同样的，这样的贸易关系也促进了产生供求模式，成为财经基础科学的一部分。

3.贸易商全球原油贸易涉及许多贸易商，它们在不同范围内负责原油的购买、销售和跨区域运输等。

它们通常是国际贸易的重要参与者，是使跨区域原油贸易实现的中间人。

人民币原油期货国际地位研究——基于多元传递熵的复杂网络分析

人民币原油期货国际地位研究——基于多元传递熵的复杂网
络分析
施楠;任再萍
【期刊名称】《上海经济》
【年(卷),期】2024()1
【摘要】近年来原油期货计价货币由美元垄断转向逐渐出现多元化的趋势,为了科学评估上期所人民币原油期货(SC原油)在国际原油期货市场中的地位,本文通过基于多元传递熵的信息溢出网络分析,构建国际原油期货市场信息溢出网络,检验原油期货市场的重要性。

实证证据显示,孟买、东京等非美元原油期货市场有较大的信息溢出,与美元原油市场存在显著的独特信息溢出关系,但SC原油的独特信息溢出不显著。

与其他非美元原油期货不同,SC原油与人民币汇率之间没有显著的独特信息溢出关系,揭示原油期货市场与外汇市场之间的信息传递较弱。

本文的发现对人民币国际化发展带来启示,应加强人民币在多元化的国际货币体系中的作用,加快人民币原油期货市场发展,进一步完善人民币汇率衍生品市场,与人民币原油市场实现协同发展。

【总页数】14页(P84-97)
【作者】施楠;任再萍
【作者单位】上海社会科学院信息研究所;上海对外经贸大学金融管理学院
【正文语种】中文
【中图分类】F062.9
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复杂断块油藏开发动态分析

复杂断块油藏开发动态分析前言 (2)§1 注水油田开发中存在的主要矛盾 (2)§2 复杂断块油藏开发动态分析 (9)2、平面矛盾的调整 (14)3、层内矛盾的调整 (14)前言采油矿和采油队作为采油厂的采油管理单位，其管理水平和技术水平的高低，直接关系到采油厂的决策能否坚决的、全面的得到实施，关系到采油厂能否实现持续、稳定发展的发展目标，而采油队技术人员作为采油厂决策的直接实施者，其自身素质的高低直接决定了实施效果，所以，如何提高采油队技术人员的业务素质，提高其理解上层领导的决策意图的能力就显得尤为重要，下面就采油队技术管理人员生产中遇到的主要问题进行探讨，希望能够给大家带来一定的收获。

§1 注水油田开发中存在的主要矛盾在油田注水开发过程中，多油层非均质油藏由于储层渗透率在纵向上和平面上的非均质性，注入水沿着相对高渗透层或高渗透条带窜流，而相对中低渗透层和中低渗透条带吸水较少，使得油层生产能力不能得到充分发挥，从而引起一系列矛盾，归纳起来有三大矛盾，它们是影响高产稳产和提高采收率的基本因素。

要搞好油水井的管理和分析，首先要分析油水运动的规律，正确认识三大矛盾：§1.1 层间矛盾层间矛盾是指高渗透储层与中低渗透储层在吸水能力、水线推进速度等方面存在的差异性。

相对高渗透层连通好，注水效果好，吸水能力强，产量高，油层压力高，但是见水快，容易形成单层突进，成为高含水层，并干扰中低渗透层产油能力的发挥。

而相对中低渗透层，渗透率低，注水见效慢，产量低，生产能力不能充分发挥。

当与高渗透层合采时，这些油层受到高压层的干扰，出油少或不出油，甚至出现倒灌现象。

层间矛盾使油井产量递减较快，含水上升速度快。

层间矛盾能否得到较好的调整，是东辛复杂断块油田能否长期稳定生产，油田能否获得较高采收率的关键所在。

§1.1.1 层间干扰与稳产基础的关系§1.1.1.1 波及体积与采收率的关系表征油藏开发效果的参数非常的多，其中经济采收率是表征油藏开发效果最重要的参数之一[1]。