Global energy flows embodied in international trade: A combination of environmentally extended input–output analysis and complex network analysis

[1]  Guoqian Chen,et al.  Energy overview for globalized world economy: Source, supply chain and sink , 2017 .

[2]  Haizhong An,et al.  Indirect energy flow between industrial sectors in China: A complex network approach , 2016 .

[3]  Michael Szell,et al.  Multirelational organization of large-scale social networks in an online world , 2010, Proceedings of the National Academy of Sciences.

[4]  John Scott What is social network analysis , 2010 .

[5]  S. Tao,et al.  Globalization and pollution: tele-connecting local primary PM2.5 emissions to global consumption , 2016, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[6]  Zhan-Ming Chen,et al.  Demand-driven energy requirement of world economy 2007: A multi-region input-output network simulation , 2013, Commun. Nonlinear Sci. Numer. Simul..

[7]  S. Havlin,et al.  Breakdown of the internet under intentional attack. , 2000, Physical review letters.

[8]  Huajiao Li,et al.  Competition pattern of the global liquefied natural gas (LNG) trade by network analysis , 2016 .

[9]  Lei Shen,et al.  Global pattern of the international fossil fuel trade: The evolution of communities , 2017 .

[10]  S. Suh,et al.  The material footprint of nations , 2013, Proceedings of the National Academy of Sciences.

[11]  Howard T. Odum,et al.  Environmental Accounting: Emergy and Environmental Decision Making , 1995 .

[12]  Guoqian Chen,et al.  Embodied energy assessment for Macao׳s external trade , 2014 .

[13]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[14]  M E J Newman,et al.  Fast algorithm for detecting community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[15]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[16]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[17]  Ge Chen,et al.  Urban economy's carbon flow through external trade: Spatial-temporal evolution for Macao , 2017 .

[18]  G. Davis,et al.  The Small World of the American Corporate Elite, 1982-2001 , 2003 .

[19]  Manfred Lenzen,et al.  Mapping the structure of the world economy. , 2012, Environmental science & technology.

[20]  Ruyin Long,et al.  Calculation of embodied energy in Sino-USA trade: 1997–2011 , 2014 .

[21]  Haizhong An,et al.  Evolution of the exergy flow network embodied in the global fossil energy trade: Based on complex network , 2016 .

[22]  Yudong Wang,et al.  Disentangling the determinants of real oil prices , 2016 .

[23]  Mei Sun,et al.  Features and evolution of international fossil energy trade relationships: A weighted multilayer network analysis , 2015 .

[24]  Bin Chen,et al.  Tracking mercury emission flows in the global supply chains: A multi-regional input-output analysis , 2017 .

[25]  Manfred Lenzen,et al.  Decoupling global environmental pressure and economic growth: scenarios for energy use, materials use and carbon emissions , 2016 .

[26]  Ying Fan,et al.  A Dynamic Analysis on Global Natural Gas Trade Network , 2014 .

[27]  L. Freeman Centrality in social networks conceptual clarification , 1978 .

[28]  Manfred Lenzen,et al.  BUILDING EORA: A GLOBAL MULTI-REGION INPUT–OUTPUT DATABASE AT HIGH COUNTRY AND SECTOR RESOLUTION , 2013 .

[29]  Manfred Lenzen,et al.  A structural decomposition analysis of global energy footprints , 2016 .

[30]  Xu Tang,et al.  Analysis of energy embodied in the international trade of UK , 2013 .

[31]  John Skvoretz,et al.  Node centrality in weighted networks: Generalizing degree and shortest paths , 2010, Soc. Networks.

[32]  Sharmistha Bagchi-Sen,et al.  Small and flat worlds: A complex network analysis of international trade in crude oil , 2015 .

[33]  M. Newman Analysis of weighted networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[34]  S. Davis,et al.  China’s international trade and air pollution in the United States , 2014, Proceedings of the National Academy of Sciences.

[35]  Brian D. Fath,et al.  Network structure of inter-industry flows , 2012, ArXiv.

[36]  A. Chiu,et al.  Final production-based emissions of regions in China , 2018 .

[37]  Wei-Qiang Chen,et al.  Structural Investigation of Aluminum in the U.S. Economy using Network Analysis. , 2016, Environmental science & technology.

[38]  S. Mathur Trade, the WTO and energy security : mapping the linkages for India , 2014 .

[39]  Shailesh Kumar,et al.  An aggregated energy security performance indicator , 2013 .

[40]  Bin Chen,et al.  Embodied energy analysis for coal-based power generation system-highlighting the role of indirect energy cost , 2016 .

[41]  Martin Rosvall,et al.  An information-theoretic framework for resolving community structure in complex networks , 2007, Proceedings of the National Academy of Sciences.

[42]  S. Sharma The relationship between energy and economic growth: Empirical evidence from 66 countries , 2010 .

[43]  Yi-Ming Wei,et al.  Consumption-based emission accounting for Chinese cities , 2016 .

[44]  Yalin Lei,et al.  Interprovincial transfer of embodied energy between the Jing-Jin-Ji area and other provinces in China: A quantification using interprovincial input-output model. , 2017, The Science of the total environment.

[45]  Alessandro Vespignani,et al.  Detecting rich-club ordering in complex networks , 2006, physics/0602134.

[46]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.

[47]  Albert-László Barabási,et al.  Error and attack tolerance of complex networks , 2000, Nature.

[48]  Guoqian Chen,et al.  Global supply chain of arable land use: Production-based and consumption-based trade imbalance , 2015 .

[49]  Bin Chen,et al.  Decoupling analysis on energy consumption, embodied GHG emissions and economic growth — The case study of Macao , 2017 .

[50]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[51]  R. Costanza,et al.  Embodied energy and economic valuation. , 1980, Science.

[52]  Lixin Tian,et al.  A complex network perspective on interrelations and evolution features of international oil trade, 2002–2013☆ , 2017 .

[53]  U. Brandes A faster algorithm for betweenness centrality , 2001 .

[54]  Klaus Hubacek,et al.  The Economic Gains and Environmental Losses of US Consumption: A World-Systems and Input-Output Approach , 2014 .

[55]  Ming Xu,et al.  Structure of the Global Virtual Carbon Network: Revealing Important Sectors and Communities for Emission Reduction , 2015 .

[56]  A. Vespignani,et al.  The architecture of complex weighted networks. , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[57]  D. Vuuren,et al.  Indicators for energy security , 2009 .

[58]  Manfred Lenzen,et al.  A CARBON FOOTPRINT TIME SERIES OF THE UK – RESULTS FROM A MULTI-REGION INPUT–OUTPUT MODEL , 2010 .

[59]  Haizhong An,et al.  The evolution of communities in the international oil trade network , 2014 .

[60]  A. Löschel,et al.  Indicators of energy security in industrialised countries , 2010 .

[61]  Bin Chen,et al.  China's energy-related mercury emissions: Characteristics, impact of trade and mitigation policies , 2017 .

[62]  Leto Peel,et al.  The ground truth about metadata and community detection in networks , 2016, Science Advances.

[63]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[64]  Martin Rosvall,et al.  Maps of random walks on complex networks reveal community structure , 2007, Proceedings of the National Academy of Sciences.

[65]  Lei Shen,et al.  The roles of countries in the international fossil fuel trade: An emergy and network analysis , 2017 .

[66]  Ming Xu,et al.  Betweenness-Based Method to Identify Critical Transmission Sectors for Supply Chain Environmental Pressure Mitigation. , 2016, Environmental science & technology.

[67]  Xunpeng Shi,et al.  Setting effective mandatory energy efficiency standards and labelling regulations: A review of best practices in the Asia Pacific region☆ , 2014 .

[68]  M. Newman,et al.  Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[69]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[70]  Manfred Lenzen,et al.  The Employment Footprints of Nations , 2014 .

[71]  Marián Boguñá,et al.  Topology of the world trade web. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[72]  G. Q. Chen,et al.  Global land-water nexus: Agricultural land and freshwater use embodied in worldwide supply chains. , 2018, The Science of the total environment.

[73]  Xiang Li,et al.  A local-world evolving network model , 2003 .

[74]  Judith Gurney BP Statistical Review of World Energy , 1985 .

[75]  Guoqian Chen,et al.  An overview of energy consumption of the globalized world economy , 2011 .

[76]  A. Barabasi,et al.  Quantifying social group evolution , 2007, Nature.

[77]  Ying Fan,et al.  Identification of Global Oil Trade Patterns: An Empirical Research Based on Complex Network Theory , 2014 .

[78]  B. Zhang,et al.  Energy implications of China's regional development: New insights from multi-regional input-output analysis , 2017 .