Global energy flows embodied in international trade: A combination of environmentally extended input–output analysis and complex network analysis
暂无分享,去创建一个
Bin Chen | Ge Chen | Bin Chen | Ge Chen | Jiashuo Li | X. Wu | M. Y. Han | L. Zeng | Jiashuo Li | Ziyu Li | X. F. Wu | L. Zeng | Ziyu Li
[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 .