Global Energy Trade Network and its Virtual Water
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Uneven spatial and temporal distribution of natural resources, including energy, has led to the creation of local, regional, and global trade networks. Among a wide range of products that are traded in global trade chains, energy is considered one of the most critical elements for national energy security purposes. Energy availability and consumption are key indexes of human development and social welfare, as all economic sectors need energy for their operations. The necessity of global energy trade is well recognized as the majority of countries suffer from an energy deficiency. The capability of the global energy market to supply the global demand, in relation to the current and future availability and distribution of resources, raises a potential concern. A comprehensive characterization of the global energy trade network would enable an analysis of future energy trade under various global socioeconomic and political scenarios, which is essential for long-term assessments of global energy security. In addition, large amounts of water are consumed to produce tradable fossil fuels in all fuel-producing countries. Assessment of national, regional, and global virtual water flow embodied within the energy trade network is important for evaluating the sustainability of both water and energy. In this thesis, a modeling framework is developed to characterize the global fossil fuel energy trade network (with a focus on oil, natural gas, and coal) and identify the important factors that contribute to the energy flux therein. The future evolution of global energy trade networks under various future scenarios is also investigated, along with the regional and global virtual water trade associated with the energy trade. The proposed methodology is divided into two components: (1) modelling the global energy trade network, focusing on 77 selected major countries (exporters and importers) of the energy market over a baseline period (1993–2016) and (2) projecting the future of the network up to 2050 based on five Shared Socioeconomic Pathways (SSPs). Gravity Law Models (GLMs) are developed for each country, which serve as nodes of the network, to estimate their total capacity for trade with the rest of the world using the identified contributing factors. The RAS method, as a matrix balancing technique, is employed and coupled with the GLMs to estimate bilateral energy trade between exporters and importers. Finally, the constructed trade networks are converted into virtual water trades based on the assessed range of water footprints in the process of fossil fuel production as well as the ratio of each type of fossil fuel in the energy mix of bilateral trade among countries. Evaluation of the proposed modeling methodology shows it performs well in terms of the (i) construction of the global energy trade network while keeping it balanced (total imports = total exports) and (ii) distribution of global energy from exporters to importers, in particular for the major trade players that carry the largest proportion of the global energy trade. The projection results are in line with the narratives of the future scenarios where the global energy trades among countries in year 2050 could vary within a wide range from 215 to 538 EJ. Consequently, such a large-scale energy trade could lead to a median value of 51x109 m3 of virtual water trade at the global scale. However, the large uncertainty in the estimation of the water footprint related to fossil fuel energy production could increase the global virtual water trade within the fossil fuel energy trade to 100x109 m3. This thesis provides a modeling framework that can be used by resource managers for making well-informed decisions for investment and long-term strategies concerning global energy trade and its associated virtual water.
DegreeMaster of Science (M.Sc.)
DepartmentCivil and Geological Engineering
CommitteePatrick, Robert; McPhedran, Kerry; Ferguson , Grant; Hawkes, Christopher
Copyright DateApril 2021
Global energy trade - Virtual water - Trade network modeling - Socioeconomic scenarios