EXIOPOL (A New Environmental Accounting Framework Using Externality Data and Input-Output Tools for Policy Analysis) was a European Union (EU)-funded project creating a detailed, global, multiregional environmentally extended Supply and Use table (MR EE SUT) of 43 countries, 129 sectors, 80 resources, and 40 emissions. We sourced primary SUT and input-output tables from Eurostat and non-EU statistical offices. We harmonized and detailed them using auxiliary national accounts data and co-efficient matrices. Imports were allocated to countries of exports using United Nations Commodity Trade Statistics Database trade shares. Optimization procedures removed imbalances in these detailing and trade linking steps. Environmental extensions were added from various sources. We calculated the EU footprint of final consumption with resulting MR EE SUT. EU policies focus mainly on energy and carbon footprints. We show that the EU land, water, and material footprint abroad is much more relevant, and should be prioritized in the EU's environmental product and trade policies.

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This paper develops a new methodology to predict the interregional and interindustry impacts of disruptive events. We model the reactions of economic agents by minimizing the information gain between the pre-and postevent pattern of economic transactions. The resulting nonlinear program reproduces, as it should, the pre-event market equilibrium. The methodology is tested further by means of a comparison of this base scenario with two regional production shock scenarios and two interregional trade shock scenarios. The outcomes show a plausible combination of partially compensating demand, supply, and spatial substitution effects, which justifies the further development, testing, and application of this new approach.

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The GRAS method as presented by Junius and Oosterhaven [Junius, T. and J. Oosterhaven (2003) The Solution of Updating or Regionalizing a Matrix with Both Positive and Negative Elements. Economic Systems Research, 15,[87][88][89][90][91][92][93][94][95][96] assumes that every row and every column of a matrix to be balanced has at least one positive element. This might not necessarily be true in practice, in particular, when dealing with large-scale input-ouput tables, supply and use tables, social accounting matrices, or, for that matter, any other matrix. In this short note we relax this assumption and make available our MATLAB program for anyone interested in matrix GRASing. The same issue arises in the presentations of the KRAS method [Lenzen, M., B. Gallego and R. Wood (2009) Matrix Balancing Under Conflicting Information. Economic Systems Research, 21, and the SUT-RAS method [Temurshoev, U. and M.P. Timmer (2011) Joint Estimation of Supply and Use Tables. Papers in Regional Science, 90, 863-882], which should be accordingly accounted for in their empirical applications.