Disaggregating Input--Output Models With Incomplete Information
AbstractDisaggregating a sector within the Leontief input--output (IO) framework is not a straightforward task since there is more than one possibility for the unknown technical coefficients of the disaggregated IO table, and more information than what is embodied in the aggregated IO table is thus required. This paper presents a methodology for disaggregating sectors into an arbitrary number of new sectors when the only available information about the newly formed sectors is their output weights. A random walk algorithm is used to explore the polytope containing the admissible combinations for the unknown technical coefficients of the disaggregated IO table. These combinations are then used to construct the probability distribution of the coefficients of the inverse Leontief matrix. The methodology is illustrated by disaggregating the electricity production sector of China's 2007 IO table and by looking at the probability distribution of the CO 2 emission intensity factors of the sectors of the economy.
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Bibliographic InfoArticle provided by Taylor & Francis Journals in its journal Economic Systems Research.
Volume (Year): 24 (2012)
Issue (Month): 4 (April)
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- Wang, Yafei & Liang, Sai, 2013. "Carbon dioxide mitigation target of China in 2020 and key economic sectors," Energy Policy, Elsevier, vol. 58(C), pages 90-96.
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