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Disaggregating Input--Output Models With Incomplete Information

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  • Sören Lindner
  • Julien Legault
  • Dabo Guan

Abstract

Disaggregating 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.

Suggested Citation

  • Sören Lindner & Julien Legault & Dabo Guan, 2012. "Disaggregating Input--Output Models With Incomplete Information," Economic Systems Research, Taylor & Francis Journals, vol. 24(4), pages 329-347, April.
  • Handle: RePEc:taf:ecsysr:v:24:y:2012:i:4:p:329-347 DOI: 10.1080/09535314.2012.689954
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    References listed on IDEAS

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    1. Love, Inessa & Preve, Lorenzo A. & Sarria-Allende, Virginia, 2007. "Trade credit and bank credit: Evidence from recent financial crises," Journal of Financial Economics, Elsevier, pages 453-469.
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    Cited by:

    1. Hawkins, Jacob & Ma, Chunbo & Schilizzi, Steven & Zhang, Fan, 2015. "Promises and pitfalls in environmentally extended input–output analysis for China: A survey of the literature," Energy Economics, Elsevier, pages 81-88.
    2. Su, Bin & Thomson, Elspeth, 2016. "China's carbon emissions embodied in (normal and processing) exports and their driving forces, 2006–2012," Energy Economics, Elsevier, vol. 59(C), pages 414-422.
    3. Wan, Liyang & Wang, Can & Cai, Wenjia, 2016. "Impacts on water consumption of power sector in major emitting economies under INDC and longer term mitigation scenarios: An input-output based hybrid approach," Applied Energy, Elsevier, pages 26-39.
    4. 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.
    5. Qian Zhang & Jun Nakatani & Yuichi Moriguchi, 2015. "Compilation of an Embodied CO 2 Emission Inventory for China Using 135-Sector Input-Output Tables," Sustainability, MDPI, Open Access Journal, vol. 7(7), pages 1-17, June.
    6. Chen, Shaoqing & Chen, Bin, 2015. "Urban energy consumption: Different insights from energy flow analysis, input–output analysis and ecological network analysis," Applied Energy, Elsevier, pages 99-107.
    7. Anke Schaffartzik & Dominik Wiedenhofer & Nina Eisenmenger, 2015. "Raw Material Equivalents: The Challenges of Accounting for Sustainability in a Globalized World," Sustainability, MDPI, Open Access Journal, vol. 7(5), pages 1-26, April.
    8. Jordan Hristov & Aleksandra Martinovska-Stojcheska & Yves Surry, 2016. "The Economic Role of Water in FYR Macedonia: An Input–Output Analysis and Implications for the Western Balkan Countries," Water Economics and Policy (WEP), World Scientific Publishing Co. Pte. Ltd., pages 1-37.

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