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Regional And Sectoral Disaggregation Of Multi-Regional Input-Output Tables - A Flexible Algorithm

Author

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  • Leonie Wenz
  • Sven Norman Willner
  • Alexander Radebach
  • Robert Bierkandt
  • Jan Christoph Steckel
  • Anders Levermann

Abstract

A common shortcoming of available multi-regional input-output (MRIO) data sets is their lack of regional and sectoral detail required for many research questions (e.g. in the field of disaster impact analysis). We present a simple algorithm to refine MRIO tables regionally and/or sectorally. By the use of proxy data, each MRIO flow in question is disaggregated into the corresponding sub-flows. This downscaling procedure is complemented by an adjustment rule ensuring that the sub-flows match the superordinate flow in sum. The approximation improves along several iteration steps. The algorithm unfolds its strength through the flexible combination of multiple, possibly incomplete proxy data sources. It is also flexible in a sense that any target sector and region resolution can be chosen. As an exemplary case we apply the algorithm to a regional and sectoral refinement of the Eora MRIO database.

Suggested Citation

  • Leonie Wenz & Sven Norman Willner & Alexander Radebach & Robert Bierkandt & Jan Christoph Steckel & Anders Levermann, 2015. "Regional And Sectoral Disaggregation Of Multi-Regional Input-Output Tables - A Flexible Algorithm," Economic Systems Research, Taylor & Francis Journals, vol. 27(2), pages 194-212, June.
    • Handle: RePEc:taf:ecsysr:v:27:y:2015:i:2:p:194-212
    DOI: 10.1080/09535314.2014.987731
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    Cited by:

    1. Ward, Hauke & Radebach, Alexander & Vierhaus, Ingmar & Fügenschuh, Armin & Steckel, Jan Christoph, 2017. "Reducing global CO2 emissions with the technologies we have," Resource and Energy Economics, Elsevier, vol. 49(C), pages 201-217.
    2. Otto, Christian & Willner, Sven Norman & Wenz, Leonie & Frieler, Katja & Levermann, Anders, 2017. "Modeling loss-propagation in the global supply network: The dynamic agent-based model acclimate," OSF Preprints 7yyhd, Center for Open Science.
    3. Otto, C. & Willner, S.N. & Wenz, L. & Frieler, K. & Levermann, A., 2017. "Modeling loss-propagation in the global supply network: The dynamic agent-based model acclimate," Journal of Economic Dynamics and Control, Elsevier, vol. 83(C), pages 232-269.
    4. White, David J. & Hubacek, Klaus & Feng, Kuishuang & Sun, Laixiang & Meng, Bo, 2018. "The Water-Energy-Food Nexus in East Asia: A tele-connected value chain analysis using inter-regional input-output analysis," Applied Energy, Elsevier, vol. 210(C), pages 550-567.
    5. Gengyuan Liu & Asim Nawab & Fanxin Meng & Aamir Mehmood Shah & Xiaoya Deng & Yan Hao & Biagio F. Giannetti & Feni Agostinho & Cecília M. V. B. Almeida & Marco Casazza, 2021. "Understanding the Sustainability of the Energy–Water–Land Flow Nexus in Transnational Trade of the Belt and Road Countries," Energies, MDPI, vol. 14(19), pages 1-19, October.
    6. Schwärzler, Marion Cornelia & Kronenberg, Tobias, 2017. "Methodology of the Multiregional Health Account for Germany - An Iterative Algorithm-Based Multiregionalization Approach of Supply and Use Tables with Emphasis on Health," MPRA Paper 80712, University Library of Munich, Germany.
    7. Meng, Fanxin & Wang, Dongfang & Meng, Xiaoyan & Li, Hui & Liu, Gengyuan & Yuan, Qiuling & Hu, Yuanchao & Zhang, Yi, 2022. "Mapping urban energy–water–land nexus within a multiscale economy: A case study of four megacities in China," Energy, Elsevier, vol. 239(PB).
    8. J. Verschuur & E. E. Koks & J. W. Hall, 2022. "Ports’ criticality in international trade and global supply-chains," Nature Communications, Nature, vol. 13(1), pages 1-13, December.

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