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A modular bottom-up approach for constructing physical input–output tables (PIOTs) based on process engineering models

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  • Liz Wachs

    (Purdue University)

  • Shweta Singh

    (Purdue University
    Purdue University)

Abstract

Physical input–output tables (PIOTs) were first conceptualized in the 1990s but have not been widely adopted. However, with the increased emphasis on building a circular economy and understanding the resource nexus, PIOTs will become critical for optimizing resource flows and restructuring economies to close material loops. This necessitates a focus on improved methodologies for PIOT development to allow wider adoption. In this work, we propose and demonstrate a modular bottom-up approach for constructing PIOTs from process engineering models. The method was tested on a PIOT for nitrogen with a subset of sectors in Illinois (USA) and compared with a nitrogen PIOT developed earlier for the same time period, finding equal or higher confidence in sector balances. While the method has high initial costs, its suitability for automation enables it to allow the fast creation of PIOTs where technical coefficient matrices reflect underlying physical processes and relationships within and between sectors, thus capturing accurately the physical structure of the economy. We also demonstrate how the method can be extended for the creation of regional input coefficient matrices. While not implemented here, the method can potentially be used for the creation of hybrid IO tables, trend analysis through time series and combined with non-survey methods to fill data gaps. This will allow combining the strengths of complementary methodologies for constructing PIOTs and standardization of methods for better reliability.

Suggested Citation

  • Liz Wachs & Shweta Singh, 2018. "A modular bottom-up approach for constructing physical input–output tables (PIOTs) based on process engineering models," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 7(1), pages 1-24, December.
    • Handle: RePEc:spr:jecstr:v:7:y:2018:i:1:d:10.1186_s40008-018-0123-1
    DOI: 10.1186/s40008-018-0123-1
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    2. Cottafava, Dario & Gastaldo, Michele & Quatraro, Francesco & Santhiá, Cristina, 2022. "Modeling economic losses and greenhouse gas emissions reduction during the COVID-19 pandemic: Past, present, and future scenarios for Italy," Economic Modelling, Elsevier, vol. 110(C).

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