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Matrix balancing with unknown total costs: preserving economic relationships in the electric power sector

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  • Jeffrey C. Peters
  • Thomas W. Hertel

Abstract

Many preferred matrix balancing methods suffer limitations when total costs (i.e. column sums) are unknown or uncertain. If the total cost (column) constraint is relaxed, economic relationships both between inputs to and/or amongst the sub-sectors may not be preserved (i.e. cost structure and row share, respectively). These relationships are significant in modeling, where production behavior depends on relative costs. This paper presents a share preserving cross-entropy (SPCE) approach which targets economic relationships directly and allows the column constraint to be relaxed. Further, the SPCE solution is identical to the RAS solution when the column constraint is imposed. This cross-entropy formulation complements an existing sum squared error-type approach. The two matrix balancing methods are demonstrated with a disaggregation of the electric power industry where only unit input costs are known with greater certainty. There is a clear trade-off between preserving economic relationships versus the column totals when compared to their column-constrained counterparts.

Suggested Citation

  • Jeffrey C. Peters & Thomas W. Hertel, 2016. "Matrix balancing with unknown total costs: preserving economic relationships in the electric power sector," Economic Systems Research, Taylor & Francis Journals, vol. 28(1), pages 1-20, March.
  • Handle: RePEc:taf:ecsysr:v:28:y:2016:i:1:p:1-20
    DOI: 10.1080/09535314.2015.1124068
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    Cited by:

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    2. Russell Hillberry & David Hummels, 2021. "Tom Hertel’s Influence and Its Lessons about Academic Inquiry," World Scientific Book Chapters, in: Peter Dixon & Joseph Francois & Dominique van der Mensbrugghe (ed.), POLICY ANALYSIS AND MODELING OF THE GLOBAL ECONOMY A Festschrift Celebrating Thomas Hertel, chapter 2, pages 9-39, World Scientific Publishing Co. Pte. Ltd..
    3. Többen, Johannes & Schröder, Thomas, 2018. "A maximum entropy approach to the estimation of spatially and sectorally disaggregated electricity load curves," Applied Energy, Elsevier, vol. 225(C), pages 797-813.
    4. Peters, Jeffrey C. & Hertel, Thomas W., 2016. "The database–modeling nexus in integrated assessment modeling of electric power generation," Energy Economics, Elsevier, vol. 56(C), pages 107-116.
    5. Jakub Boratyński, 2016. "A Bayesian Approach to Matrix Balancing: Transformation of Industry-Level Data under NACE Revision," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 8(4), pages 219-239, December.
    6. Mu, Yaqian & Cai, Wenjia & Evans, Samuel & Wang, Can & Roland-Holst, David, 2018. "Employment impacts of renewable energy policies in China: A decomposition analysis based on a CGE modeling framework," Applied Energy, Elsevier, vol. 210(C), pages 256-267.
    7. Huang, Hai & Roland-Holst, David & Springer, Cecilia & Lin, Jiang & Cai, Wenjia & Wang, Can, 2019. "Emissions trading systems and social equity: A CGE assessment for China," Applied Energy, Elsevier, vol. 235(C), pages 1254-1265.
    8. Weng, Yuwei & Cai, Wenjia & Wang, Can, 2021. "Evaluating the use of BECCS and afforestation under China’s carbon-neutral target for 2060," Applied Energy, Elsevier, vol. 299(C).
    9. C. Oliveira Henriques & S. Sousa, 2023. "A Review on Economic Input-Output Analysis in the Environmental Assessment of Electricity Generation," Energies, MDPI, vol. 16(6), pages 1-26, March.

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