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Sectors May Use Multiple Technologies Simultaneously: The Rectangular Choice-Of-Technology Model With Binding Factor Constraints

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  • Faye Duchin
  • Stephen H. Levine

Abstract

We develop the rectangular choice-of-technology model with factor constraints, or RCOT, a linear programming input--output model for analysis of the economy of a single region. It allows for one or more sectors to operate more than one technology simultaneously, with the relatively lowest-cost one supplemented by others if it encounters a binding factor constraint. The RCOT model solves for sector outputs, goods prices that are set by the highest-cost technologies in use, and scarcity rents that correspond to binding factor constraints experienced by the lower-cost technologies. The model is motivated by the fact that mineral deposits of different qualities may be exploited simultaneously, as may primary and recycled sources for the same materials or irrigated and rain-fed techniques for producing the same crop. RCOT generalizes Carter's square choice-of-technology model, in particular adding the factor constraints that allow several alternatives to operate simultaneously. The Appendix gives a numerical example.

Suggested Citation

  • Faye Duchin & Stephen H. Levine, 2011. "Sectors May Use Multiple Technologies Simultaneously: The Rectangular Choice-Of-Technology Model With Binding Factor Constraints," Economic Systems Research, Taylor & Francis Journals, vol. 23(3), pages 281-302, March.
    • Handle: RePEc:taf:ecsysr:v:23:y:2011:i:3:p:281-302
    DOI: 10.1080/09535314.2011.571238
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    Citations

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    Cited by:

    1. 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.
    2. Faye Duchin, 2017. "Resources for Sustainable Economic Development: A Framework for Evaluating Infrastructure System Alternatives," Sustainability, MDPI, vol. 9(11), pages 1-15, November.
    3. Wimmer, Lorenz & Kluge, Jan & Zenz, Hannes & Kimmich, Christian, 2023. "Predicting structural changes of the energy sector in an input–output framework," Energy, Elsevier, vol. 265(C).
    4. Joseph Palazzo & Roland Geyer & Sangwon Suh, 2020. "A review of methods for characterizing the environmental consequences of actions in life cycle assessment," Journal of Industrial Ecology, Yale University, vol. 24(4), pages 815-829, August.
    5. Lisa Duval & Guillaume Majeau‐Bettez & François Saunier & François Maréchal & Manuele Margni, 2024. "Optimization of the end‐of‐life tire repartition within the European treatment system to minimize its environmental impacts," Journal of Industrial Ecology, Yale University, vol. 28(3), pages 512-526, June.
    6. Vaccaro, Roberto & Rocco, Matteo V., 2021. "Quantifying the impact of low carbon transition scenarios at regional level through soft-linked energy and economy models: The case of South-Tyrol Province in Italy," Energy, Elsevier, vol. 220(C).
    7. Reinout Heijungs & Arjan Koning, 2019. "Analyzing the effects of the choice of model in the context of marginal changes in final demand," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 8(1), pages 1-22, December.
    8. Kang, Jidong & Ng, Tsan Sheng & Su, Bin & Milovanoff, Alexandre, 2021. "Electrifying light-duty passenger transport for CO2 emissions reduction: A stochastic-robust input–output linear programming model," Energy Economics, Elsevier, vol. 104(C).
    9. Roxana Juliá & Faye Duchin, 2013. "Land Use Change and Global Adaptations to Climate Change," Sustainability, MDPI, vol. 5(12), pages 1-18, December.
    10. Christine Roxanne Hung & Paul Kishimoto & Volker Krey & Anders Hammer Strømman & Guillaume Majeau‐Bettez, 2022. "ECOPT2: An adaptable life cycle assessment model for the environmentally constrained optimization of prospective technology transitions," Journal of Industrial Ecology, Yale University, vol. 26(5), pages 1616-1630, October.
    11. Ignacio Cazcarro & Carlos A. López‐Morales & Faye Duchin, 2019. "The global economic costs of substituting dietary protein from fish with meat, grains and legumes, and dairy," Journal of Industrial Ecology, Yale University, vol. 23(5), pages 1159-1171, October.
    12. Maria Amaya & Faye Duchin & Erich Hester & John C. Little, 2022. "Applying a Coupled Hydrologic-Economic Modeling Framework: Evaluating Alternative Options for Reducing Impacts for Downstream Locations in Response to Upstream Development," Sustainability, MDPI, vol. 14(11), pages 1-19, May.
    13. Rocco, Matteo V. & Golinucci, Nicolò & Ronco, Stefano M. & Colombo, Emanuela, 2020. "Fighting carbon leakage through consumption-based carbon emissions policies: Empirical analysis based on the World Trade Model with Bilateral Trades," Applied Energy, Elsevier, vol. 274(C).
    14. Dilekli, Naci & Cazcarro, Ignacio, 2019. "Testing the SDG targets on water and sanitation using the world trade model with a waste, wastewater, and recycling framework," Ecological Economics, Elsevier, vol. 165(C), pages 1-1.
    15. Jadwiga R. Ziolkowska, 2016. "Socio-Economic Implications of Drought in the Agricultural Sector and the State Economy," Economies, MDPI, vol. 4(3), pages 1-11, September.
    16. Malik, Arunima & Lenzen, Manfred & Ely, Rômulo Neves & Dietzenbacher, Erik, 2014. "Simulating the impact of new industries on the economy: The case of biorefining in Australia," Ecological Economics, Elsevier, vol. 107(C), pages 84-93.

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