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Factorial optimization-driven input-output analysis for socio-economic and environmental effects of GHG emission reduction in electric power systems – A Canadian case study

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  • Luo, Bin
  • Huang, Guohe
  • Chen, Leian
  • Liu, Lirong
  • Zhao, Kai

Abstract

Low-carbon transition of electric power system plays an important role in meeting national greenhouse gas (GHG) emission reduction goals. Analyzing the effects of such transition on various sectors could provide targeted and effective mitigation policy recommendations at sectoral level. In this study, a factorial optimization-driven input-output model has been developed to explore socio-economic and environmental (SEE) effects of GHG emission reduction in Canada's electric power system under uncertainty and their interactions. The results highlight the importance of optimizing the structure of a certain system (e.g., energy system or electric power system) on the emission reduction of the whole society under a specific mitigation target. Significance of indirect GHG emissions for sectoral emission reduction policy formulation is further emphasized, especially for agriculture- and manufacturing-related sectors. In addition, factors with significant interactive effects on sectoral total outputs have been identified. Increasing the proportion of clean power (i.e., wind/solar power, small modular reactor power, and coal-fired power with carbon capture and storage technology) is conducive to promoting sectoral total outputs and GHG emission reduction by 2050. The modelling framework can be extended and applied to other regions to help analyze SEE effects under various emission mitigation policies and scenarios.

Suggested Citation

  • Luo, Bin & Huang, Guohe & Chen, Leian & Liu, Lirong & Zhao, Kai, 2024. "Factorial optimization-driven input-output analysis for socio-economic and environmental effects of GHG emission reduction in electric power systems – A Canadian case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    • Handle: RePEc:eee:rensus:v:192:y:2024:i:c:s1364032123010857
    DOI: 10.1016/j.rser.2023.114227
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