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Optimal governmental incentives for biomass cofiring to reduce emissions in the short-term

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  • Amin Khademi
  • Sandra Eksioglu

Abstract

Several studies have shown that biomass cofiring is a viable short-term option for coal-fired power plants to reduce their emissions if supported by appropriate tax incentives. These results suggest a unique opportunity for governments to design monetary incentives such as tax credits that lead to reduction in greenhouse gas emissions in biomass-rich regions. Therefore, a natural question is: What is an optimal tax credit strategy in these regions? To this end, we propose a Stackelberg/Nash game, and solve it algorithmically via reformulating the model as a mixed-integer bilinear program and using a piecewise linear relaxation of bilinear terms. The structure of the optimal solution of special cases is exploited, which helps design efficient heuristics. This study develops a case study using real data about power plants and biomass availability in Mississippi and Arkansas. The results compare the optimal tax credit schemes and plants’ cofiring strategies to provide insights on optimal tax credit mechanisms. Results show that a flexible tax credit scheme, which allows a plant-specific tax credit rate, is more efficient than the currently-used flat tax credit rate. This proposed approach uses a smaller budget and targets the plants that need funding support to comply with emissions regulations.

Suggested Citation

  • Amin Khademi & Sandra Eksioglu, 2021. "Optimal governmental incentives for biomass cofiring to reduce emissions in the short-term," IISE Transactions, Taylor & Francis Journals, vol. 53(8), pages 883-896, August.
  • Handle: RePEc:taf:uiiexx:v:53:y:2021:i:8:p:883-896
    DOI: 10.1080/24725854.2020.1718247
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    Cited by:

    1. Dan Yu & Caihong Zhang & Siyi Wang & Lan Zhang, 2023. "Evolutionary Game and Simulation Analysis of Power Plant and Government Behavior Strategies in the Coupled Power Generation Industry of Agricultural and Forestry Biomass and Coal," Energies, MDPI, vol. 16(3), pages 1-19, February.

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