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Optimal subsidy level for waste-to-energy investment considering flexibility and uncertainty

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  • Hu, Junfei
  • Chen, Huanyue
  • Zhou, Peng
  • Guo, Peng

Abstract

With the development of waste-to-energy (WTE) technology, waste has become an important energy source in recent years. Currently, WTE investment is highly dependent on government subsidies. How to optimize subsidy levels has thus attracted extensive attention from researchers and government agencies. Most existing studies determined subsidy levels based on the net present value (NPV, i.e., the profit and cost from disposing of waste and selling electricity), neglecting the value generated by flexibly adjusting the investment time to avoid risks. As a result, an underestimated subsidy is achieved and the incentive effect of such subsidy is affected. To address this issue, this paper proposes a real option model to evaluate the value of flexibility. The proposed model reflects realistic decision-making processes and better supports subsidy design by considering flexibility and uncertainty. The proposed model is further applied to a WTE project based on incineration technology located in Shaanxi, China. The lump-sum investment subsidy determined by the proposed model (with flexibility) is larger than that determined by the NPV method (without flexibility). This result verifies the importance of considering flexibility in the subsidy design process. A sensitivity analysis is also performed, which indicates that the feed-in tariff is the most important factor affecting the investment subsidy. These results demonstrate that increasing the volatility rate of uncertain parameters can further increase the investment subsidy with flexibility. Overall, our results may provide useful management insights for government agencies involved in WTE subsidy design.

Suggested Citation

  • Hu, Junfei & Chen, Huanyue & Zhou, Peng & Guo, Peng, 2022. "Optimal subsidy level for waste-to-energy investment considering flexibility and uncertainty," Energy Economics, Elsevier, vol. 108(C).
  • Handle: RePEc:eee:eneeco:v:108:y:2022:i:c:s0140988322000755
    DOI: 10.1016/j.eneco.2022.105894
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