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Improving new energy subsidy efficiency considering learning effect: A case study on wind power.

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Summary
This summary is machine-generated.

New energy subsidies are crucial but challenging to allocate. This study finds optimal subsidy rates should decrease with production growth to ensure long-term new energy investment profitability and efficiency.

Keywords:
Learning effectNew energySubsidy efficiencyWind power

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Area of Science:

  • Energy economics
  • Industrial policy
  • Dynamic programming

Background:

  • New energy development relies heavily on government subsidies.
  • Effective allocation of these subsidies is constrained by fiscal limitations.
  • The learning effect in new energy industries influences investment decisions.

Purpose of the Study:

  • To develop a model for optimizing new energy subsidy allocation considering learning effects.
  • To establish a principle for guiding company investment decisions under subsidy policies.
  • To evaluate the efficiency of past subsidy allocations in the wind power sector.

Main Methods:

  • A two-stage dynamic programming model was formulated to simulate investment decisions.
  • Theoretical findings were calibrated using wind power data from 14 countries.
  • The correlation between subsidy rates and the variation rate of production scale increment (VRPSI) was analyzed.

Main Results:

  • Investment decisions are guided by the principle that subsidy rates should be negatively correlated with VRPSI.
  • Excessively high subsidy rates can lead to low VRPSI, reducing future profitability and causing inefficiency.
  • Annual subsidy allocations in 2014, 2016, and 2017 may have been too high.

Conclusions:

  • Optimal subsidy allocation requires a dynamic approach, adjusting rates based on production scale changes.
  • Governments should avoid subsidy patterns that create thresholds for investment, potentially neglecting other energy sources.
  • The developed theoretical model is applicable to various new energy and emerging industries.