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Allocating renewable subsidies

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  • Hortay, Olivér
  • Rozner, Bence Péter

Abstract

Since 2014, European Union member states have been obligated to partially replace feed-in tariff renewable subsidies with feed-in premiums, in which subsidy rights should be auctioned. The cost of the support system depends on electricity prices and the auction outcomes, meaning that the state cannot directly influence the system. In addition, investors may postpone their projects; therefore, the time required for capacity realization is also uncertain. By contrast, European policymakers expect to achieve an exact proportion of renewable production for each year. National decision-makers need to plan the capacity amount for each period for which subsidy auctions will be required, with their aim being to achieve the target at the lowest cost. We argue that a good allocation strategy saves money for the state. Thus, this article aims to show how different market trends affect subsidy costs and the extent to which allocation strategy influences the realizations of supported capacities. Our model was calibrated using Hungarian data and solar auctions. In a Monte Carlo simulation, we analyzed different solar cost scenarios and investment delay probabilities for stochastic electricity price processes. The three most important results of our model are as follows. (1) The increase in solar costs increases the expected value and standard deviation of premiums. (2) Due to project postponement, a greater amount of subsidy should be allocated, increasing social costs. (3) The diversification of allocated capacity between periods reduces the uncertainty of realization.

Suggested Citation

  • Hortay, Olivér & Rozner, Bence Péter, 2019. "Allocating renewable subsidies," Economic Analysis and Policy, Elsevier, vol. 64(C), pages 236-247.
  • Handle: RePEc:eee:ecanpo:v:64:y:2019:i:c:p:236-247
    DOI: 10.1016/j.eap.2019.09.003
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    References listed on IDEAS

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    3. Kenta Tanaka & Clevo Wilson & Shunsuke Managi, 2022. "Impact of feed-in tariffs on electricity consumption," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 24(1), pages 49-72, January.

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    More about this item

    Keywords

    Allocation strategy; Renewable energy subsidy; Feed-in premium; Monte Carlo simulation;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • H23 - Public Economics - - Taxation, Subsidies, and Revenue - - - Externalities; Redistributive Effects; Environmental Taxes and Subsidies
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy

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