Towards Conditional Prediction Markets as Policy-Making Instruments for the Promotion of Renewable Energy

In prediction markets participants bet on the outcome of uncertain future events and the instantaneous price of such shares then represents an accurate forecasting signal. This paper demonstrates how conditional prediction markets can be employed as decision support tools to identify effective policies for the promotion of renewable energy. In this model, the policy maker’s objective (e.g. achievement of renewable generation targets, social welfare, carbon emission level, etc.) is defined as the settlement metric of the prediction market; participants speculate on whether this metric will be achieved, conditional on each of available policy alternatives being implemented. For instance, one prediction market could ask will the installed capacity of renewable generation in a specific region exceed 6 GW by the end of the year 2023, conditional on implementing a feed-in-tariff policy?; another prediction market may ask will the installed capacity of renewable generation in a specific region exceed 6 G W until the end of the year 2023 conditional on implementing renewable portfolio standards policy? The policy with the highest market price can be interpreted as the option believed by the market participants as having the best prospects for acheiving the installed capacity target of 6 GW. We have simulated the evolution of prices within such a prediction market, where an automated market maker aggregates the trades and settles the market. By using the proposed approach, a case study evaluating two renewable support schemes (comparing feed-in-tariff and renewable portfolio standards) in achieving the renewable generation target is analysed.