Using computational intelligence to forecast carbon pricesUsing computational intelligence to forecast carbon prices Peer-Reviewed Journal Publication Δημοσίευση σε Περιοδικό με Κριτές 2018-10-112016enEuropean Union has introduced the European Trading System (ETS) as a tool for developing and implementing international treaties related to climate changes and to identify the most cost-effective methods for reducing greenhouse gas emissions, in particular carbon dioxide (CO2), which is the most substantial. Companies producing carbon emissions must effectively manage associated costs by buying or selling carbon emission futures. Viewed from this perspective, this paper provides a model for managing the risk by buying and selling carbon emission futures by implementing techniques that leverage computational intelligence. Three computational intelligence techniques are proposed to provide accurate and timely forecasts for changes in the price of carbon: A novel hybrid neuro-fuzzy controller that forms a closed-loop feedback mechanism called PATSOS; an artificial neural network (ANN) based system; an adaptive neuro-fuzzy inference system (ANFIS). Results are based on 1074 daily carbon price observations collected to comprise a useful time-series dataset and for evaluation of the proposed techniques. The extra-sample performance of the proposed techniques is calculated. Analysis results are compared with those produced by other models. Comparison studies reveal that PATSOS is the most accurate and promising methodology for predicting the price of carbon. It is stated that this paper registers a first attempt to apply a hybrid neuro-fuzzy controller to forecasting carbon prices. http://creativecommons.org/licenses/by/4.0/Applied Soft Computing43107-116 Atsalakis Georgios Ατσαλακης Γεωργιος Elsevier ANFIS forecasting Carbon allowance Carbon price forecasting Computational intelligent forecasting Neuro-fuzzy forecasting PATSOS forecasting