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The social cost of carbon is the expected present value of damages from emitting one ton of carbon today. We use perturbation theory to derive an approximate tractable expression for this cost adjusted for climatic and economic risk. We allow for different aversion to risk and intertemporal fluctuations, skewness and dynamics in the risk distributions of climate sensitivity and the damage ratio, and correlated shocks. We identify prudence, insurance, and exposure effects, reproduce earlier analytical results, and offer analytical insights into numerical results on the effects of economic and damage ratio uncertainty and convex damages on the optimal carbon price. (JEL E12, G22, H23, O44, Q35, Q51, Q54)

AbstractThis introduction outlines the main choices that the Australian Government had in designing a carbon price policy. It concludes with an assessment of the findings of the articles in this Policy Forum.

Since the creation of the European Union Emissions Trading Scheme (EU ETS) in 2005, a burgeoning academic literature has emerged to identify the factors that shape the price of carbon, where one European Union Allowance is equal to one ton of CO2-equivalent emitted in the atmosphere. Thus, there is a need for an updated and thorough literature review on the state-ofthe-art on topic that this paper aims to fulfill. Namely, we consider the main econometric studies that have been recently published in the academic literature, which feature the influence of the following determinants to explain the variation of the price of carbon: institutional decisions; energy prices and weather events; macroeconomic and financial market shocks. The paper concludes with some directions for future research in this area.

Since the creation of the European Union Emissions Trading Scheme (EU ETS) in 2005, a burgeoning academic literature has emerged to identify the factors that shape the price of carbon, where one European Union Allowance is equal to one ton of CO2-equivalent emitted in the atmosphere. Thus, there is a need for an updated and thorough literature review on the state-ofthe-art on topic that this paper aims to fulfill. Namely, we consider the main econometric studies that have been recently published in the academic literature, which feature the influence of the following determinants to explain the variation of the price of carbon: institutional decisions; energy prices and weather events; macroeconomic and financial market shocks. The paper concludes with some directions for future research in this area.

Since the creation of the European Union Emissions Trading Scheme (EU ETS) in 2005, a burgeoning academic literature has emerged to identify the factors that shape the price of carbon, where one European Union Allowance is equal to one ton of CO2-equivalent emitted in the atmosphere. Thus, there is a need for an updated and thorough literature review on the state-ofthe-art on topic that this paper aims to fulfill. Namely, we consider the main econometric studies that have been recently published in the academic literature, which feature the influence of the following determinants to explain the variation of the price of carbon: institutional decisions; energy prices and weather events; macroeconomic and financial market shocks. The paper concludes with some directions for future research in this area.

Summarization: European 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. ; Presented on: Applied Soft Computing