This paper demonstrates that empirically grounding the discount factor significantly influences the determination of the carbon price. Using two complementary nonlinear statistical approaches, we assess which utility formulations and corresponding stochastic discount factors best align with U.S. data. We provide evidence that habit formation is essential for capturing the time variation in the stochastic discount factor necessary to match the data. This increased time variation raises the carbon price by 32% and makes it five times more procyclical compared to standard models. The heightened procyclicality reduces aggregate risk, the risk premium, and the need for precautionary savings.
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This paper demonstrates that empirically grounding the discount factor significantly influences the determination of the carbon price. Using two complementary nonlinear statistical approaches, we assess which utility formulations and corresponding stochastic discount factors best align with U.S. data. We provide evidence that habit formation is essential for capturing the time variation in the stochastic discount factor necessary to match the data. This increased time variation raises the carbon price by 32% and makes it five times more procyclical compared to standard models. The heightened procyclicality reduces aggregate risk, the risk premium, and the need for precautionary savings.