Learning and Stock Effects in Environmental Regulation: The Case of Greenhouse Gas Emissions

Abstract This paper concerns the optimal regulation of greenhouse gases that lead to global climate change. In particular, we focus on uncertainty and learning (which, over time, resolves uncertainty). We present an empirical stochastic model of climate–economy interactions and present results on the tension between postponing control until more is known vs acting now before irreversible climate change takes place. Uncertainty in our model is in the damage caused by global warming. The results suggest that a temporary carbon tax may dominate a permanent one because a temporary tax may induce increased flexibility.

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