Optimal Policy Under Uncertain Climate Sensitivity in an Agent-Based Model

Abstract

This report aims to answer two primary research questions: how aggressive policymakers should be in their approach to mitigation while the Equilibrium Climate Sensitivity (ECS) is still largely uncertain, and whether policymakers should adapt their strategy as understanding of the ECS evolves. An agent-based integrated assessment model, the DSK model, is modified to incorporate adaptive policymakers, who learn the climate sensitivity as the global mean surface temperature increases and update their strategy accordingly. The learning process is a combination of Bayesian inference and externally imposed probability distribution functions, which aim to simulate developments in climate science. The outcomes, in global warming and unemployment, seen under adaptive policymakers are compared with the outcomes under non-adaptive policymakers, who maintain the same approach as temperature increases. Risk-neutral policymakers, concerned with the expected ECS are compared with risk-averse policymakers, who are concerned with the 99th percentile value. These four policymakers are compared under two policies: a carbon tax with no accompanying policy, and a carbon tax, 50% of the revenue of which is used to fund the building of renewable energy sources. It is concluded that only risk-averse policymaking is effective, where meeting the Paris Climate Agreement goals are concerned. Among risk-averse policymakers, when the results are aggregated across different ECSs according to their current estimated probability, non-adaptive policymaking achieves the greatest climate mitigation. However it also leads to higher expected unemployment, even under the second policy. While which of these policymaking strategies is preferable may be debatable, it is clear that it is the choice of policy that has the most impact, in terms of both unemployment and climate change.