Optimal carbon emission path under uncertainty: Physical risks and transition risks

To quantify the physical risks and transition risks associated with climate change, while analyzing the impact of uncertainty, we develop a dynamic stochastic general equilibrium model. It explores how the social planner maximizes social welfare objectives while accounting for risk preferences over uncertainty in climate, production, and investment. We obtain an optimal carbon tax path within a market economy to inform decisions made by businesses and governments. For a simple scenario, we provide analytical solutions for the Hamilton–Jacobi-Bellman (HJB) equations. We identify the trade-off between physical risks and transition risks affected by climate change. Under the optimal carbon emission path, we further discover the relationship between stock price and carbon tax. Specifically, this is reflected in the consistency of the trends and their impacts. For a complex, empirically realistic scenario, we use a deep learning algorithm to solve this problem. We find that the social planner’s aversion to uncertainty can reduce the stock price benefits through investors’ climate sensitivity (or climate risk premium). This helps enhance efforts to reduce emissions and keeps temperature rises within 2∘C this century. Additionally, we analyze the impacts of climate damage, technological advancements, and changes in investors’ climate sensitivity. The planner can improve investors’ climate sensitivity and constrain corporate emission behaviors through tighter policies or other measures to achieve a more robust optimal reduction strategy. Therefore, the planner must balance physical risks and transition risks in the decision-making process, while also being clearly aware of the window for emission reductions. Especially, when making long-term decisions, the uncertainty of the model and the aversion to uncertainty should be considered, which implies that carbon emission reduction intensity should be adjusted accordingly.