Working Paper: NBER ID: w22795
Authors: Kent D. Daniel; Robert B. Litterman; Gernot Wagner
Abstract: Pricing greenhouse gas emissions involves making trade-offs between consumption today and unknown damages in the (distant) future. This setup calls for an optimal control model to determine the carbon dioxide (CO2) price. It also relies on society’s willingness to substitute consumption across time and across uncertain states of nature, the forte of Epstein-Zin preference specifications.\nWe develop the EZ-Climate model, a simple discrete-time optimization model in which uncertainty about the effect of CO2 emissions on global temperature and on eventual damages is gradually resolved over time. We embed a number of features including potential tail risk, exogenous and endogenous technological change, and backstop technologies.\nThe EZ-Climate model suggests a high optimal carbon price today that is expected to decline over time as uncertainty about the damages is resolved. It also points to the importance of backstop technologies and to very large deadweight costs of delay. We decompose the optimal carbon price into two components: expected discounted damages and the risk premium.
Keywords: climate risk; carbon pricing; asset pricing; optimal control model
JEL Codes: G0; G12; Q51; Q54
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| optimal CO2 price (D41) | reduction in emissions (Q52) |
| higher expected damages (K13) | higher optimal CO2 price (D41) |
| technological advancements (O33) | optimal CO2 price (D41) |
| risk aversion increases (D81) | higher optimal CO2 price (D41) |
| expected damages from climate change (Q54) | stochastic discount factor (D15) |
| stochastic discount factor (D15) | optimal CO2 price (D41) |