Working Paper: NBER ID: w21097
Authors: Robert S. Pindyck
Abstract: In recent articles, I have argued that integrated assessment models (IAMs) have flaws that make them close to useless as tools for policy analysis. IAM-based analyses of climate policy create a perception of knowledge and precision that is illusory, and can fool policy-makers into thinking that the forecasts the models generate have some kind of scientific legitimacy. But some have claimed that we need some kind of model, and that IAMs can be structured and used in ways that correct for their shortcomings. For example, it has been argued that although we know little or nothing about key relationships in the model, we can get around this problem by attaching probability distributions to various parameters and then simulating the model using Monte Carlo methods. I argue that this would buy us nothing, and that a simpler and more transparent approach to the design of climate change policy is preferable. I briefly outline what that approach would look like.
Keywords: Integrated Assessment Models; Climate Policy; Social Cost of Carbon
JEL Codes: D81; Q51; Q54
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| modeler discretion (C52) | variability of outcomes (I24) |
| arbitrary inputs (C69) | social cost of carbon (SCC) (H43) |
| uncertainty surrounding climate sensitivity (D89) | unreliable policy recommendations (D78) |
| lack of understanding of feedback mechanisms (D83) | insufficient understanding of climate sensitivity (Q54) |
| damage function (H84) | arbitrary functional forms (C51) |
| temperature increases (Q54) | GDP (E20) |
| lack of empirical support (D91) | arbitrary functional forms (C51) |
| IAMs (F53) | misleading policymakers (D72) |