¿Se puede conocer el coste social del CO2?

Resources for the Future acaba de lanzar una iniciativa para mejorar la forma en la que se determina el coste social del CO2, que es un parámetro de gran importancia para la regulación norteamericana. De hecho, una de las primeras cosas que hizo Trump fue cargarse al grupo que lo calculaba...

Key Elements of the Initiative

• Transitioning the current estimation process to an integrated framework, built on an open source, computationally efficient, publicly accessible, and clearly documented computational platform
• Revising the socioeconomic projections of population growth, economic activity, and emissions to better reflect key uncertainties
• Adopting an updated climate model that passes well-defined performance tests of its representation of current climate science
• Updating the climate damage functions that translate climate impacts into monetary values to reflect the current state of the peer-reviewed literature
• Incorporating a discounting procedure that integrates socioeconomic projections and explicitly recognizes the uncertainty surrounding discount rates as well as the interrelationship of uncertainty in discounting and economic growth—and, in turn, societal damages from carbon dioxide emissions
• Convening domestic and international government entities and businesses and conducting educational outreach on using estimates of the social cost of carbon in order to facilitate more informed policymaking worldwide

La cuestión es si esto es factible o no. A nivel académico hay un debate muy interesante a este respecto. Mientras que seguramente la mayoría plantea que el disponer de este numerito es fundamental, y además a nivel global, no falta gente como Pindyck o Stainforth, gente también absolutamente sensata, que dicen que los modelos complejos son un peligro:

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 policymakers into thinking that the forecasts the models generate have some kind of scientific legitimacy. However, some economists and climate scientists have claimed that we need to use some kind of model for policy analysis 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 very little 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 such an approach would look like.

Quizá por eso Gib Metcalf dice que lo importante es el proceso, y no tanto el numerito en sí mismo:

We argue that policymakers need a numerical value for the SCC for policy evaluation and implementation and that producing a credible numerical value requires sophisticated computer models that incorporate climate and economic considerations, that is, IAMs. We also argue that whatever the true value of the SCC, it is not zero, although there is considerable uncertainty surrounding the current state of scientific knowledge about the costs of climate change. We conclude that the evolving nature of the science and the ultimate goal of informing first-best policy suggests that the official SCC used for regulatory analysis by the U.S. government should not be thought of as a single number or even a range of numbers; rather it should be thought of more broadly as a process that yields updated estimates of those numbers and ranges. The ultimate goal of the process is scientific credibility, public acceptance, and political and legal viability.