The economics of climate change involves a vast array of uncertainties, complicating both the analysis and development of climate policy. This study presents the results of the first comprehensive study of uncertainty in climate change using multiple integrated assessment models. The study looks at model and parametric uncertainties for population, total factor productivity, and climate sensitivity. It estimates the pdfs of key output variables, including CO2 concentrations, temperature, damages, and the social cost of carbon (SCC). One key finding is that parametric uncertainty is more important than uncertainty in model structure. Our resulting pdfs also provide insights on tail events.
Description
Modeling Uncertainty in Climate Change: A Multi-Model Comparison
%0 Report
%1 NBERw21637
%A Gillingham, Kenneth
%A Nordhaus, William D.
%A Anthoff, David
%A Blanford, Geoffrey
%A Bosetti, Valentina
%A Christensen, Peter
%A McJeon, Haewon
%A Reilly, John
%A Sztorc, Paul
%B Working Paper Series
%D 2015
%K model
%N 21637
%R 10.3386/w21637
%T Modeling Uncertainty in Climate Change: A Multi-Model Comparison
%U http://www.nber.org/papers/w21637
%X The economics of climate change involves a vast array of uncertainties, complicating both the analysis and development of climate policy. This study presents the results of the first comprehensive study of uncertainty in climate change using multiple integrated assessment models. The study looks at model and parametric uncertainties for population, total factor productivity, and climate sensitivity. It estimates the pdfs of key output variables, including CO2 concentrations, temperature, damages, and the social cost of carbon (SCC). One key finding is that parametric uncertainty is more important than uncertainty in model structure. Our resulting pdfs also provide insights on tail events.
@techreport{NBERw21637,
abstract = {The economics of climate change involves a vast array of uncertainties, complicating both the analysis and development of climate policy. This study presents the results of the first comprehensive study of uncertainty in climate change using multiple integrated assessment models. The study looks at model and parametric uncertainties for population, total factor productivity, and climate sensitivity. It estimates the pdfs of key output variables, including CO2 concentrations, temperature, damages, and the social cost of carbon (SCC). One key finding is that parametric uncertainty is more important than uncertainty in model structure. Our resulting pdfs also provide insights on tail events.},
added-at = {2015-11-06T10:35:10.000+0100},
author = {Gillingham, Kenneth and Nordhaus, William D. and Anthoff, David and Blanford, Geoffrey and Bosetti, Valentina and Christensen, Peter and McJeon, Haewon and Reilly, John and Sztorc, Paul},
biburl = {https://www.bibsonomy.org/bibtex/25a6df3c8ea9e564bd3d73a8219d07f27/maanwa},
description = {Modeling Uncertainty in Climate Change: A Multi-Model Comparison},
doi = {10.3386/w21637},
institution = {National Bureau of Economic Research},
interhash = {a4be9d4b913612f3d5395670ea1e543b},
intrahash = {5a6df3c8ea9e564bd3d73a8219d07f27},
keywords = {model},
month = {October},
number = 21637,
series = {Working Paper Series},
timestamp = {2015-11-06T10:35:10.000+0100},
title = {Modeling Uncertainty in Climate Change: A Multi-Model Comparison},
type = {Working Paper},
url = {http://www.nber.org/papers/w21637},
year = 2015
}