Integrated assessment models (IAMs) offer a crucial support to decision-makers in climate policy making. For a full understanding and corroboration of model results, analysts ought to identify the exogenous variables that influence the model results the most (key drivers), appraise the relevance of interactions and the direction of change associated with the simultaneous variation of uncertain variables. We show that such information can be directly extracted from the data set produced by Monte Carlo simulations. Our discussion is guided by the application to the well-known DICE model of William Nordhaus (see Nordhaus, 2008). The proposed methodology allows analysts to draw robust insights about the dependence of future atmospheric temperature, global emissions and carbon costs and taxes on the model’s exogenous variables
UNCERTAINTY IN CLIMATE CHANGE MODELLING: CAN GLOBAL SENSITIVITY ANALYSIS BE OF HELP
BORGONOVO, EMANUELE;GALEOTTI, MARZIO DOMENICO;ROSON, ROBERTO
2014
Abstract
Integrated assessment models (IAMs) offer a crucial support to decision-makers in climate policy making. For a full understanding and corroboration of model results, analysts ought to identify the exogenous variables that influence the model results the most (key drivers), appraise the relevance of interactions and the direction of change associated with the simultaneous variation of uncertain variables. We show that such information can be directly extracted from the data set produced by Monte Carlo simulations. Our discussion is guided by the application to the well-known DICE model of William Nordhaus (see Nordhaus, 2008). The proposed methodology allows analysts to draw robust insights about the dependence of future atmospheric temperature, global emissions and carbon costs and taxes on the model’s exogenous variables
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