A BAYESIAN TECHNIQUE FOR REFINING THE UNCERTAINTY IN GLOBAL ENERGY-MODEL FORECASTS

Global energy models have a large degree of uncertainty associated wit h them. This consists of uncertainty in the model structure as well as uncertainty in the exogenous input parameters. This paper combines Mo nte Carlo methods with Bayesian updating techniques to provide a metho d for refining the uncertainty in the Edmonds-Reilly global energy mod el. The Bayesian updating technique uses likelihood-based windows cons tructed from actual observations of the output variables to filter out the model simulations that do not conform with the observed output. T he windows are based on outputs of energy consumption and carbon emiss ions. Two alternative model structures are examined: one with uncorrel ated input parameters and the other with correlated input parameters. Statistical properties are calculated to measure the effects of window ing on the output distributions. The partial rank correlations between the inputs and outputs and between the inputs are also determined. Th e prior distributions and correlation structure of the inputs are then revised through the updating process. An application of the windowing process illustrates the effects of capping carbon emissions on the in put structure.