Parameter Uncertainty in CGE Modeling of the Macroeconomic Impact

WANG Can (王 灿) et al:Parameter Uncertainty in CGE Modeling of… 621
endpoint was set at 0 and the highest was set at 4.0.
The resulting distribution for import substitution elasticity
was modeled by a beta distribution with parameters
a = 2.5 and b = 3.5, while the beta parameters for
export transfer elasticity were a = 3.5 and b = 2.5.
Fig. 1 Probability distribution for elasticity of import
substitution
Fig. 2 Probability distribution for elasticity of export
transfer
3.2 Uncertainty in the TEDCGE outputs
Figure 3 shows the probability distributions for carbon
tax rates required to achieve the carbon reduction goals
of 10%, 20%, 30%, and 40% compared with the BAU
scenario for 2010 in China. 5000 repetitions of the
Monte Carlo experiment were first used to formulate
the probability distributions. The Monte Carlo experiments
were then repeated to get another probability
distribution. The errors in the means and variances
from these two experiments were found to be less than
1%, which means that the 5000 Monte Carlo simulations
give sufficiently accurate results. The implications
of the uncertainties are important when evaluating
the performance of a carbon tax policy for a carbon
mitigation strategy, but even in the absence of a policy
the uncertainties in the carbon tax rates have implications
for interpreting results. What is often called a
marginal abatement cost curve for the international
carbon emission trading model is in fact only one series
of values from a series of distributions of possible
values, given the uncertain parameters existing in the
model. Policy analysis with models on carbon emission
trading should be viewed in this context.
Fig. 3 Probability distribution of carbon tax rate response
to carbon reduction rates
The mean (bold middle line), the 95% and 100%
confidence intervals, and the relative error for the marginal
abatement cost curve in 2010 for China calculated
by TEDCGE are illustrated in Fig. 4. The results
show increasing means and variances for the abatement
cost as the carbon reduction goal increases. In
addition, the relative error in the TEDCGE output is
much smaller than the error in the input parameters
(see Table 1).
Fig. 4 Marginal abatement cost curve with parameter
uncertainties
The results show that if the input parameter statistical
information is fully provided, the CGE model outputs
also exhibit statistical characteristics reflecting the
uncertainties transmitted from the inputs to the simulation
results. Although parameter uncertainties throw
doubt on the reliability of the CGE model simulation
results, the input uncertainties are reduced to some extent
through the CGE modeling procedure.