Parameter Uncertainty in CGE Modeling of the Macroeconomic Impact

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tested for robustness by implementing sensitivity
analyses around the elasticity mean values [9-14] . However,
in practice most analyses use limited sensitivity
analyses, with changes in a few key elasticities to examine
the sensitivity of the results. This procedure has
no way of ensuring that the chosen elasticities are the
important ones, and its role is also limited by the discretionary
character of the values selected. Few of the
existing studies either systematically explore the parameter
uncertainty propagation or examine the contribution
of the various parameters to the uncertainty due
to the expensive computing costs. Harrison et al. [11]
and Alber et al. [13] conducted unconditional sensitivity
analyses, but they focused only on some of the elasticities
and failed to describe the CGE output distributional
information.
The purpose of this paper is to study the problem of
measuring the uncertainty of CGE model simulations
associated with parameter uncertainty using the macroeconomic
impacts of a carbon tax in China as the
policy question for the simulations. The study used
Monte Carlo experiments for the parameter uncertainty
propagation and employed an unconditional systematic
sensitivity analysis procedure to identify critically sensitive
elasticities. The analytical process adopted in this
paper is extensively used in model evaluations [15] .
1 CGE Model
The model developed here, integrated energyeconomy-environment
dynamic CGE (TEDCGE), is a
time recursive dynamic general equilibrium model,
which has been presented in detail [16] . Since the dynamic
mechanism has little impact on the elasticities’
influence on the CGE output and it is very time consuming
compared with the static CGE model, the static
version of the model was used to examine the elasticities’
uncertainties.
The TEDCGE model provides a description of the
China economy of 1997, the latest year for which the
China input/output is available. The model identifies
10 production sectors (agriculture, heavy industry,
light industry, transportation, construction, service
electricity, coal, oil, and natural gas, which are numbered
as 1 to 10, respectively), 10 consumption goods,
and 2 household groups distinguished by rural and urban
residents. The model operates by simulating the
operation of markets for factors, products, and foreign
Tsinghua Science and Technology, October 2006, 11(5): 617-624
exchange. The model is highly nonlinear, with equations
specifying supply and demand behavior across all
markets. The model pays particular attention to modeling
the energy sector and its linkages to the rest of the
economy. The energy use is disaggregated into coal, oil,
natural gas, and electricity in the model. Along with
capital, labour, and intermediate inputs, the four energy
inputs are regarded as the basic inputs into the production
function.
The carbon tax revenue from the sectoral energy
consumption is described by
E
10 10
∑∑
RC ( t) = tc ⋅ε⋅(1−fc ) ⋅θ ⋅VE ( t)
⋅ο
i= 1 j=
7
h= 1 j=
7
j j j ji j
(1)
The carbon tax revenue from residential energy use is
described by
2 10
RC C ( t) = ∑∑ tc ⋅εj ⋅θj ⋅CI hj( t)
⋅οj
(2)
VEji is the monetary consumption (RMB) of fuel j by
sector i, CIhj is the residential consumption (RMB) of
fuel j by household h, tc denotes the carbon tax rate in
terms of RMB/t, εj denotes the CO2 emission coefficient
of fuel j with the unit of t/TJ, fcj is the carbon
fixed rate of fuel j, θj is the conversion factor of fuel j
from a nominal output value to a physical term with the
unit of TJ/RMB, and οj is the oxidation rate of fuel j.
The CO2 emissions are calculated for each sector by
means of the sectoral fuel consumption and the corresponding
emission coefficient. Since the fuel consumption
is in terms of the corresponding nominal output
rather than directly as the physical units in the model,
the sectoral real fuel consumption is first translated
into physical terms, using a fuel-specific technical
conversion factor, and then further converted into CO2
based on the fuel-specific emission coefficient. The
fuel used by different sectors is assumed to have the
same quality, implying equivalent emission factors, in
terms of tons of CO2 per unit of fuel consumption, for
a fuel used in production, households, or government.
A carbon tax is incorporated into the model as a means
of achieving an assumed carbon reduction target. The
tax applies to the consumption of primary fuels only;
that is, the energy sectors only pay the carbon tax on
their own use of fuels. The carbon tax revenue is recycled
to the economy in several alternative ways, ensuring
that the total government revenue is neutral to the
carbon tax.