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A critical point for the results of any modelling is the definition of the
baseline (or reference or business-as-usual) scenario. The SRES (Nakicenovic
et al., 2000) explores multiple scenarios using six models and identifies 40
scenarios divided into 6 scenario groups. As OECD (1998) points out, among the
key factors and assumptions underlying reference scenarios are:
Differences in the reference scenarios lead to differences in the effects of
mitigation policies. Most notably, a reference scenario with a high growth in
GHG emissions implies that all the mitigation scenarios associated with that
reference case may require much stronger policies to achieve stabilization.
Nevertheless, even if reference scenarios were exactly the same, there are
other reasons for changes in model results. Model specification and, more importantly,
differences in model parameters also play a significant role in determining
the results.
If any fuel becomes perfectly elastic in supply at a given price (i.e., the
backstop technology), the overall price of energy will be determined independently
of the level of demand, which will then become the critical determinant of mitigation
costs. Hence, the assumption of a backstop technology strongly determines mitigation
costs. Models without a backstop technology will tend to estimate higher economic
impacts of a carbon tax, because they rely completely on conventional fuels,
so that the tax rate has to rise indefinitely to keep carbon concentrations
constant, to offset the effects of economic growth.
The treatment of technology change is crucial in the macroeconomic modelling
of mitigation. The usual means of incorporating technical progress in CGE models
is through the use of time trends, as exogenous variables constant across sectors
and over time. These trends give the date of the solution. Technical progress
usually enters the models via two parameters: (i) autonomous energy efficiency
(AEEI) (if technical progress produces savings of energy, then the value share
of energy of total costs will be reduced); and (ii) as changes in total factor
productivity. The implication of this treatment is that technological progress
in the models is assumed to be invariant to the mitigation policies being considered.
If in fact the policies lead to improvements in technology, then the costs may
be lower then the models suggest.
In assessing the effects of mitigation, estimations of price-induced substitution
possibilities between fuels and between aggregate energy and other inputs can
be crucial for model outcomes. All such substitutions become greater as the
time for adjustment increases. The problem is that estimates of substitution
elasticities are usually highly sensitive to model specification and choice
of sample period. There is little agreement on the order of magnitude of some
of the substitution elasticities, or even whether they should be positive or
negative, e.g., there is debate whether capital and energy are complements or
substitutes. If energy and capital are complements, then increasing the price
of energy will reduce the demand in production for both energy and capital,
reducing both investment and growth. Most CGE models consider very different
possibilities of substitution, for example WW, Global 2100, and Nordhaus’s
DICE/RICE models assume capital and labour as substitutes, while GREEN assumes
capital and energy as direct substitutes.
There are many different products, skills, equipment, and production processes;
many important features are missed when they are necessarily lumped into composite
variables and functions. A basic difference among models and their results is
the level of aggregation. Indeed, in practice, different goods have different
energy requirements in production, and therefore any changes in consumption
and production patterns will affect them differently. Hence, a highly aggregated
model will miss some potentially major interactions between output and energy
use, which is precisely the purpose of the analysis. For example, sectoral disaggregation
allows the modelling of a shift towards less energy-intensive sectors, which
might reduce the share of energy in total inputs. In the same way, when a carbon
tax is introduced, it could reduce the estimated costs of abatement by allowing
substitution effects of energy-intensive goods by less energy-intensive goods.
Constant returns to scale represent a common assumption on the economic modelling
of climate change. However, in practice, economies of scale seem to be the rule
rather than the exception. Indeed, there are several reasons for economies of
scale, see Pratten (1988), and Buchanan and Yoon (1994). For example, many electricity-generating
stations benefit from economies of scale, utilizing a common pool of resources
including fuel supply, equipment maintenance, voltage transformers, and connection
to the grid. In spite of this fact, the impact of the effects of increasing
returns and imperfect competition (IC) in the modelling of climate-change strategies
has consistently been neglected in the literature. Most of the global models,
if not all, assume explicitly perfect competition, for example, see DICE/RICE,
G-Cubed, Global 2100, GREEN, GTEM, WorldScan, and WW.
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