1
2003-01-07
CGE Modeling of Environmental Policy and Resource Management
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Prof. Lars Bergman and Prof. Magnus Henrekson)
Stockholm School of Economics
Department of Economics
Box 6501 (Sveavaegen 65)
SE-113 83 Stockholm, Sweden
1. Introduction
General equilibrium theory is a formalization of the simple but fundamental observation that
markets in real world economies are mutually interdependent. Theoretical general equilibrium
analyses have provided important insights about factors and mechanisms that determine
relative prices and the allocation of resources within and between market economies. As
witnessed by, for instance, Debreu (1959) general equilibrium theory has reached a very high
level of rigor and elegance. However, most contributions to general equilibrium theory have
focused on the allocation of private goods and privately owned resources. The prime
exception is Mäler (1973) who, inspired by Ayres and Kneese (1969), extended the general
equilibrium framework to encompass externalities and environmental resources with public
goods characteristics.
Computable General Equilibrium (CGE)2 modeling is an attempt to use general equilibrium
theory as an operational tool in empirically oriented analyses of resource allocation and
income distribution issues in market economies. The first CGE model was presented in
Johansen (1960), and with the development of fast computers and suitable software a large
number of CGE models has been developed and used for policy analysis. The applications
include analyses of major tax reforms, changes in trade policy regimes, economic integration,
agricultural policies and energy policies. A number of CGE models have been designed to
elucidate various policy issues in developing countries3.
Since the beginning of the 1990´s CGE modeling has also become a widely used tool for
analysis of environmental policy and natural resource management issues. The purpose of this
chapter is to review this branch of CGE modeling. The aim is to elucidate the modeling
approaches adopted and the policy and resource management issues dealt with in
“environmental” CGE models. In addition some specific problems in environmental CGE
modeling will be discussed. A number of specific models will be referred to, but the chapter is
1
Professor of economics at the Stockholm School of Economics, Stockholm, Sweden. Financial support from
the National Swedish Energy Administration, as well as research assistance by Martin Hill and Charlotte
Nilsson, is gratefully acknowledged. The author is grateful for comments by Eirik Amundsen, Martin Hill and
the editors of the Handbook on an earlier version, but is solely responsible for all remaining errors and mistakes.
2
Sometimes this class of numerical economic models is called Applied General Equilibrium (AGE) models.
However, as theoretical models of specific classes of economic problems, for instance international trade theory,
can be seen as applications of general equilibrium theory the label “computable” seems more appropriate than
the label “applied”.
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Robinson et.al. (1999) provide something like a handbook for building CGE models for policy analysis in
developing countries.
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far from an exhaustive survey of all CGE models intended for environmental resource
management or policy analysis4.
The exposition is organized in the following way. In the ensuing section the distinguishing
features, and the potential usefulness, of CGE models are briefly discussed. Section 3 is
devoted to a short summary of the history of CGE modeling. In section 4 some general
modeling issues in relation to environmental CGE models are discussed. In section 5 global
models are discussed, while section 6 is devoted to regional multi-country models. Then
single-country models are discussed in section 7 and 8, while some concluding remarks on
environmental CGE modeling are made in section 9.
2. What is a CGE model – and what is it good for?
There is no precise definition of a CGE model, but whenever this particular label is used the
model in question tends to have certain specific features. A very basic one is that it is a multisector model based on real world data of one or several national economies. However, while
general equilibrium theory is concerned with the interactions of large numbers of individual
households and firms most CGE models are rather aggregated. Thus, in a typical CGE model
there is only one or possibly a few “households”, while the number of production sectors
generally is in the interval 5 – 50. It is a matter of taste whether numerical models with only a
couple of sectors should be denoted CGE models5.
In general the technology is assumed to exhibit constant returns to scale, and preferences are
assumed to be homothetic. Utility and profit maximization behavior on the part of households
and firms is generally assumed, and excess demand functions are homogenous of degree zero
in prices and satisfy Walras´ law. Moreover, product and factor markets are assumed to be
competitive, and relative prices flexible enough to simultaneously clear all product and factor
markets. A key difference compared to Leontief´s input-output model is that in a typical CGE
model the technological coefficients are flexible and determined by relative prices.
CGE models almost always are focused on the real side of the economy and thus do not
include markets for financial assets. This is one of several differences between CGE models
and the numerical models based on microeconomic theory that increasingly are used in
macroeconomics (see Ljungqvist and Sargent (2000))6. Consequently a typical CGE model
endogenously determines relative product and factor prices and the real exchange rate, but
cannot determine nominal prices and the nominal exchange rate. This means that CGE models
generally are aimed at elucidating equilibrium resource allocations and growth paths rather
than business cycle or disequilibria phenomena. In particular CGE models are aimed at
quantifying the impact of specific policies on the equilibrium allocation of resources and
relative prices of goods and factors.
Categories of CGE models
In spite of these basic similarities there are also significant differences between individual
CGE models, and a number of distinct categories of CGE models can be distinguished. While
several classification alternatives can be envisaged the distinction between static and dynamic
4
Se Conrad (1999) for a recent survey.
One-sector numerical models such as the climate-change models DICE (Nordhaus (1994)) and RICE (Nordhaus
and Yang (1996)), however, should not be classified as CGE models. Yet there is reason to discuss these models
in a survey of environmental CGE modeling.
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Other features found in these but not in standard CGE models are heterogeneous agents and incomplete
markets.
5
3
models seems to be appropriate for a broad classification of modeling approaches. However,
there is a slight ambiguity with respect to the precise meaning of “dynamic” in this context. It
is obvious that models in which forward looking behavior on the part of households and firms
is assumed and stock accumulation relations are explicitly included should be denoted
“dynamic”7. But several static CGE models are used for multi-period analyses. Thus solutions
are obtained for each one of a number of consecutive years, and the solution for an individual
year t is used to define the stock of capital and other relevant assets available in year t+1. As
the model is static the agents are implicitly assumed have myopic expectations, i.e. to base
resource allocation decisions entirely on current conditions. In the following I will denote
these models “quasi-dynamic”.
In addition to the static-dynamic dimension it is useful to distinguish between single-country,
multi-country and global models. Single-country models tend to be more detailed in terms of
sectors and household types, and they are in general used for analyses of country-specific
policy issues and proposals. Multi-country and global models, on the other hand, tend to have
less sector detail and to be designed for analysis of proposed multi-lateral policies such as
free-trade agreements. In the case of environmental CGE models the multi-country and global
models in most cases are designed for analysis of trans-boundary pollution problems.
Needless to say the models within each one of these categories can differ in many ways. In
particular they may differ with respect to the number of production sectors, the number of
primary factors and the specification of international trade relations.
CGE models and environmental policy analysis
As an initial observation it should be noted that in general there is a case for using a CGE
model if proposed policy measures, or other expected changes in exogenous conditions, are
likely to have general equilibrium effects. However, far from all policy measures related to
environmental and natural resource issues are likely to have general equilibrium effects. Some
environmental problems are local and site-specific. This is the case for air quality and noise
problems in urban areas and in the vicinity of major industrial installations. Other
environmental problems are related to specific substances, such as CFCs, that are used only in
a few industrial processes or products, or relatively easily can be replaced by other
substances. Although measures to solve these environmental problems may be quite costly for
some firms and households, the repercussions to the rest of the economy often are small or
close to non-existent.
However, there are indeed major environmental problems with a much wider geographic and
economic scope, and calling for measures with potentially quite significant effects on the
allocation of resources in the entire national, or even global, economy. “Acid rain”, which is
related to emissions of sulfur and nitrogen oxides, is one example. The prime example,
however, is “climate change”, which is related to emissions of carbon dioxide and other socalled greenhouse gases8. In both of these cases there is a strong link between the use of
energy and the emissions of pollutants. Moreover, in both cases very significant emission
reductions are considered to be necessary in order to protect the environment. Not
surprisingly CGE models are widely used for evaluation of policies related to climate change
and acid rain issues.
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8
A very nice introduction to this class of CGE models is given in Devarajan and Go (1998).
See the Handbook chapter by Charles Kolstad and Michael Toman.
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CGE models focused on climate change or acid rain problems basically deal with externalities
and policies aimed at internalizing externalities. However, environmental problems may also
reflect ill-defined property rights, badly functioning capital or insurance markets or some
other kind of market failure leading to poor management of natural resources and losses of
environmental amenities. Thus in economies highly dependent on natural resources like
forests, fisheries, agricultural land or grazing land changes in the natural resource
management regime may have economy-wide effects, and appropriately designed CGE
models may be able to elucidate and quantify these effects. However, CGE models designed
for analysis of this type of natural resource management issues are likely to differ
substantially in many respects from CGE models designed for analysis of problems related to
externalities. The two types of CGE models are also likely to be used in quite different
settings.
In the following I will treat each category as a separate type of environmental CGE model.
Lacking a better terminology the first category will be called “Externality CGE Models”,
while the second will be called “Resource Management CGE Models”. It should immediately
be pointed out, however, that in terms of numbers the “Externality CGE Models” completely
dominate the field of environmental CGE modeling.
What are CGE models good for?
Most authors in the field would probably claim that a CGE model is an appropriate substitute
for an analytical general equilibrium model whenever the size and complexity, in terms of the
number of households and production sectors or pre-existing taxes and other distortions, make
such a model mathematically intractable. They would probably also claim that a CGE model
is useful whenever the magnitude, and thus not only the sign, of the impact of changes in
exogenous conditions on key economic variables are to be estimated. Needless to say most
evaluations of policy proposals have to be concerned about the magnitude of the impacts of
proposed policy measures, and the effects usually have to be estimated on a relatively detailed
sector level. Thus in many instances there are a strong case for using multi-sector numerical
models for policy analysis. Whether a specific CGE model, or CGE models in general, can
satisfy this need is of course a slightly different issue.
CGE models obviously rest upon strong assumptions about optimizing behavior, competitive
markets, and flexible relative prices. In addition lack of data usually prohibits econometric
estimation of key supply and demand parameters. In view of this the validity and usefulness
for policy evaluation of the results generated by CGE models might be, and often is, seriously
questioned. However, there is no general answer to the question about what CGE models are
good for. The usefulness of a carefully designed and implemented CGE model depends on
what it is intended for and what the alternatives are.
A CGE model of a complex real-world economy may be useful simply because it can help the
analyst to identify general equilibrium effects of changes in exogenous conditions that
initially were not obvious. This is the case even if key parameters of the model are quite
uncertain. Moreover, even if uncertainty about the numerical values of key parameters makes
the magnitude of computed effects of policy changes uncertain, the analyst may be able to
safely conclude that the effects in question are “small” or “big”. This is particularly the case
as the computational capacity of modern computers has made it possible to carry out very
extensive sensitivity analyses, and thus to find out how uncertainty about parameter values
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and structural aspects of the model affect the results and conclusions of the analysis 9.
Sometimes CGE model results may seem counter-intuitive and in the process of explaining
such results the modeler gains deeper insights into the interdependencies in the economy.
3. The history of CGE modeling10
The current literature on CGE modeling and economic analyses based on CGE models is vast.
It has developed from three quite distinct origins, each one associated with the contributions
of a particular author. The three authors are Leif Johansen, Herbert Scarf and Dale W.
Jorgenson. In this section I will briefly discuss the contributions of these authors and how
they have influenced the development of CGE modeling. I will also briefly comment on the
impact of increasingly efficient computers and software on the development of CGE
modeling, and close the section with a brief account of the origins of environmental CGE
modeling.
Leif Johansen and the MSG model
In his dissertation A multi-sectoral study of economic growth (1960), the Norwegian
economist Leif Johansen presented a numerical model that soon became known as the “MSG
model”. This model, which is generally seen as the first CGE model, was primarily intended
to be a tool for long term economic forecasting and economic policy evaluation. In the
original version there were 20 production sectors and one aggregated household sector. Public
consumption, net investments and exports were exogenously determined. Johansen saw the
MSG-model as an extended version of an input-output model. Thus, keeping the fixed input
coefficients for intermediate inputs, Johansen added value-added production functions and
factor markets where market-clearing prices for labor and capital were determined.
Although the MSG-model had an obvious flavor of Walrasian general equilibrium theory, it
also contained what seemed to be ad hoc assumptions about the determination of wages and
the rates of return on capital. Thus, although both labor and capital were entirely mobile
across sectors, there were equilibrium inter-sector differences in wages and rates of return on
capital. These deviations from Walrasian general equilibrium theory were motivated by the
existence of factors and conditions not explicitly dealt with in the model but likely to have an
impact on the sectoral development of the economy. Among the factors and conditions
mentioned by Johansen were persistent inter-sector differences with respect to the
composition of the labor force, working conditions, uncertainty and the degree of
monopolization of product markets. In view of these conditions a model entirely based on
Walrasian general equilibrium theory was not considered appropriate. Instead the MSG-model
was intended to be an approximation of a complex but largely unknown “true” model.
The MSG model soon became a key instrument for long term economic planning and
forecasting in Norway, and it has been extended in several stages and directions (see Førsund,
Hoel and Longva (1985). In particular a considerably more elaborated treatment of factor
substitution and energy demand has been incorporated, and a recent version, MSG-EE, is
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Leif Johansen, whose so-called MSG model is generally regarded as the first CGE model, commented upon the
usefulness of his model in the following way: “The data and the quantitative analysis do serve the purpose of
illustrating the method and the model. But, at the same time, if I were required to make decisions and take
actions in connection with relationships covered by this study, I would (in the absence of more reliable results,
and without doing more work) rely to a great extent on the data and the results presented in the following
chapters. Thus, the quantitative analysis does not solely serve the purpose of illustrating a method. I do believe
that the numerical results also give a rough description of some important economic relationships in the
Norwegian reality” (Johansen (1960)).
10
This section is partly based on Bergman (1990).
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especially designed for analysis of issues related to energy use and environmental pollution
(Alfsen et.al. (1996)). It was also the role model for ORANI (see Dixon et.al.(1982)), which is
a very elaborated CGE model of the Australian economy and often referred to as a “Johansen
model”11. The MSG-model also had an influence on the design of CGE models of developing
countries (see for instance Adelman and Robinson (1982)).
Herber Scarf and Scarf´s algorithm
Herbert Scarf´s famous algorithm for computing a Walrasian general equilibrium (Scarf
(1967)) was another point of departure for the development of CGE modeling. Using Scarf´s
algorithm John Shoven and John Whalley proved the existence of and designed a
computational procedure for finding a general equilibrium with taxes (Shoven and Whalley
(1983)). Together with early work on a two-sector model by Arnold Harberger (Harberger
(1962)) this inspired a series of analyses of tax and trade policy issues within the frame of
Walrasian and Heckscher-Ohlin general equilibrium models. A contribution in the same spirit,
but focused on international trade and resource allocation issues in a small open economy, is
Norman and Haaland (1987). An early survey is found in Shoven and Whalley (1984), and a
more textbook-like one in Shoven and Whalley (1992).
In contrast to Johansen´s MSG-model the models developed within the Scarf-ShovenWhalley tradition were firmly rooted in Walrasian general equilibrium theory. To some extent
the purpose of the modeling was to “put numbers on the theory”. While Johansen obviously
was very concerned about the ability of the model to approximately reflect real world
conditions, authors in the Scarf-Shoven-Whalley tradition have stressed transparency and
consistency with basic economic theory. Moreover, while Johansen focused on economic
growth and long-term structural change, most authors in the Scarf-Shoven-Whalley tradition
have had a static welfare economic perspective and focused on the efficiency and
distributional effects of various economic policy measures.
Dale W. Jorgenson and econometric general equilibrium modeling
Dale W. Jorgenson has made several contributions to CGE modeling, but the most unique of
these is the systematic use of econometric methods for parameter estimation. This is in sharp
contrast to most other CGE models where supply and demand function parameters are
estimated with simple calibration techniques12. The development of econometric general
equilibrium modeling was made possible by significant contributions by Jorgenson (and coauthors) to production and utility analysis, econometrics and national accounting (see
Jorgenson (1998)). An early user of Jorgenson’s approach to CGE modeling was Hazilla and
Kopp (1990).
Jorgenson´s approach to CGE modeling to some extent combines the Johansen tradition and
the Scarf-Shoven-Whalley tradition. Thus, as in Johansen´s work there is a focus on capital
accumulation and economic growth. However, while Johansen could only compute the rates
of change of key economic variables at a specific point in time, Jorgenson´s analyses are
based on a fully dynamic model (of the US economy). Like the models in the Scarf-ShovenWhalley tradition Jorgenson´s models are firmly rooted in neoclassical economic theory and
have been used for analyses of the welfare effects of various forms of taxation. But while the
static models in the Scarf-Shoven-Whalley tradition were focused on reallocation effects,
Jorgenson´s dynamic models were focused on growth effects of various tax policies.
11
It should be noted, however, that this label often was motivated by the fact that ORANI, like the MSG-model,
was solved on the basis of a linearization procedure.
12
For a discussion of calibration techniques, see Whalley and Mansur (1984).
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Computers and software
Needless to say the development of CGE modeling would not have been possible without the
dramatic development of fast computers and suitable software. In the early days of CGE
modeling lack of sufficient computer capacity put serious constraints on the size and
specification of CGE models, and lack of user-friendly software made CGE modeling a field
for specialists in numerical methods. Computer codes were model-specific and could not
easily be used by other modelers. Moreover, sensitivity analyses to evaluate the uncertainty
about parameter values were time-consuming.
A major change came with the introduction of GAMS (General Algebraic Modeling System,
Brooke et. al. (1988)), which allowed non-specialists in numerical methods to design and
solve Walrasian models. More efficient computers made it possible to solve models with
more sectors, and to take the first steps towards dynamic CGE modeling. It also made
extensive sensitivity analysis feasible. As a result the use of CGE models expanded rapidly.
The recent developments of GAMS/PATH (Ferris and Munson (2000)13) have made it easy to
solve dynamic models with a relatively large number of sectors at a low cost in terms of time
and money. This means that CGE modeling gradually has become an accessible tool for
applied economics, and “solution time” is no longer an issue for CGE modelers. Instead it is a
typical feature of modern CGE studies that a very extensive sensitivity analysis, in which the
model is solved for several thousands of randomly selected combinations of values of the
uncertain parameters, is carried out.
Environmental CGE modeling
In connection with the Energy Policy Project in the early 1970´s, summarized in the volume
A Time to Choose (Ford Foundation (1974)), Hudson and Jorgenson developed an
econometric CGE model for energy policy analysis (see Hudson and Jorgenson (1975)). This
turned out to be the first of a large number of models designed for analysis of energy policy
issues in the wake of the oil price increases in 1973 and 1979. However, most of these models
were energy sector models in which the rest of the economy was represented by an
exogenously determined rate of growth of energy demand. A well-known exception is Alan
Manne´s so called ETA-MACRO model in which a detailed energy technology assessment
model was linked to a neoclassical one-sector model of the rest of the economy (see Manne
(1977)).
However, in the beginning of the 1990´s the focus shifted from problems associated with the
supply of energy to the external effects associated with the use of energy, particularly fossil
fuels. One concern was acid rain, but the prime concern was climate change caused by
emissions of carbon dioxide. Many of the energy models could easily be redesigned for
analysis of carbon taxation and other types of climate policies. In addition a new set of CGE
models, designed for climate policy analysis, was developed. One of the most well known is
the GREEN model developed at the OECD secretariat (see Burniaux et.al. (1992)) for
analysis of climate policy issues at a global scale.
At the same time a number of single-country models for environmental policy and resource
management analysis in different individual countries were developed. Thus, Hazilla and
Kopp (1990) estimated the social cost of environmental quality regulations using a CGE
model of the US economy. Bergman (1990) estimated the social cost of phasing out nuclear
13
See also www.gams.com.
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power in the presence of SO2, NOx and CO2 emission constraints, using a CGE model of the
Swedish economy. In the following sections I will discuss the approaches adopted, the issues
addressed, and some of the conclusions that have been drawn in environmental CGE
modeling.
4. Some General Issues in Environmental CGE modeling
Most environmental CGE models are designed to elucidate various aspects of climate change
or, in some cases, acid rain policies. To a large extent climate change and acid rain problems
are caused by emissions from the combustion of fossil fuels. In both cases the environmental
damage depends on the accumulated stock rather than the current flow of pollutants.
Moreover, the stocks of the pollutants in question accumulate slowly so there is a
considerable time lag, particularly in the case of climate change, between the emission of
pollutants and the resulting impact on the environment. These observations have several
implications for the design of CGE models intended for policy analysis.
One obvious implication is that the model should have an elaborated treatment of the supply
and demand for energy. In particular it should have an elaborated treatment of the possibilities
to substitute other forms of energy, or other factors of production, for fossil fuels. It should
also have an explicit treatment of the relation between the use of fossil fuels and the emission
of various pollutants.
Another implication of the nature of the environmental problems in question is that the model
should take stock accumulation over very long periods of time into account. While the time
horizon is one or two decades into the future in typical CGE model analyses of tax or trade
policies, the relevant time horizon in climate change policy analysis is several decades or even
a century or two into the future. The key modeling problem with such a distant time horizon is
that the potential impact of new technologies is quite significant.
A third implication for CGE modeling is related to the fact that the benefits of environmental
policy measures are “non-economic”, i.e. that they come in the form of better environmental
quality. Thus a CGE model intended for cost-benefit analyses of environmental policies
should have an “environmental module”, i.e. a module in which the environmental benefits of
reduced pollution are quantified and converted into a monetary measure of environmental
benefits. The environmental module could also include “feed-back” mechanisms, i.e. a submodel of the impact of environmental improvement (or deterioration) on factor productivity
and household utility of environmental services.
It is obvious that environmental CGE modeling is quite a demanding task, and that the
modeler is bound to encounter a number of intricate modeling issues. It is also obvious that
environmental CGE models should be dynamic or at least quasi-dynamic. The purpose of this
section is to briefly discuss some commonly adopted modeling approaches in this particular
field of CGE modeling.
Production sectors
CGE models intended to elucidate climate change or acid rain policies need to have an
elaborated treatment of the demand for fossil fuels. This has certain implications for the
specification of production functions, but also for the production sector division of the model.
In particular there is a case for treating the fossil fuel intensive sectors as separate production
sectors. For this reason a typical “externality” CGE model has separate production sectors for
electricity, transportation, metals, pulp and paper, and chemicals, while the rest of the
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economy may be aggregated into only a few production sectors. When an elaborated
environmental module is incorporated, however, sectors that are affected by climate change
(for instance agriculture) or acid rain (for instance forestry) are treated as separate production
sectors (see Nordhaus (1994) and Hill (2001)).
However, production sectors that are fossil fuel intensive may consist of sub-sectors that
differ significantly from this point of view. This is clearly the case for the electricity sector
where the output can be produced both by fossil fuel intensive technologies such as coal and
oil power, and fossil fuel free technologies such as hydroelectric power and nuclear power.
Thus part of the electricity sector response to climate policy measures is to change the mix of
different technologies used for power production. In order to capture these substitution
possibilities in a realistic way the technological constraints of the electricity sector, or the
entire energy sector, is sometimes represented by a separate sub-model rather than by a
standard neoclassical production function.
The common origin of the energy sector sub-models is the linear activity models used for
planning and technology assessment in the energy sector. An elaborated example of a global
model in this tradition is Nordhaus (1974). The key feature of these models, often called
“bottom-up” models, is that individual technologies for energy extraction, conversion and
transportation are distinguished. Among other things this modeling approach makes it easy to
incorporate new technologies, such as wind power, with factor input proportions that radically
differ from existing technologies. On the other hand the linear energy sector model has to be
integrated with “neoclassical” models of the non-energy sectors. An early example of an
integrated “bottom-up” energy sector model and a neoclassical “rest-of-the-economy” model
is Alan Manne´s above mentioned ETA-MACRO model. Other examples are Jorgenson
(1982) and Lundgren (1985),
The transportation sector is similar to the electricity sector in the sense that there are several
different modes of transportation that exhibit very different properties with respect to the use
of fossil fuels per unit of output. The long run response to climate policy measures affecting
the transportation sector are likely to include adjustments and substitutions both on the supply
and the demand side. However, the modeling of the transportation sector is usually not very
elaborated in CGE models intended for environmental policy analysis. The different modes of
transportation are often not explicitly distinguished, and there is no measure of the
transportation services produced by ordinary firms and households.
Moreover, while the demand for transportation services obviously reflects the location of
production and consumption activities, most CGE models do not have a spatial dimension.
And while the choice between different modes of transportation to a large extent depends on
the amount of time that the user has to spend, time is usually not treated as a scarce factor in
CGE models. In addition the relative competitiveness of different modes of transportation to a
large extent depends on the transportation infrastructure, i.e. roads, railways, airports, etc.
Altogether this means that the CGE models developed so far have little to say about the
demand for and substitution between different modes of transportation. In order to account for
the relevant substitution opportunities some kind of “bottom up” approach might be needed.
Production functions
The sectoral production functions basically define substitution possibilities between explicitly
defined input factors. In CGE models focused on environmental policies related to climate
change or acid rain it is important to distinguish not only between capital, labor, non-energy
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intermediate inputs and energy, but also between fossil and non-fossil energy. Often it is also
convenient to distinguish between fuels and electricity. Thus the production function of a
representative production sector j in such a CGE model can be written
X j  f j (K j , L j , M j , Fj , E j ) ;
(1)
where X is gross output, K capital, L labor, M non-energy intermediate inputs, F fuels and E
electricity. In most cases F is an aggregate of various fossil and non-fossil fuels. In the
following non-energy intermediate inputs are denoted “materials”.
In some CGE models the production function fj(.), or rather its dual cost function, is assumed
to have a so called flexible form (translog or generalized Leontief) and the parameters are
econometrically estimated. The use of flexible functional forms is a way to circumvent the
strong assumptions about the elasticities of substitution between different pairs of inputs
implied by the standard production functions. To some extent these functional forms were
developed in order to properly deal with the substitutability of energy and other factors of
production in econometric general equilibrium models (see Jorgenson (1998a)). However,
lack of data often prevents econometric estimation of the sector cost functions. Instead the
elasticities of substitution between different inputs generally are “guesstimated”. This means
that both the nesting structure of the production functions and the adopted numerical values
are based on literature surveys of relevant econometric studies.
Thus, based on available external information about elasticities of factor substitutions the
technology in most CGE models is described by some kind of nested production function
structure in which CES (constant elasticity of substitution), Cobb-Douglas and Leontief
production functions are combined. The existing literature on econometric studies of
production does not lead to definite conclusions about the most appropriate nesting structure.
However, in most models fuels and electricity, i.e. F and E in the equation above, are
combined in a CES function with a relatively high elasticity of substitution. The input “fuels”
is often defined as a CES-aggregate of different types fossil and non-fossil fuels. The
elasticities of substitution between different types of fuels are usually taken to be relatively
high.
In the case of capital and energy the econometric evidence is conflicting. Some studies
indicate that capital and energy are substitutes at the relevant level of aggregation, while
others suggest that capital and energy are complements. However, most CGE models assume
that capital and energy are substitutes, although the elasticity of substitution between capital
and energy is generally taken to be quite low. The nesting structure may differ between
different models, but the structure of the sector production function (2) below can be found in
many CGE models intended for climate change or acid rain policy analysis.
X j  f j ( L j , M j , Q j ( K j , H j ( F j ( F j1 ,..., F jn ), E j ))
(2)
Thus fuels (F), which is an aggregate of n different types of fossil and non-fossil fuels, and
electricity (E) are combined in a CES aggregate that defines a composite energy good (H).
The composite energy input is combined with capital in a CES aggregate of capital-energy.
Then the composite capital-energy input Q is combined with labor (L) and materials (M). In
some models, however, capital and labor rather than capital and energy are combined.
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Emissions and abatement
In general the emissions of pollutant per unit of output can be reduced if the input of one, or
several, of the other inputs is increased. Thus the possibility to emit pollutants into the
environment can be seen as a kind of input in the production process, and it should be
possible to estimate the elasticity of substitution between emissions into the environment and
other input factors. Estimation of these substitution possibilities obviously requires that the
factor inputs and the emissions of pollutants can be appropriately measured. However, the
emissions of pollutants such as sulfur and nitrogen oxides and carbon dioxide generally are
not measured directly, and in many cases direct measurement is difficult and costly. Instead
the emissions are estimated on the assumption that they are proportional to the use of various
types of fossil fuels14.
This assumption implies that emission reductions can be brought about only by reductions of
the consumption of fossil fuels or by changes in the composition of fossil fuel consumption.
In practice inter-fuel substitutions can lead to quite significant emission reductions. For
instance, the combustion of natural gas gives rise to less emissions of carbon dioxide per unit
of energy than coal. Thus substitution of natural gas for coal ceteris paribus reduces the
emissions of carbon dioxide at give output levels.
However, the emissions of sulfur and nitrogen oxides can be reduced not only by output
reductions and by fuel switching. There are also direct abatement possibilities. In order to
capture abatement measures some environmental CGE models incorporate abatement cost
functions, usually estimated on the basis of generic rather than site-specific engineering data.
In representative CGE models the abatement activity is assumed to depend on economic
incentives so that abatement takes place whenever the marginal cost of abatement is less than
or equal to the cost to the firm, or household, of marginal emissions. The marginal cost of
emission, in turn, is determined by charges on emissions or by the price of emission permits
(see for instance Hill (2001)). From an institutional point of view it is assumed that
specialized firms are supplying abatement services to industries obliged to comply with
emission constraints.
Technological change
In the short and medium term substitution between inputs is a key mechanism in the
adjustment to various environmental policy measures. This is why the elasticity of
substitution parameters of the sector production functions are so important in CGE models
intended for environmental policy analysis. However, as was mentioned above the time
horizon in environmental policy analyses often extends several decades or even a century or
two into the future. Thus the development and implementation of new technologies might
affect emissions and other impacts on the environment much more than substitution between
currently existing technologies. Expectations about future relative prices, taxes and
regulations clearly have an impact on the speed and direction of technological development.
The links between past and current conditions, the formation of expectations about the future
and the development and implementation of new technologies are not well understood.
Nordhaus (1997) discusses induced technical change in the context of the optimal timing of
abatement measures. Goulder and Schneider (1999) introduce a market for R&D services in a
CGE model. As the R&D services can be used as a substitute for other factors of production,
this means that technological change in effect becomes an endogenous process.
14
Even if the emissions of sulfur and nitrogen oxides could be independently measured, the lack of uniform
prices (or emission charges) would cause estimation problems.
12
However, in most CGE models technological change is an exogenous factor making the total
factor productivity an increasing function of time. In CGE models intended for energy or
environmental policy analysis it is quite common to incorporate specific assumptions about
“autonomous energy efficiency improvements” (AEEI). The AEEI-factor is assumed to be
exogenously determined and to reflect all factors, except current price-induced substitutions,
that make the input of energy in a given production sector grow slower than the output of that
sector. The numerical value of the AEEI-factor is often assumed to be in the interval 0-2
percent per annum.
Needless to say an AEEI-factor at the level of one percent per annum or more has a very
significant impact on energy use, and thus on emissions, in a 50-100 years time perspective.
Thus the assumptions made about the numerical value of AEEI in key production sectors may
have a very significant impact on the results of the whole modeling exercise. As the CGE
model is supposed to elucidate the impact of changes in relative prices on the allocation of
resources in the economy, it is of course somewhat disturbing to be forced to treat
technological change as an exogenous factor. What is even more disturbing is that the
assumptions about positive AEEI-factors seem to rest on somewhat uncertain empirical
grounds. Thus Hogan and Jorgenson (1991) show that there is no clear evidence of
autonomous energy efficiency increases if price-induced substitution effects are taken into
account.
In environmental CGE models with a “bottom-up” description of the technology of the energy
sector it is quite common to incorporate a “back-stop” technology that becomes available
some time in the future. The back-stop technology is typically based on non-exhaustible
resources. The date when the back-stop technology is available is exogenously determined,
but whether the back-stop technology will be used at that date, at some later date or not at all
is endogenously determined in the model.
Environmental benefits
One way of using an environmental CGE model is to focus on the cost of specific
environmental policy measures (such as a ban on the use of nuclear power), or on the cost of
attaining a specific environmental policy goal (such as reducing the total sulfur emissions by
30 percent). However, if the model is to be used for evaluation of policies it should be capable
of quantifying both the costs and the benefits of the policies in question. This means that the
CGE model needs to have an “environmental module” in which the environmental benefits of
reduced pollution are quantified and expressed in monetary units. From a purely theoretical
point of view the development of such an environmental module is fairly straightforward. In
reality a lack of relevant and reliable data makes it an almost impossible task.
What is needed in order to construct a “benefit function” can be divided into two sets of
functional relationships. The first is a set of physical damage functions that convert emissions
and other environmental effects of production and consumption into measures of physical
environmental damage (in the case of increased emissions etc.) or improvements (in the case
of reduced emissions etc.). The estimation of such functions is obviously outside the realm of
economics, and it does not seem to be a prime concern for natural scientists. The second is a
set of functions defining the value, in monetary units, of changes in the physical
characteristics of the environment. From an economic point of view these changes can take
two different, but not mutually exclusive, forms (see the Handbook chapter by Nancy
Bockstael and A. Myrick Freeman III).
13
One is that the physical changes in the environment affect the supply of environmental
services that are directly “consumed” by the households. In terms of a CGE model this means
that changes in environmental quality affect welfare directly via the utility functions of the
household(s). Obvious examples of such services are clean air and water. In these and most
other cases the environmental services in question can be characterized as public goods. Thus
the relevant values cannot be determined on the basis of regular market prices. Instead the
valuation has to be based on some estimate of the willingness to pay for the environmental
services in question.
The other alternative is that the changes in the physical characteristics of the environment
only affect the productivity in sectors producing “ordinary” goods and services that are traded
on regular markets (see the Handbook chapter by Kenneth McConnell and Nancy Bockstael).
In terms of a CGE model this means that changes in environmental quality affect welfare
indirectly via the cost of producing ordinary goods and services. The impact of environmental
damage on the cost of ordinary goods and services is an example of what is sometimes called
“feed-back effects”15. One example of such a feedback effect is the reduction of factor
productivity in forestry that may be the result of acid deposition caused by emissions of
sulfur. In this case the cost of the environmental damage can be estimated on the basis of
regular market prices for forest products.
Many environmental CGE models lack a module for environmental benefit calculation, or
have an environmental module that is based on shaky data and/or very bold assumptions.
Basically two types of approaches have been adopted. One is to focus on feedback effects. An
elaborate example is Nordhaus (1994) in which an advanced climate model is used to estimate
feedback effects of the emissions of green-house gases. Examples of CGE models with
explicit feedback effects are Harrison et.al. (1989), Vennemo (1995) and Hill (2001). Another
approach is to assume that politically determined environmental goals, or international
agreements on emission reductions, represent an efficient trade-off between the relevant costs
and benefits. Given this assumption the parameters of an environmental benefit function can
be determined. The benefit function can then be used to evaluate other policy proposals. One
example of this approach is Whalley and Wigle (1992).
International trade in CGE models
The specification of international trade relations is an important aspect of all open-economy
CGE models, but seems particularly important in environmental CGE models. The primary
reason for this is that international relocation of economic activity is a key potential response
to unilateral environmental policy measures. It is beyond the scope of this chapter to discuss
specification issues in any detail, but a few words should be said about the treatment of
international trade in CGE models of open economies.
The natural point of departure then is the Heckscher-Ohlin model of a small open economy in
which the technology exhibits constant returns to scale, and the domestic producers are pricetakers on international markets for tradable goods. However, with n goods and m factors, and
n>m, the equilibrium output levels in such a model are positive in at most m sectors.
Moreover, a small change in a world market price, or a domestic tax rate, may reduce the
equilibrium output level in a given sector from a relatively large positive value to zero, or
from zero to a relatively large positive value. As most CGE models have many more sectors
15
Another type of feedback effect is the change in the demand for ordinary goods and services caused by
changes in environmental quality.
14
than factors this feature of models in the Heckscher-Ohlin tradition tends to produce rather
extreme and unrealistic patterns of specialization. This so-called overspecialization problem
has attracted a lot of attention in the CGE-modeling literature, and several “solutions” have
been proposed.
The most widely used approach is to adopt the “Armington assumption” (Armington (1969)),
which implies that goods with the same statistical classification but different countries of
origin are treated as non-perfect substitutes. The application of this idea in CGE models
amounts to defining domestically consumed goods as CES-aggregates of domestically
produced and imported goods with the same statistical classification. As a result 16 the import
of a given type of goods depends on the relation between the prices of imported and
domestically produced goods of that type. Moreover, if the same assumption is applied on the
rest of the world the producers of the small open economy will face relative-price dependent
export demand functions, and the terms of trade will depend on the volume of exports. This
means that the properties of CGE models based on the Armington assumption17 may differ
quite significantly from the properties of models based on standard Heckscher-Ohlin
assumptions.
Another widely adopted approach to the “overspecialization” problem in CGE models is to
retain the assumption about exogenously given terms of trade, while relative-price dependent
export supply functions are added. These functions usually are derived from constant
elasticity of transformation (CET) functions defining the output of a given sector as a
revenue-maximizing aggregate of goods for the domestic market and goods for foreign
markets. This means that if the price of, say, goods for foreign markets increase the
composition of domestic supply is shifted in the direction of more goods for foreign markets
and less for the domestic market. The magnitude of the response to changes in relative prices
depends on the elasticity of transformation between goods for the two types of markets.
Both the Armington assumption and the CET function approach prevent extreme
specialization patterns in CGE models with more tradable goods than factors. However, the
empirical basis for these approaches seems somewhat questionable. Product differentiation,
which is implied by both approaches, clearly is a real world fact, but the patterns of product
differentiation depend on market conditions and change over time. In CGE models employing
the Armington assumption or using the CET function approach, however, the current patterns
of product differentiation in effect are assumed to persist. This means that the models are
likely to underestimate the structural effects of long run changes in relative prices. As will be
discussed in some detail below this feature might be particularly important in CGE models
intended for analysis of environmental policies.
5. Global “Externality” CGE Models
During the 1990´s a number of global CGE models intended for analysis of climate change
policies were developed and used for policy analysis. The major field of application has been
evaluations of various aspects of the Kyoto protocol, i.e. the (not yet ratified) agreement to
16
The Armington assumption implies that the price of the domestically consumed composite of a given type of
goods is a linearly homogenous function of the prices of imported and domestically produced goods of that type.
By Shephard´s lemma the share of imports in the composite good is given by the partial derivative of the price
function with respect to the price of imports.
17
From a microeconomic point of view an Armington model of a small open economy is somewhat questionable
in the sense that the firms in a given sector collectively face a downward sloping export demand function, but
refrain from forming an export cartel and exploiting their market power on foreign markets. Harris (1984)
avoided this problem by modeling product markets as monopolistically competitive.
15
reduce the emissions of carbon dioxide and other greenhouse gases. In fact the commitments
by industrial countries under the Kyoto protocol seem to be the primary reason so many
global environmental CGE models were developed during the 1990´s. The purpose of this
section is to briefly present some of the most well known models, and to discuss some of the
results obtained from simulations with global “externality” models. In particular I will discuss
what we can learn from the models about the so-called “leakage” problem, i.e. the alleged
international relocation of emission-intensive production induced by unilateral climate policy
measures.
The models
The key characteristics of the selected models are summarized in Table 1. This is not a
complete list of all existing global CGE models for environmental policy analysis. However,
the collection of models included in the list should give a fairly complete account of the
modeling approaches, in terms regional and sector division and several other dimensions,
generally adopted in this field of CGE modeling. Moreover the list to some extent reflects the
continuing development of the several of the models. Thus MIT-EPPA is an upgraded and
extended version of GREEN. In the same way 12RT is an extended version of Global 2100,
and RICE is a regionalized version of DICE (Nordhaus (1994)).
A common feature of the models is that baseline GDP growth essentially is determined by the
assumptions made about aggregate savings, technological change and the growth of the labor
force in different regions of the world. This means that the emissions of green-house gases to
a large extent also is determined by these assumptions. However, the Kyoto commitments are
defined in terms of emission reductions in relation to a historical benchmark. Thus the
stringency of the imposed emission constraints, and to a large extent the cost of complying
with these constraints, in effect is determined by the assumptions about baseline economic
growth. For instance, in Manne and Richels (1992) the baseline growth assumptions imply
that China will grow faster than the world average, and increase its share of world GDP from
1.8 percent in 1988 to 22.1 percent in the year 2100. Under these conditions it turns out that
global emission reduction policies will be very costly, particularly if no emission reduction
measures are implemented in China. If China grows more slowly than the world average,
however, attaining the emission reduction targets will be considerably less costly.
Another common feature is that the models are used for simulations over periods that are long
enough to make resource depletion effects important. In order to capture depletion effects
Global 2100 and 12RT distinguish between two categories of oil and gas resources. Thus the
cost of extracting oil and gas from currently proven reserves is lower than the corresponding
cost for the remaining but still unproven stock of reserves. In GREEN coal reserves are
assumed to be (practically) infinite, while oil and gas are assumed to be exhaustible resources.
The cost of these resources is taken to depend on the initial levels of proven and unproven
reserves, the rate of reserve discovery, and the rate of extraction. Moreover the rate of reserve
discovery is assumed to depend on world oil and gas prices. Mechanisms that reflect
increasing cost of oil and gas as currently proven reserves are exhausted are also incorporated
in CRTM, IIAM and the G-Cubed model. However, in the models where a back-stop
technology is incorporated the long run cost of energy is capped by the cost of using the backstop technology for energy production.
The “leakage” issue
According to the Kyoto protocol the so-called Annex I countries, i.e. the high-income OECD
countries, should start reducing their carbon dioxide emissions before the countries in the rest
16
of the world. One possible effect of such a policy is “carbon leakage”, i.e. emission sources
migrate from abating to non-abating countries. The possibility of carbon leakage is a matter of
great concern in several countries, and it seems to be a major obstacle to unilateral emission
reduction policies. From a theoretical point of view it is obvious that unilateral action will
induce some “leakage”. The question is whether the leakage is quantitatively significant or
not. A global CGE model should be suitable tool for assessing the magnitude of carbon
leakage due to unilateral emission reduction policies.
Pezzey (1992) used the WW-model to estimate the leakage effects of unilateral European
Community and OECD carbon dioxide emission reduction policies. Assuming that the
emission target was 20 percent below the baseline level, the leakage turned out to be 70
percent. Thus, if OECD reduced emissions by 100 tons of carbon dioxide, the resulting
reduction of global emissions would only be 30 tons. Rutherford (1992) also finds that the
leakage is significant. In particular he finds that if OECD increases the emission reduction
target from 4 percent to 5 percent of current emissions, there would be no reduction at all of
global emissions. Thus, according to this particular study the “marginal” leakage effect is 100
percent!
However, other CGE-based studies have come to very different results concerning the leakage
effect. McKibbin and Wilcoxen (1995) studied unilateral emission abatement by the Annex I
countries in accordance with the Kyoto protocol, and found that the leakage effect was 6
percent. In experiments with GREEN the leakage effect was only 3.5 percent when the OECD
carbon dioxide emissions were stabilized at the 1990 level. In 12RT the leakage effect is
smaller than in the studies by Pezzey and Rutherford, but significantly bigger than in GREEN
and the study by McKibbon & Wilcoxen. Thus the estimated leakage effect in 12 RT when
Annex I countries act unilaterally is 35 percent.
In order to explain these big differences Manne and Martins (1994) made a systematic
comparison of 12RT and GREEN simulation results. They found that the leakage effect
reflected two main effects of climate policy measures. The first was due to the fact that a tax
on carbon dioxide emissions raised the cost of energy intensive production in the abating
countries. Thus the relative competitiveness of tradable energy intensive production in the
non-abating countries increased, and part of the production decrease in the abating countries
was compensated by increased production and net export in the non-abating countries. This
effect can be called the “relocation effect”. The second effect was due to the fact that a
reduction of energy consumption in the abating countries tends to reduce the world market
prices of oil and coal. As a result of the lower prices the consumption of energy, and the
emissions of carbon dioxide, increases in the non-abating countries. This effect can be called
the “rebound effect”.
The large difference between the two models in terms of the leakage effect turned out to
primarily depend on the relocation effect, which in turn depended on differences in the
treatment of international trade. In GREEN international trade is modeled in accordance with
the Armington assumption. This means that similar goods with different country of origin are
treated as imperfect substitutes, which tends to dampen the relocation effect. In 12RT, on the
other hand, non-energy goods with different country of origin are assumed to be homogenous,
which tend to make international trade flows sensitive to changes in relative cost conditions
between countries. These findings suggest that the specification of international trade in
global CGE models may have a significant impact on the results and thus deserves
considerable attention. However, as is demonstrated by McKibbin and Wilcoxen (1995) any
17
factor that makes structural change costly tends to reduce the leakage effect of unilateral
climate policies. In their case international relocation of capital is hampered by capital
installation costs in the non-abating countries.
Concluding remarks
A lot more can of course be said about the global models and the studies in which such
models have been used for analyses of environmental policy proposals. However, it suffices
to conclude that these models and studies to a large extent have formed the “common
wisdom” about the economic consequences of the policies suggested by the Kyoto protocol. It
could be added that the Kyoto protocol has created an ideal case for using CGE models: the
time horizon is far into the future so that short term adjustment problems can be neglected. At
the same time the proposed emission reductions are significant and clearly call for policy
measures that are likely to have general equilibrium effects both within and between countries
and regions.
6. Regional Multi-Country “Externality” CGE Models
A model in this category typically covers a region, such as the European Union, and consists
of sub-models of each one of the countries within that region. From an environmental policy
point of view regional models are suitable for analyses of regional environmental problems,
such as acid rain. Regional multi-country CGE models are also used for analyses of policy
proposals that imply coordination of the national policies within the region. One example is
analyses of the implications of the Kyoto protocol where the European Union, rather than the
individual member states, is a signatory party. In this section three representative models and
a special problem associated with this category of CGE models will be briefly discussed.
The models
The main features of the three models are summarized in Table 2. GEM-E3, where E3 stands
for Energy, Environment and Economy, is the result of a major project within the JOULE
program funded by the European Union. The GEM-E3 model has a quite detailed treatment
both of the energy sector and the emissions to the environment. Thus, within the “top-down”
energy sector four types of energy, namely electricity, coal, oil and gas, are distinguished.
Among other things this allows for a relatively detailed link between the consumption of
energy and the emissions to the environment. In GEM-E3 the emissions of five different
pollutants, CO2, SO2, NOx and PM (particulate matter), are distinguished, and abatement cost
functions for all pollutants except CO2 are included. In addition to “end of pipe” abatement
options possibilities to substitute less polluting forms of energy, and other factors of
production, for polluting forms of energy are included in the model.
The GEM-E3 also has a module in which emissions to the environment are converted into
damage to the ecosystem and to public health. In addition damage to materials is included.
However, no feedback-effects are included in GEM-E3. In the extended version of the HRWmodel, on the other hand, feedback effects are included. Thus the environmental module
distinguishes the emissions of CO2, SO2 and PM, and mortality and morbidity effects are
assumed to depend on the stocks and flows of these pollutants. Increased morbidity has both
direct welfare effects and feedback effects on the demand for “ordinary” goods and services.
Increased mortality is modeled as separable and turns out to be the most significant welfare
effect of emissions to the environment. The BFR, model, finally, only treats CO2 emissions,
and does not include environmental benefit functions or feedback effects.
18
Specific features and problems
Regional multi-country CGE models share many of the features of global CGE models. Both
types of models are often used for evaluations of unilateral vs. internationally coordinated
policies. As in global CGE models an elaborated treatment of international trade between the
individual countries is needed in a regional multi-country model. Data problems are usually
less severe in regional multi-country models than in global models, and problems associated
with aggregation of data for individual countries can in general be avoided. However, there is
a modeling problem that is unique to the regional multi-country models, namely the treatment
of “the rest of the world” (ROW).
In global models there is by definition no ROW, so the problem does not have to be
addressed. In single-country models there is clearly a ROW, but in general it is reasonable to
adopt the “small country assumption”. In other words it is assumed that world market prices
are given and unaffected by changes in the export, import and factor prices of the single
country in question. In regional multi-country models, however, the small country assumption
often is untenable. For instance, the European Union accounts for a very significant share of
the world trade, and it is likely that the changes the Union’s trade with the rest of the world
would influence world market prices. However, if the small country assumption is not
adopted some other type of “world closure” has to be included in the model.
The problem of “world closure”, i.e. the modeling of how ROW would act in response to the
actions of the countries explicitly included in the model, has been widely discussed in the
literature on CGE modeling. Early contributions are Whalley and Yeung (1982) and de Melo
and Robinson (1989). Koschel and Schmidt (1998) used the GEM-E3 model for an extensive
test of the closure rules suggested by Whalley & Yeung and de Melo & Robinson.
In the standard version of GEM-E3 it is assumed that the ROW export prices are given and
independent of the demand for ROW goods by the European Union. The ROW import
demand functions are modeled in accordance with the Armington assumption. Thus the ROW
demand for imports from the European Union countries depends on the ratio between
exogenously given ROW prices and endogenously determined European Union export prices,
and an exogenously given level of output in ROW. In various experiment versions of GEME3 alternative trade specifications were used. The key difference was that ROW prices were
assumed to depend on the quantity of ROW exports to the European Union. The conclusion
was that the assumptions about the behavior of ROW did influence the results for the
countries explicitly treated in the model. This is hardly surprising, and suggests that the only
satisfactory way to deal with the “world closure” problem is to extend the regional multicountry model to become a global model, albeit one with a less detailed treatment of ROW.
7. Single-country “Externality” CGE models
A large number of single-country environmental CGE models were developed during the
1990´s. Most were designed to elucidate environmental problems or policies that are specific
to the country in question. The most commonly studied environmental problem is emissions
to air that contribute to climate change or acid rain. Thus most single-country environmental
CGE models can be characterized as “externality” CGE models. However, there are also a
few single-country models that can be classified as “resource management CGE models”, i.e.
are designed to elucidate problems like depletion of natural resources and other natural
resource management issues. A selection of representative models is briefly described in
Table 3.
19
Single-country environmental CGE models can broadly be divided into three main categories.
The first consist of models that primarily are constructed and used for analyses of specific
theoretical issues. The prime example is CGE analyses aimed at testing the existence of a socalled “double dividend” (see the Handbook chapter by Anil Markandya for a detailed
discussion of this issue). Goulder et.al. (1997) and Bovenberg and de Moij (1994) both belong
to this category. The second category is CGE models that are constructed for testing of new
model features or modeling approaches, which, if the testing is successful, may be
incorporated in models intended for policy analysis. Vennemo (1995), where an approach to
incorporate feedback effects from the environment to the economy, belongs to this category.
Another example is Abler et.al. (1999) where the impact of parameter uncertainty on
simulation results is studied. The third is “multi-purpose” CGE models that are designed for
analyses of a wide range of economic and environmental policies. Harrison (1997) belongs to
this category. In the following the use of single-country CGE models for analysis of the
“double dividend” issue and some natural resource management issues will be briefly
discussed.
The double dividend issue.
The idea that revenues generated by emission taxes could be used to reduce distortionary
taxes, and thus produce benefits in addition to those resulting from reduced emissions, has
been widely discussed in relation to environmental policy in several countries. Goulder
(1995a) contributed to the discussion by defining three types of “double dividends”. The most
interesting was the “strong” double dividend which refers to a case where a revenue-neutral
substitution of an environmental tax for a representative distortionary tax would lead to a nonpositive welfare cost. In other words emission taxes could be welfare improving even if the
environmental benefits were small or even zero!
The existence of a strong double dividend seems to have been taken for granted by many
politicians. In particular replacement of part of the labor income tax with emission taxes has
been seen as an environmentally attractive way of increasing the demand for labor, and such
tax reforms have been proposed in several and implemented in some countries. Economists,
however, have been more skeptical and the issue has become subject to extensive research. To
a large extent this research has been based on CGE models.
The double dividend issue offers an ideal case for CGE modeling. It is not a matter of
studying the impact of an environmental tax in an economy without taxes, but in an economy
with an extensive system of distortionary taxes. Thus the existence of the strong double
dividend depends both on how the environmental tax interacts with other taxes, and on how
the revenues are recycled. Moreover, even if the sign of the double dividend is a key issue its
magnitude is quite important from a policy point of view. What, then, can we learn about the
double dividend from CGE model based studies?
Using a static CGE model Bovenberg and de Moij (1994) show that a strong double dividend
is possible only if the labor supply function is backward-bending, which is not consistent with
the findings in empirical studies of labor supply behavior. On the basis of this result they
concluded that the strong double dividend does not exist for realistic values of the relevant
elasticity parameters. However, a static model is not well suited for analyses of investments
and capital taxation. Thus there is a case for using a dynamic CGE model for the analysis of
the double dividend issue.
20
Jorgenson and Wilcoxen (1993), used a dynamic model and they found that a strong double
dividend exists when the revenues from the environmental tax are used to reduce capital
taxes. If the revenues instead were used to reduce labor taxes, however, there was no strong
double dividend. However, neither Goulder (1995b) nor Bovenberg and Goulder (1997), who
also used dynamic models of the U.S. economy, found evidence of a strong double dividend.
One reason for this was that both Goulder and Bovenberg & Goulder assumed that capital
was immobile across sectors, while Jorgenson and Wilcoxen assumed full inter-sector capital
mobility.
In contrast to the results by Jorgenson and Wilcoxen (1993) Bye (2000), who used a dynamic
model of the Norwegian economy, found that a revenue-neutral swap between an increased
environmental tax and a reduced tax on labor income was welfare increasing. According to
Bye the differences between Jorgenson´s and Wilcoxens´s results and her own results depend
on the fact that the marginal excess burden is higher for capital taxation than for labor
taxation in the U.S., while the opposite holds in Norway. However, in Böhringer and Pahlke
(1997), who also used a dynamic model, no strong double dividend could be found. Thus the
conclusion that emerges from the CGE model analyses is that the existence of a strong double
dividend can neither be taken for granted nor entirely ruled out.
8. CGE models of resource depletion and management
As developing countries tend to be more dependent on natural resources than industrialized
high-income countries CGE models focused on natural resource management issues are
typically models of developing countries. However, the “externality” type of environmental
CGE models completely dominates the field. In fact very few models are focused on natural
resource management and policy issues.
Devarajan (1988) surveys the issues that have to be dealt with in a CGE model of a
developing country in which the economy to a large extent depends on a depletable resource.
Three different perspectives are adopted, and the related CGE modeling issues are discussed.
In the first the natural resource is seen as an input to production. In the second, denoted the
“Dutch disease” perspective, it is seen as a source of revenue for the economy. In the third the
analysis is focused on the exhaustibility of the resource and the inter-temporal resource
allocation issues related to that. However, most models of developing countries are focused
either on pollution problems or issues related to excessive exploitation of natural resources. A
few examples are given in the following.
Xie and Saltzman (2000) present both a general framework for CGE models of developing
countries and a specific model of the Chinese economy. As a basis for the CGE model they
develop an environmentally extended social accounting matrix (ESAM) to serve as a
consistent data set for calibrating the model. The China model is used for an evaluation of
pollution control policies focused on wastewater, smog dust and solid waste.
A recent model with an elaborated treatment of natural resources is Abler et.al. (1999). It is a
model of Costa Rica, and one of the distinguishing features of the model is that it includes
eight different environmental indicators, including the degree of deforestation and the degree
of over-fishing. The impact of production and consumption activities is essentially modeled
as external effects, and the environmental indicators are in effect treated as public goods.
21
Persson and Munasinghe (1995)18 also use a model of Costa Rica and focuses on
deforestation. In the model deforestation is an endogenous result of ill-defined rights to
forestland. Thus, when property rights to forest land are not well defined and protected
loggers and squatters neglect the value of maintaining the land in question for forestry in the
future, and the incentives to deforestation are strong. When, on the other hand, property rights
to forestland are well defined the owners take the value of maintaining the forest into
consideration, and the incentives for deforestation are much weaker.
Another example of a CGE model focused on natural resource management issues is Unemo
(1995). The purpose of the study is to analyze unintended side effects of government policies
in Botswana. One key feature of the model is that “capital” in the livestock sector has the
form of cattle. Another key feature is a measure of land pressure, measuring the ratio between
the number of hectares of grazing land and the number of cattle held on that land. Grazing
land is treated as a common property resource. The idea is that an increase in the number of
cattle per unit of grazing land has a negative impact on productivity in the livestock sector.
The land pressure variable can also been seen as an indicator of environmental quality. Using
the model Unemo finds some interesting relations between, on the one hand, economic policy
measures and changes in world market conditions and, on the other hand, environmental
quality.
Thus in one case it was assumed that there was a fall in the world market price of diamonds,
which is a major export product of Botswana. According to the model the lower world market
price of diamonds would lead to a deterioration of environmental quality, in terms of land
pressure, in Botswana. The reason is that lower revenues from diamond export lead to lower
demand for manufactured goods and thus a lower rate of return of capital in the
manufacturing sector. As a result of that capital is reallocated to the livestock sector, i.e. the
number of cattle increased. As a result the pressure on land increases. This result illustrates
that a CGE model can reveal indirect interdependencies in the economy that are not
immediately obvious to the analyst.
9. Concluding remarks
In terms of the number of models, and studies based on these models, CGE modeling has
expanded very significantly, particularly during the 1990´s. Currently CGE modeling is both a
field for specialized research, and an almost standard part of the toolbox of economists
concerned with policy-oriented research. A major reason for the widespread use of CGE
modeling probably is that a CGE model is an ideal bridge between economic theory and
applied policy research. The “bridge” perspective, however, suggests that CGE modeling is a
way of using rather than testing economic theory. Yet carefully designed and estimated CGE
models have a lot to say about real world economies.
CGE modeling has made significant progress in terms of the size and complexity of the
models that can be solved. Thus, while the early CGE models were simple static Walrasian
models of a single economy, later CGE models to a large extent are dynamic, multi-country or
global. There are also models with imperfect competition in one or several markets. In
environmental CGE modeling both the dynamic and the multi-country features have made
CGE models useful for in analyses of important policy issues such as climate change and acid
rain. However, in many cases the damage caused by emission of pollutants is uncertain and
policy measures could in effect be seen as an insurance against future catastrophic damage.
18
Their model is described in detail in Persson’s PhD dissertation, Haksar (1997).
22
Thus a desirable further development of environmental CGE modeling is to incorporate
uncertainty.
Yet complexity should never be an end in itself in CGE modeling. Much of the usefulness of
a CGE model stems from its solid foundation in basic economic theory. Thus, even if
simulation results from a standard CGE model sometimes may be surprising they can always
be explained in terms of well known income and substitution effects in combination with
interdependencies between markets. The addition of non-standard features, such as imperfect
competition on product and factor markets, price rigidities and inter-temporal relations, may
make the model more “realistic”. But that may also imply that the transparency of the CGE
model is lost.
References
Abler, D., A.G. Rodriguez and J.S. Shortle (1999), “Parameter Uncertainty in CGE Modeling
of the Environmental Impacts of Economic Policy”, Environmental and
Resource Economics, 14, 75-94.
Adelman, I. and S. Robinson (1982), Income Distribution Policy in Developing Countries.
Oxford: Oxford University Press.
Alfsen, K.H., T.A. Bye and E. Holmøy (1996), “MSG-EE: An Applied General Equilibrium
Model for Energy and Environmental Analyses”. Social and Economic Studies.
Oslo: Statistics Norway.
Armington, P. (1969), “A Theory of Demand for Products Distinguished by Place of
Production”, IMF Staff Papers, 16, 159-78.
Ayres, R.U. and A.V. Kneese (1969), “Production, Consumption and Externalities”,
American Economic Review, vol. 59, no. 3, June, 282-297.
Babiker, M.H., K.E. Maskus and T.F. Rutherford (1997), “Carbon Taxes and the Global
Trading System”. Paper presented at the International Energy Workshop and
Energy Modeling Forum Meeting, IIASA, June 23-25.
Bergman, L. (1990), “Energy and Environmental Constraints on Growth: A CGE Modeling
Approach”, Journal of Policy Modeling, 12(4):671-691.
Bergman, L. (1990), “The Development of Computable General Equilibrium Modeling”, in
Bergman, L., D.W. Jorgenson and E. Zalai (1990), General Equilibrium
Modeling and Economic Policy Analysis. Cambridge and Oxford: Basil
Blackwell.
Bernstein, P.M., W.D. Montgomery, T.F. Rutherford and Gui-Fang Yang (1999), “Effects of
Restrictions on International Permit Trading: The MS-MRT Model”, The
Energy Journal, Special Issue: The Costs of the Kyoto Protocol: A Multi-Model
Evaluation.
Böhringer, C. and A. Pahlke (1997), “Environmental Tax Reforms and the Prospects for a
Double Dividend”
Böhringer, C., M. Ferris and T.F. Rutherford (1998), “Alternative CO2 Abatement Strategies
for the European Union”, in Braden, J. and S. Proost (eds.) Climate Change,
Transport and Environmental Policy: Empirical Applications in a Federal
System. London: Edward Elgar.
Bollen, J., A. Gielen and H. Timmer (1999), “Clubs, Ceilings and CDM: Macroeconomics of
Compliance with the Kyoto Protocol”, The Energy Journal, Special Issue: The
Costs of the Kyoto Protocol: A Multi-Model Evaluation.
23
Bovenberg, A.L. and R.A. de Moij (1994), “Environmental Levies and Distortionary Taxation
and Labor Market Distortions”, European Journal of Political Economy, 10(4),
655-683.
Bovenberg, A.L. and L.H. Goulder (1997), “Costs of Environmentally Motivated Taxes in the
Presence of Other Taxes: General Equilibrium Analyses”, National Tax Journal,
Brooke, A., D. Kendrick and A. Meeraus (1988), GAMS. A User´s Guide. Redwood City,
Ca.: Scientific Press.
Burniaux, J-M, J.P. Martin and J.O. Martins (1992), “GREEN: A Global Model for
Quantifying the Cost of Policies to Curb CO2 Emissions”, OECD Economic
Studies, no. 19.
Bye, B. (2000), “Environmental Tax Reform and Producer Foresight: An Intertemporal
Computable General Equilibrium Analysis”, Journal of Policy Modeling, Vol.
22, No. 6, 719-752.
Capros, P., T. Georgakopoulos, D. Van Regemorter, S. Proost, K. Conrad, T. Schmidt,Y.
Smeers, N. Ladoux, M. Vielle and P McGregor (1995), GEM-E3.Computable
General Equilibrium Model for Studying Economy-Energy-Environment
Interactions. European Commission. EUR 16714 EN.
Conrad, K. (1999), “Computable General Equilibrium Models for Environmental Economics
and Policy Analysis”, in J.C.J.M. van den Bergh (ed.) Handbook of
Environmental and Resource Economics, Cheltenham: Edward Elgar.
De Melo, J. and S. Robinson (1989), “Product Differentiation and the Treatment of Foreign
Trade in Computable General Equilibrium Models”, Journal of International
Economics, 27, 47-67.
Debreu, G. (1959), Theory of Value. Cowles Foundation. New York: John Wiley & Sons.
Devarajan, S. (1988), “Natural Resources and Taxation in Computable General Equilibrium
Models of Developing Countries”, Journal of Policy Modeling, 10(4), 505-528.
Devarajan, S. and D.S. Go (1998), “The Simplest Dynamic General-Equilibrium Model of an
Open Economy”, Journal of Policy Modeling, 20(6):677-714.
Farmer, K. and K.W. Steininger (1999), “Reducing CO2 Emissions under Fiscal
Retrenchment”: A Multi-cohort CGE model for Austria”, Environmental and
Resource Economics, 13(3):309-40.
Ferris, M.C. and T.S. Munson (2000), GAMS/PATH User Guide, www.gams.com.
Ford Foundation (1974), A Time to Choose: America´s Energy Future (1974). Final Report
by the Energy Policy Project of the Ford Foundation. Cambridge: Ballinger.
Førsund, F.R., M. Hoel and S. Longva (1985), Production, Multi-Sectoral Growth and
Planning. Amsterdam: North-Holland.
Goulder, L.H. (1995a), “Environmental Taxation and the ‘Double Dividend’: A Reader´s
Guide”, International Tax and Public Finance, 2(2), 157-183.
Goulder, L.H. (1995b), “Effects of Carbon Taxes in an Economy with Prior Tax Distortions:
An Intertemporal General Equilibrium Analysis”, Journal of Environmental
Economics and Management, October.
Goulder, L.H., I.W.H Perry and D. Burtraw (1997), “Revenue-Raising vs. Other Approaches
to Environmental Protection: The Critical Significance of Pre-Existing Tax
Distortions”, RAND Journal of Economics. Winter.
Goulder, L.H. and S.H. Schneider (1999), “Induced Tecnical Change and the Attractiveness
of CO2 Abatement Policies”, Resource and Energy Economics, 21(2-3), 211253.
Haksar, A. (1997), Environmental Effects of Economywide Policies: Case Studies of Costa
Rica and Sri Lanka. PhD dissertation, Stockholm School of Economics.
24
Harberger, A.C. (1962), “The Incidence of the Corporate Income Tax”, Journal of Political
Economy, 70 (3), June, 215-40.
Harris, R. (1984), “Applied General Equilibrium Analysis of Small Open Economies with
Scale Economies and Imperfect Competition”, American Economic Review, 74,
December, 1016-31.
Harrison, G.W., T.F. Rutherford and I. Wooton (1989), “The Economic Impact of the
European Community”, American Economic Review, Papers and Proceedings 79
(May) 288-294.
Harrison, G.W. and T.F. Rutherford (1997), “Burden Sharing, Joint Implementation, and
Carbon Coalitions”, in C. Carraro (ed.) International Environmental Agreements
on Climate Change, Amsterdam: Kluwer Academic Press.
Harrison, G.W., J. Jensen, M.I. Lau and T.F. Rutherford (1997), “”Passing the Laugh Test:
Version
0
of
the
MobiDK
Model”.
Available
at
www.gams.com/projects/dk/mobiDK.htm.
Hazilla, M. and R.J. Kopp (1990), “Social Cost of Environmental Quality Regulations: A
General Equilibrium Analysis”, Journal of Political Economy, vol. 98, no. 4,
853-873.
Hill, M. (2001), “Essays on Environmental Policy Analysis: Computable General Equilibrium
Approaches Applied to Sweden”, PhD dissertation, Stockholm School of
Economics.
Hogan, W.W. and D.J. Jorgenson (1991), “Productivity Trends and the Cost of Reducing CO2
Emissions”, The Energy Journal, 12(1), 67-85.
Hudson, E.A. and D.W. Jorgenson (1975), “U.S. Energy Policy & Economic Growth 19752000”, Bell Journal of Economics and Management Science, 5(2), Autumn,
461-514.
Johansen, L. (1960), A Multi-sectoral Study of Economic Growth. Amsterdam: NorthHolland.
Jorgenson, D.W. (1982), “Econometric and Process Analysis Models for Energy Policy
Assessments”, in R. Amit and M. Ariel (eds.), Perspectives on Resource Policy
Modeling: Energy and Minerals. Cambridge: Ballinger.
Jorgenson, D.W. (1998a), Growth. Volume 1: Econometric General Equilibrium Modeling.
Cambridge: The MIT Press.
Jorgenson, D.W. (1998b), Growth. Volume 2: Energy, the Environment and Economic
Growth. Cambridge: The MIT Press.
Jorgenson, D.W. and P.J. Wilcoxen (1993), ”Reducing U.S. Carbon Emissions: An
Econometric General Equilibrium Assessment”, Resource and Energy
Economics, 14, 243-268.
Kainuma, M., Y. Matsuoka and T. Morita (1999), “Analysis of Post-Kyoto Scenarios: The
Asian-Pacific Integrated Model”,
Ljungqvist, L. and T.J. Sargent (2000), Recursive Macroeconomic Theory. Cambridge: MIT
Press.
Lundgren, S. (1985), “Model Integration and the Economics of Nuclear Power”. PhD
dissertation, Stockholm School of Economics.
Mäler, K.G. (1974), Environmental Economics. Baltimore: Johns Hopkins Press.
Manne, A. (1977), “ETA-MACRO: A Model of Energy-Economy Interactions”, in Hitch, C.J.
(ed.) Modeling energy-economy interactions: Five approaches.Washington DC:
Resources for the Future.
Manne, A. and R. Richels (1990), “Global 2100: Model Formulation”. Stanford University.
Manne, A. and R. Richels (1992), Buying Greenhouse Insurance: The Economic Cost of CO 2
Emission Limits. Cambridge: MIT Press.
25
Manne, A. and J.O. Martins (1994), “Comparison of Model Structure and Policy Scenarios:
GREEN and 12RT”, OECD Economics Department Working Paper. No. 146.
McKibbin, W.J. and P.J. Wilcoxen (1995), “The Theoretical and Empirical Structure of the
G-Cubed Model”, Brookings Discussion Papers in International Economics #
18. The Brookings Institution. Washington D.C.
Nordhaus, W.D. (1974), “The Allocation of Energy Resources”, Brookings Papers on
Economic Activity, 4:1974, 529-577.
Nordhaus, W.D. (1994), Managing the Global Commons: The Economics of Climate Change.
Cambridge: The MIT Press.
Nordhaus, W.D. and Z. Yang (1996), “A Regional Dynamic General-Equilibrium Model of
Alternative Climate Change Strategies”, American Economic Review, vol. 86,
No. 4, September, 741-765.
Nordhaus, W.D. (1997). Modeling Induced Innovation in Climate Change Policy. Paper
presented at the Workshop on Induced Technological Change, June 1997,
IIASA, Austria.
Norman, V.D. and J. Haaland (1987), “VEMOD – a Ricardo-Viner-Heckscher-Ohlin-Jones
Model of Factor-Price Determination”, Scandinavian Journal of Economics, 89
(3), 251-70.
Parry, I.W.H, R. Williams and L.H. Goulder (1998), “When Can Carbon Abatement Policies
Increase Welfare? The Fundamental Role of Distorted Factor Markets”, Journal
of Environmental Economics and Management,
Persson, A. and M. Munasinghe (1995), “Natural Resource Management and Economic
Policies in Costa Rica: A Computable General Equilibrium (CGE) Approach”,
The World Bank Economic Review, 9(2), 259-85.
Pezzey, J. (1992), “Analysis of Unilateral CO2 Control in the European Community and
OECD”, The Energy Journal, 13(3), 159-171.
Pohjola, J. (1999), “Economy-wide effects of reducing CO2 emissions: A comparison
between net and gross emissions”, Journal of Forest Economy, 5:1, 139-168.
Robinson, S., A. Yúnez-Naude, R. Hinojosa-Ojeda, J.D. Lewis and S. Devarajan (1999),
“From stylized to applied models: Building multisector CGE models for policy
analysis”, North American Journal of Economics and Finance, 10, 5-38.
Rutherford, T.F. (1992), “The Welfare Effects of Fossil Carbon Restrictions: Results from a
Recursively Dynamic Trade Model”, OECD Economics Department Working
Papers, No. 112.
Scarf, H.E. (1967), “On the Computation of Equilibrium Prices”, in Feliner, W.J. (ed), Ten
Economic Studies in the Tradition of Irving Fisher. New York: John Wiley &
Sons.
Shoven, J.B. and J. Whalley (1984), “Applied General Equilibrium Models of Taxation and
Trade: An Introduction and Survey”, Journal of Economics Literature, 22,
September, 1007-51.
Shoven, J.B. and J. Whalley (1992), Applying General Equilibrium. Cambridge: Cambridge
University Press.
Unemo, L. (1995), “Environmental Impact of Governmental Policies and External Shocks in
Botswana: A Computable General Equilibrium Approach”, in Perringer, C.A.
et.al. (eds.), Biodiversity Conservation. Amsterdam: Kluwer.
Vennemo, H. (1995), “A Dynamic Applied General Equilibrium Model with Environmental
Feedbacks”, Economic Modeling, 14, 99-114.
Whalley, J. and A. Mansur (1984), “Numerical Specification of Applied General Equilibrium
Models: Estimation, Calibration and Data”, in H.E. Scarf and J.B. Shoven (eds),
26
Applied General Equilibrium Analysis. Cambridge: Cambridge University Press,
69-127.
Whalley, J. and R. Wigle (1991), “The International Incidence of Carbon Taxes”, in R.
Dornbush and J.M. Poterba, Economic Policy Responses to Global Warming.
Cambridge: MIT Press.
Whalley, J. and R. Wigle (1992), “International Linkages and Carbon Reduction Initiatives”,
in K. Anderson and R. Blackhurst (eds.), The Greening of World Trade Issues.
Harvester, Wheatsheaf.
Whalley, J. and B. Yeung (1982), “External Sector “Closing” Rules in Applied General
Equilibrium Models”, Journal of International Economics, 16, 123-138.
Xie, J. and S. Saltzman (2000), “Environmental Policy Analysis: An Environmental
Computable General Equilibrium Model for Developing Countries”, Journal of
Policy Modeling, 22(4):453-489.
Yang, Z., R.S. Eckaus, A.D. Ellerman and H.D. Jacoby (1996), “The MIT Emissions
Predictions and Policy Analysis (EPPA) Model”. Report 6. MIT Joint Program
on the Science of Global Change.
27
Table 1. Key characteristics of selected global CGE models
Model
Reference
WW
Whalley and Wigle
(1991)
Burniaux et.al. (1992)
GREEN
Global 2100
Regions
6
Sectors per Dynamics
region
3
Static
12
11
5
2
12RT
Manne and Richels
(1992)
Manne (1993)
12
2
CRTM
Rutherford (1992)
5
3
G-Cubed
McKibbin et.al. (1995)
8
12
MIT-EPPA
Yang et.al. (1996)
12
8
RICE
13
1
5
2
UR
Nordhaus and Yang
(1996)
Harrison and Rutherford (1997)
Babiker et.al. (1997)
26
13
MS-MRT
Bernstein et.al. (1999)
10
6
IIAM
AIM
WorldScan
Quasidynamic
Fully
dynamic
Fully
dynamic
Quasidynamic
Fully
dynamic
Quasidynamic
Fully
dynamic
Fully
dynamic
Static
Energy sector
Top-down
Backstop
technology
No
Technological change
None
Environmental benefits
Yes
Top-down
Yes
AEEI
No
Bottom-up
Yes
AEEI
No
Bottom-up
Yes
AEEI
No
Bottom-up
Yes
AEEI
No
Top-down
No
None
No
Top-down
Yes
AEEI
No
**
Yes
AEEI
Yes
Top down
No
None
No
Top down
No
None
No
AEEI
No
AEEI
No
None
No
Fully
Top down
Yes
dynamic
Kainuma et.al. (1999)
21
11
QuasiTop-down
No
dynamic
Bollen et.al. (1999)
13
11
QuasiTop-down
No
dynamic
* No trade between regions. In some cases carbon permits can be traded against the aggregate good.
** The energy sector is a part of the single aggregated production sector.
28
Table 2. Key characteristics of selected regional multi-country CGE models
Model
GEM-E3
BFR
HRW
Reference
Regions
Sectors per
Dynamics
Energy sector
region
Capros
et.al All EU Member States
18
Quasi-dynamic Top-down
(1995)
and ROW
Böhringer et.al. Germany, France, UK,
23
Static
Top-down
(1998).
Italy Spain Denmark
and ROW
Harrison et.al. US, Japan, France,
6
Static
Top-down
(1989)
Italy, UK, Ireland
Germany, Netherlands,
Belgium,
Denmark,
and ROW
Backstop
technology
No
Technological
change
AEEI
Environmental
benefits
Yes
No
-
No
No
-
Yes
29
Table 3. Key characteristics of selected single-country CGE models
Reference
Country
Number of
sectors
36
Dynamics
Energy goods
Emissions
Quasi-dynamic
Electricity, coal, natural
gas, oil
-
7
13
Static
Dynamic
SO2, NOx, CO2
-
6
Static
Electricity, fuels
Electricity, coal, natural
gas, oil
Natural gas, coal, oil
Jorgenson
and
USA
Wilcoxen (1993)
Alfsen
et.al. Norway
(1996)
35
Dynamic
33
Dynamic
Vennemo (1995)
Norway
Harrison
et.al. Denmark
(1997)
Pohjola (1999)
Finland
6
117
18
Dynamic
Static and
dynamic versions
Quasi-dynamic
Abler et.al. (1999)
Costa
Rica
Austria
15
Static
8
Sweden
17
Overlapping
generations
Dynamic
China
7
Static
Hazilla and Kopp
(1990)
USA
Bergman (1990)
Bovenberg
and
Goulder (1997)
Parry et.al. (1998)
Sweden
Farmer
and
Steininger (1999)
Hill (2001)
Xie and Saltzman
(2000)
CO2
Electricity, coal, natural
CO2
gas, oil
Electricity, natural gas, oil SO2, NOx, VOC, O3,
CO, CO2, CH4, N2O,
Electricity, oil
Electricity, natural gas,
coal, oil
Coal, natural gas, peat,
wood,
heating
fuels,
gasoline
Electricity, oil
Electricity, fossil fuels
Electricity, gas, coal, oil
Aggregated energy
SO2, NOx, CO, PM
CO2,
CO2,
Special features
Econometrically estimated parameters;
technology-based environmental
regulations
Tradable emission quotas
Synthetic fuel as a backstop resource
Tradable CO2 quotas and taxes
Econometrically estimated parameters;
endogenous productivity growth
Damage functions defining damage to
public health, forests, lakes and building
materials
Feedback effects.
Carbon sinks
Eight environmental quality indicators
CO2,
SO2, NOx, CO2
Waste water, smog
dust, solid waste
Different cohorts
Inter-temporal emissions trading; feedback effects
Pollutant-specific abatement sectors