ABTEILUNG ÖKONOMIE UND FINANZWIRTSCHAFT / DEPARTMENT OF ECONOMICS AND FINANCE
A Guide to ATCEM-E3:
AusTrian Computable Equilibrium Model for
Energy-Economy-Environment interactions
Tamas Revesz, Corvinus University, Budapest, and
Todor Balabanov, IHS, Vienna
This research was made possible by the financial support of the
Austrian National Bank
September 2007
Table of Contents:
1 . I n t r o d u c t i o n ................................................................................................................................................ 3
2 . 1 . T h e G e n e r a l E q u i l i b r i u m M o d e l l i n g A p p r o a c h ................................................................. 5
2.2 . Ma in Cha ra cte r istic s o f th e ATCEM-E3 Mod e l ..................................................................... 6
3 . O v e r v i e w o f t h e M o d e l ..................................................................................................................... 8
3.2. Descriptions of Selected Blocks and Features of the Model ................................................................. 10
3 . 2 . 1 . I m p o r t / E x p o r t V o l u m e s a n d P r i c e s ............................................................... 10
3 . 2 . 2 . P r o d u c t i o n T e c h n o l o g y ............................................................................................. 11
3 . 2 . 3 . P r o d u c t i o n D e c i s i o n s a n d R e l a t e d P r i c e s ................................................... 12
3 . 2 . 4 . D e m a n d .............................................................................................................................. 15
3.2.5. Change i n Stocks .......................................................................................................................... 15
3.2.6. Private Consumption ...................................................................................................................... 15
3.2.7. Public Consumption ....................................................................................................................... 16
3.2.8. Investment...................................................................................................................................... 16
3 . 3 . E n v i r o n m e n t ................................................................................................................................ 16
3.3.1. Emission Control Through Abatement ........................................................................................... 18
3.4 Balance (market clearing) Conditions .................................................................................................... 22
3.5. Income D i s t r i b u t i o n and Public Finance ................................................................................ 22
4 . M o d e l ’ s I m p l e m e n t a t i o n .................................................................................................................... 23
4.1. Nom enc latu re /D im en s ion of the model .................................................................................... 23
4.1.2. Agents and sectoral dimension ............................................................................................... 23
4.2. Model Output ........................................................................................................................................ 24
5. Data base compilation for ATCEM-E3: sources and methods ....................................................................... 24
5.1. Data sources ............................................................................................................................................. 24
5.1.1. Data in industrial sector (or commodity group) break-down ............................................................ 24
5.1.2. Data in institutional sector break-down ............................................................................................ 25
5.2. Processing of the data.............................................................................................................................. 27
5.2.1. Computation of the Input-Output table .............................................................................................. 27
5.2.2. Construction of the input file of the model......................................................................................... 30
5.3. Construction of the SAM for ATCEM-E3............................................................................................. 35
5.4. Compilation of the electricity industry and emission related data of the model ................................ 38
5.4.1. Compilation of the electricity technologies related data of the model............................................... 38
5.4.1. Compilation of the air pollution related data of the model................................................................ 39
6. Model Calibration and Use .......................................................................................................................... 40
6.1. Calibration of the data ............................................................................................................................ 41
6.2. Using the model........................................................................................................................................ 42
6.3. Solution Algorithm .................................................................................................................................. 42
6.4. Description of model’s files and the order of their opening/run.......................................................... 43
6.4.1. Data and Program files...................................................................................................................... 43
6.4.2. Running the model to reproduce the base year.................................................................................. 43
6.4.3. Simulations by the model ................................................................................................................... 44
7. Validation of the model by Scenario building and analysis of the results...................................................... 45
7.1. Scenario building ..................................................................................................................................... 45
7.2. Scenario assumptions for the 2000-2010 period: .................................................................................. 45
7.3. Analysis of the results.............................................................................................................................. 46
Main simulation results ........................................................................................................................... 47
References: ........................................................................................................................................................... 50
Glossary ................................................................................................................................................................ 51
ANNEX – Model’s Equations ....................................................................................................................... 53
2
1. Introduction
This Guide describes the basic features and characteristics of ATCEM-E3 - A u s T r i a n
Computable Equilibrium Model for E nergy-E conomy-E nvironment - interactions.
The ATCEM-E3 has been developed in cooperation between Prof. Ernö Zalai and Dr. Tamas
Revesz from the Faculty of Mathematical Economics and Economic Analysis, Corvinus University,
Budapest, and Dr. Todor Balabanov from the Institut für Höhere Studien (IHS), Vienna.
The model is to be applied for study of Austrian renewable energy options, trading with CO2
certificates and possibly as investigation tool for the impact of external shocks on the welfare.
The ATCEM-E3 model is a static computable general equilibrium model representing the Austrian
economy through a Social Accounting Matrix (SAM) and is covering the interactions between the
economy, the energy system and the environment.
The model computes simultaneously the competitive market equilibrium under Walra´s law and
determines the optimum balance for energy demand/supply and emission/abatement.
The general features of the model are:
1.
It is static in scope: it includes the domestic, EU and rest of the world (ROW) markets and
represents the system at the appropriate level with respect to trade, the sub-system (energy,
environment, economy) and the dynamic mechanisms of agent’s behaviour.
2.
It formulates separately the supply or demand behaviour of the economic agents which are
considered to optimise individually their objectives while market derived prices guarantee
global equilibrium
3.
It explicitly considers the market clearing mechanism and the related price formation in energy,
environment and economy markets, i.e., prices are computed by the model as a result of supply
and demand interactions at the market place and in addition to the perfect competition different
market clearing mechanisms are allowed for
4.
The model exhibits sufficient degree of disaggregation concerning sectors (25 economic
sectors), structural features of energy/environment and policy instruments (e.g. taxation). The
model formulates production technologies in an endogenous manner allowing for price-driven
derivation of all intermediate consumption and the services from capital and labour. In the
electricity sector, the choice of production factors can be based on explicit modelling of
technologies. For the demand-side the model formulates consumer behaviour and distinguishes
between durable (equipment) and consumable goods and services.
6. The model devises pollution permits for atmospheric pollutants and flexibility instruments
allowing for a variety of options, including: allocation (grandfathering, auctioneering, etc.), userdefined bubbles for traders, various systems of exemptions, various systems for revenue recycling,
etc.
The figure hereafter gives the basic scheme of the model
ATCEM-E3 - Social Accounting Matrix with 25 sectors
Environment
CO2, SO2,
NOX,
Particles
KLEM Production
Function
Producers
Maximising Profits
Factors
Capital
and
Labour
Investment by origin
versus Investment by
destination
Maximising Utility
for varaible part of
consumption
Consumption by
purpose vesrsus
Demand for goods
Imports
(EU, ROW imperfect substitutes)
Exports (EU, ROW imperfect
substitutes)
Market Equilibrium for producers & consumers
Energy:Coal, Crude, and Feedstocks, Petroleum Products, Natural
Gas, Hydro, Renewables and Waste, Electricity, Heat
Figure 1. The basic scheme of the ATCEM-E3 model
4
2 . D e s i g n P r i n c i p l e s f o r ATCEM-E3
2.1. Th e G ener al E qu i l i br i u m M o d e l l i n g Ap p r o a c h
The distinguishing features of general equilibrium modelling derive from the Arrow-Debreu1
economic equilibrium theorem and the constructive proof of existence of the equilibrium based on the
Brower-Kakutani theorem2.
Figure 2: Fixed-point and tâtonnement process
The Arrow-Debreu theorem considers the economy as a set of agents, divided in suppliers and
demanders, interacting in several markets for an equal number of commodities. Each agent is a pricetaker, in the sense that the market interactions, and not the agents, are setting the prices. Each agent is
individually defining his supply or demand behaviour by optimising his own utility, profit or cost
objectives.
The theorem states that, under general conditions, there exists a set of prices that bring supply and
demand quantities into equilibrium, and all agents are fully (and individually) satisfied. The BrowerKakutani existence theorem is constructive in the sense of implementing a sort of tâtonnement
process around a fixed point where the equilibrium vector of prices stands (see figure 2). Models that
follow such a process are called computable general equilibrium models.
It has been demonstrated that the Arrow-Debreu equilibrium can also be obtained from global
(economy-wide) optimisation that implements Pareto optimality and uses the equilibrium
characterisation introduced by Negishi3. Models that follow this methodology have the form of
mathematical programming5 and are called optimisation equilibrium models6.
In applied policy analysis, the so-called “closure rule4” problem has often been taken as a drawback of
general equilibrium models, as the results are depending on the choice of the closure rule. As a matter
of fact, a number of earlier approaches have been classified according to the type of closure rules that
they were adopting (neoclassical, neokeynesian etc.).
A recent trend in computable general equilibrium modelling consists of incorporating an IS-LM
mechanism (termed also macro-micro integration), which has been traditionally used in Keynesian
models. J. De Melo, Branson and F. Bourguignon, P. Capros and others5 have independently proposed
the ensuing hybrid models. The IS-LM closure of computable equilibrium models overcomes the
limitation of an arbitrary closure rule that must otherwise be adopted. In addition it provides insight
into financial market mechanisms and related structural adjustment, allowing for a variety of choice of
1
See Arrow K.J. and G.Debreu (1954)
See Kakutani S. (1941)
3
See Negishi (1962)
4
or example Dorfman et.al. (1958), Ginsburgh and Waelbroeck (1981)
5
In theory, the computable and the optimisation equilibrium models are equivalent: the former, represented as a system of
simultaneous equations, correspond to the first order optimum conditions of the mathematical programming problem.
Motivated by the long-term character of the climate change issue, several new optimisation models have been constructed
recently. The computable general equilibrium models however, are more common for two reasons: their computer solution
is easier; they enable a straightforward representation of policy instruments and market-related institutional characteristics,
therefore they enrich policy analysis
2
5
a free monetary variable that can then determine the level of inflation.
The IS schedule is downword sloping curve and represent the locus of all equilibria where total
spending (Consumer spending + planned private Investment + Government purchases + net exports)
equals an economy's total output (equivalent to income, Y, or GDP). Alternatively the IS curve can
represent the equilibria where total private investment equals total saving, where the latter equals
consumer saving plus government saving (the budget surplus) plus foreign saving (the trade surplus).
Thus the IS schedule is a locus of points of equilibrium in the "real" (non-financial) economy. Given
expectations about returns on fixed investment, every level of interest rate (i) will generate a certain
level of planned fixed investment and other interest-sensitive spending: lower interest rates encourage
higher fixed investment and the like. Income is at the equilibrium level for a given interest rate when
the saving consumers choose to do out of that income equals investment (or, more generally, when
"leakages" from the circular flow equal "injections"). A higher level of income is needed to generate a
higher level of saving (or leakages) at a given interest rate. Alternatively, the multiplier effect of an
increase in fixed investment raises real GDP. Either way explains the downward slope of the IS
schedule. In sum, this line represents the line of causation from falling interest rates to rising planned
fixed investment (etc.) to rising national income and output.
The LM schedule is an upward-sloping curve representing the role of finance and money. The initials
LM stand for "Liquidity preference/Money supply equilibrium" but is easier to understand as the
equilibrium of the demand to hold money (as an asset and for use in everyday transactions) and the
supply of money by banks and the central bank. The interest rate is determined along this line for each
level of real GDP.
The IS-LM mechanism in the equilibrium models has been often used for the evaluation of
stabilisation packages. These models often incorporate additional features that enhance their
short/medium term analysis features such as financial and monetary constraints and dynamically
adjusting expectations.
Facilitated by the explicit representation of markets, the computable general equilibrium models have
often been extended to model market imperfections in the goods or labour markets and other economic
mechanisms that deviate from the Pareto optimality frontier.
Some authors used the term “generalised equilibrium modelling” to underline the flexibility of the
computable equilibrium paradigm, regarding the extensions aforementioned, but also the possibility to
represent and even mix different market clearing regimes within a single model. In general, these
possibilities enrich the analytical capability of the model regarding structural change and its relation to
market distortions, for example price regulations, cost-depending price setting, etc.
The current stream of CGE models, through its modular design, encompasses the whole area of
modern economics going much beyond the standard neo-classical economics on which the first
generation of CGE models was confined. This new generation of model design is the inspiration
behind the development of the ATCEM-E3 model.
2.2. Main Characteristics of the ATCEM-E3 Model
The design of ATCEM-E3 model has been developed following three main guidelines:
1. Model design around a basic general equilibrium core in a modular way so that different
modelling options, market regimes and closure rules are supported by the same model
specification.
2. Fully flexible (endogenous) coefficients in production and in consumer’s demand.
3. Calibration to the base year (2000) data set, incorporating detailed Social Accounting Matrices
as statistically observed.
The ATCEM-E3 model starts from the same basic structure as the standard W o r l d B a n k m o d e l s .
Following the tradition of these models, ATCEM-E3 is built on the basis of a Social Accounting Matrix
and explicitly formulates demand and supply equilibrium. Technical coefficients in production and
demand are flexible in the sense that producers can alternate the mix of production not only regarding
the primary production factors but also the intermediate goods. Production is modelled through KLEM
(capital, labour, energy and materials) production functions involving many factors (all intermediate
products and two primary factors - capital and labour).
6
At the same time consumers can also endogenously decide the structure of their demand for goods and
services. Their consumption mix is decided through a flexible expenditure system involving durable
and non-durable goods. The specification of production and consumption follows the generalised
Leontief type of models as initiated in the work of D. Jorgenson.
The model is limited to comparative static evaluation of policies.
The model is calibrated to a data set for the base year 2000 that comprises a full Social Accounting
Matrix that is built by combining Input-Output tables (as published by STATISTIK Austria) with
national accounts data. Trade flows are also calibrated for each of the 25 sectors represented in the
model, taking into account trade margins and transport costs. Consumption and investment is built
around transition matrices linking consumption by purpose to demand for goods and investment by
origin to investment by destination. The initial starting point of the model therefore, includes a very
detailed treatment of taxation and trade.
Total demand (final and intermediate) is optimally allocated between domestic and two kind of
imported goods (EU and ROW, under the hypothesis that these are considered as imperfect substitutes
(the “Armington” assumption). To this respect the model follows the methodology of the models that
are developed to study tax policy and international trade.
ATCEM-E3 considers explicitly market clearing mechanisms, and related price formation, in the
economy, energy and environment markets. Following a micro-economic approach, it formulates the
supply or demand behaviour of the economic agents regarding production, consumption, investment,
employment and allocation of their financial assets. The model computes prices as a result of supply
and demand interactions in the markets. Through its flexible formulation, it also enables the
representation of perfect and imperfect competition, as well as hybrid or regulated situations. The
current model version for example, incorporates sectors in which only a limited number of firms
operate under oligopoly assumptions.
Recently some CGE models with imperfect competition have been developed and this feature could be
included in the next generation of ATCEM-E3 model. The imperfect competition is usually based on
the concept of product varieties as this derived from the theory of industrial organisation and the
concept of economies of scale that provides for an elegant micro-economic framework for including
non-linearities in production and consumption. Such models have been developed mainly in Europe to
study the impact of European unification. Similar techniques have been utilised to study the labour
market imperfections. The concept of product varieties has also been utilised to endogenise technical
progress in a number of theoretical models.
Firms in these sectors operate under non-constant returns to scale involving a fixed cost element,
endogenously determine their price/cost mark-ups based on Nash-Bertrand or Nash-Cournot
assumptions. Firms in these sectors can make profits/losses that will alter the concentration and firm
size in the sector. Demand then is also firm specific in the sense that changes in product varieties are
directly affecting the utility of the consumers.
Institutional regimes, that affect agent behaviour and market clearing, are explicitly represented,
including public finance, taxation and social policy. All common policy instruments affecting
economy, energy and environment are included. Model closure options mainly investments/savings
equality are varied according to capital or labour mobility across the sectors, the external sector ,
possibility of adjustment, etc.
The present ATCEM-E3 model is general and complete, in the sense that it includes all agents and
markets that affect Austrian economic equilibrium. The model attempts also to represent goods that
are external to the economy as for example damages to the environment.
7
The internalisation of environmental externalities is conveyed either through taxation or global system
constraints, the shadow costs of which affect the decision of the economic agents. The current version
of ATCEM-E3 is linking together the global constraints to environmental emissions changes in
consumption or production patterns, the external costs/benefits, the taxation issues, as well as the
pollution abatement investments and pollution permits. It evaluates the impact of policy changes on
the environment by calculating the change in atmospheric emissions and damages and determines costs
and benefits through an equivalent variation measurement of global welfare (inclusive environmental
impact). The recent awareness about the greenhouse problem motivated the emergence of several
empirical models for analysis of the economy-environment interactions. For example, the work of W.
Nordhaus, D. Jorgenson and Wilcoxen, A. Manne and Richels, Blitzer and Eckaus, K. Conrad,
L. Bergman, S. Proost and Van Regemorter22 have focused on the economic conditions for obtaining
CO2 reduction by means of a carbon-related tax. Such a policy issue needs to be addressed by ensuring
consistent representation of the interactions between the economy, the energy system and the
emissions of CO2.
A counterfactual simulation is characterised through its impact on consumer’s welfare or through the
equivalent variation of his welfare function. The equivalent variation can be, under reasonable
assumptions, directly mapped to some of the endogenous variables of the model such as consumption,
employment and price levels. The sign of the change of the equivalent variation gives then a measure
of the policy’s impact and burden sharing implications.
The ATCEM-E3 model is built in a modular way around its central CGE core. This modular structure
allows for the definition of several alternative regimes and closure rules without the need for respecification or re-calibration of the model.
The most important of these Alternative beha v io ur a l/c lo su r e options in GEM-E3 are
Capital mobility across sectors
Flexible or fixed current account (with respect to the foreign sector)
Flexible or fixed labour supply
Market for pollution permits national/international, environmental constraints
Fixed or flexible public deficit
Perfect competition or Nash-Cournot competition assumptions for market competition regimes
3. Overview of the Model
The ATCEM-E3 is based on the conventions accepted by the HUGE model and by the EU GEM-E3
family of models (see Ref. 1, 6 & 12) and is aiming at the use of the general equilibrium theory as an
operational tool in empirically oriented analyses of resource allocation, energy, environmental and
income distribution issues.
The model is in fact a model family, i . e . , within certain limits the user can modify the specification of
the model.
In the following description by indicating the most important alternative specifications of the individual
equations the most interesting model versions will be discussed.
In the subsequent text the term “model” refers to ATCEM-E3.
1. The model is a system of (partly non-linear) equations.
2. The model is just identified, i . e . , the number of variables equals to the number of
equations.
3. The first 81 equations (seen ANNEX – Model’s Equations) - - or more precisely
blocks of equations since one equation may represent identical formula for each elements of
a given set – are forming the core of the model.
4. Each equation of the core determines the value of one variable.
5. Apart from 2 variables ( G E N V T X p o and AEIpoj which in certain environmentally relevant
models might be determined implicitly by their equations, i . e . , E q u a t i o n s (3) and (17),
8
all variables are determined explicitly by assignment statements that in turn can be either
definitional or behavioural equations.
6. The variables for which the core of the model does not contain explicit equations are CGOV,
CLg, , CONSC, IFTX, IPTX, IWG, L A M B D A , LUMTOT, Q , R , RSi, V , W ,
C R E S C . To make the system precisely identified the user has to choose further 14
equations from the menu shown in the closure (Equations (82a), (95a) ).
7. To avoid under- and over identification (redundancy and contradiction) the user is
recommended to start with the so-called EPM (Equilibrium Programming Model) version
which consists of Equations (82a), (83a), (84a), (85a), (86a), (87a), (88a), (89c), (90a), (91a),
(92a), (93c), (94b), (95a). Then, the user can replace these equations one-by one with the
desired alternative in order to get the required specification. Each specification corresponds
to certain theoretical assumptions or to the choice of exogenously determined variables.
Note that:
All but the last equations of the model are homogenous in respect to the nominal (price or value
type) variables. That is, once the equations hold they would continue to do so even if all nominal
variables were multiplied by the same scalar. That is the reason why for the Equation (95a) the
price level should be set exogenously.
The models equations satisfy Walras law, which implies that the sum of the net monetary
savings are equal to zero
∑ CREDIT
j
j
= SGOV + SROW + ∑ SHOU
g
g
where:
CREDITi - Sectoral net borrowing (= - savings), i ∈ ℑ
SGOV - Governmental savings
SROW – Savings of the Rest of the Word (= - foreign balance of payments)
SHOUg - Households savings, g ∈ G
Grossly speaking by including the real investment among the savings, one can say that total savings
equals total investments (the well-known S = I relationship in the macroeconomic textbooks) - so its
explicit prescription is unnecessary.
3.1 Theoretical Characteristics
Essentially, the model follows the Walrasian or neoclassical tradition. However, several versions of the
model allow for certain deviations from the standard neoclassical approaches. For example, one can
define alternative models with disequilibria in the resource markets, or irrational consumer behaviour
or models with exogenous (e.g. investment) goals that cannot be described analytically.
The model at this stage is static which implies that instead of sophisticated inter temporal decision
rules, the savings and consumption are determined by simplified rules of thumb. For more realistic
treatment of these problems a dynamic model and a monetary module of the model are under
development.
The production function of the model is of the so-called Johansen type which assumes Leontief-type
fixed input coefficients but allows for smooth substitution between:
Domestic and imported goods,
Various types of energy,
Aggregate energy and labour and
Energy-labour composite and capital.
The substitution possibilities are represented by isoelastic (nested) C E S functions.
Similar substitution p o s s i b i l i t y e x i s t s between imported and domestic goods i n consumption
(and final uses in general). In addition, in the case of the personal consumption (the variable part, i.e., in
9
excess to the exogenously given consumption) the sectorial import-domestic composites (which are
defined simply by their sectorial origin) are combined by a C E S aggregator function thus allowing for
substitution.
Export supply i n each industry is determined by a CET function, which allows for transformation in
the production process between goods produced for the domestic and foreign markets.
Although by assuming that the “small country” world prices for imports are exogenous (not
influenced by the domestic demand) then the export prices may depend on the exports volume. This can
be explained either by assuming that the domestic products are to be different from similar products
from the rest of the world (ROW) or by taking into account the higher short run transportation and
marketing unit costs when larger amounts are to be sold. the price concept used in the model is that these
unit costs are diminishing the FOB prices of exports.
Labour is homogenous or mobile across sectors but following Johansen [1960], exogenous
sectorial wage differentials are used to allow for sectorial variations in wage rates. However, the general
index of wages is uniform unless one exogenously changes the wage differentials.
Capital is either mobile or sector specific depending on the choice of the user. Producers are
assumed to minimize costs. Possible monopolistic behaviour is represented by mark-ups in the
production price formula.
Households are maximizing their utility measured by the aggregate level of the variable consumption
or the aggregate utility of consumption leisure and environmental quality (in most versions seen as a
higher level aggregate).
The income distribution is described in details, i.e., sectorial income distribution includes p r o f i t
taxes, dividends, and various earmarked or in-kind (mainly investment) transfers. Therefore we can
derive the disposable income for the individual sectors. However, in the present (static) version of the
model we assume that the shares of the sectors in the total investment are exogenously given, which in
turn is determined by the macro closure rule of the model. As a result, credits are needed to bridge the
gap between disposable incomes and consumption so that the investment level is determined endogenously
(residually). An investment matrix is used in the model to show the shifts in fina l demand structure
resulting from the different buildings and machine intensities of the sector specific investments.
3.2. Descriptions of Selected Blocks and Features of the Model
3.2.1. Import/Export Volumes and Prices
Commodities supplied to the domestic markets are in the most of the sectors composites of
domestically produced and imported goods. As to the imported goods our model can distinguish between
two d iffer e n t markets, namely the EU import/export market and the ROW. These markets can be
assumed to behave in d i f f e r e n t ways and the user has some freedom to decide on the s p e c i f i c
nested structure of substitution between the domestic products and various imports.
In most of the models the substitution possibilities would be d e f i n e d at the level of the total
sectorial use, as if the mix of imported and domestically produced goods would be the same across
d i f f e r e n t uses or users. But in the ATCEM-E3 model in order to allow for more realistic
representation of the substitution opportunities, and if needed to be able to apply d i f f e r e n t i a t e d
import duties, we have three distinguished categories, namely, consumption, investment and other uses.
The supply of imported goods is considered to be i n f i n i t e l y elastic (the so called small
country assumption), which means exogenous world market prices of imported commodities.
According to the A r m i n g t o n assumption, domestic products and imports of the same sectorial
origin are considered to be d i f f e r e n t i a t e d products of the same variety. The model users have to
decide on the special aggregation rule (simple or nested C E S functions) in order to d e f i n e the
volume of the composite supply. Also, for a given sectorial commodity one has to specify the allocation
rule on how large the share of the d iffer en t i mp o rt sources in the total supply will be. In the
neoclassical fashion the composition of the domestic supply of a given composite sectorial good is derived
from the cost minimizing principle, yielding the usual neoclassical demand f u n c t i o n s
10
M i ,t , u
⎛
Pi
= MH i ,t ,u * ⎜⎜
⎝ V * TXM i ,t ,u * PWM i ,t ,u
⎞
⎟
⎟
⎠
MELi , t , u
* XDi ,u
(23)
It is also possible to apply a d i f f e r e n t rule, namely, EU imports are to be treated as perfect
substitutes for the composite of the ROW i m p o r t s and the domestic supply, though their ratio, as in
the case of imperfect substitutes, is still a function of their relative prices. Such demand function can be
interpreted as r e f l e c t i n g imperfect adjustment to changing relative prices, rather than imperfect
substitutability,
(
− BETAi , u
i ,u
HTS i ,u = M i , E ,u + AH i ,u * XD
+ AM i ,u + M
)
1
− BETAi , u − BETA
i ,u
i ,W ,u
(28)
There is some asymmetry between the treatment of exports and of imports. In the case of exports,
unlike imports, the s m a l l c o u n t r y assumption is usually dropped. Given the price of the competing
exports from the rest of the world and the total demand of the given export market one can assume an
inverse correspondence between the volume and the unit price of exports (direct or inverse export
demand function). That brings special terms of trade effects into the model which has to be carefully
checked for its order of magnitude,
PZ i ,t
⎛ Z i ,t
= ⎜⎜
⎝ ZDi ,t
1
⎞ ZELDi ,t
⎟
* PWZ i ,t
⎟
⎠
(22)
One way to avoid large changes in terms of trade that would be d i f f i c u l t to explain and still
constrain effectively changes in the export volume is to match the above export demand functions
with export supply functions. They can be d e f i n e d in a fashion similar to those of the import demand
functions. Exports can be treated as imperfect substitutes to similar goods sold on domestic markets,
aggregated into a composite sectorial output by appropriately chosen CET (transformation) functions,
(
X i = ADi * XDTi − ZBETAi + AZ i * Z i−,WZBETAi
)
−
1
ZBETAi
+ Z i,E
(27)
One can, thus, derive the decomposition of the sectorial output as if it were the result of an income
maximizing choice. Here again the ATCEM-E3 model also allows for non-neoclassical export
supply determination,
⎛ TXZ i ,t * V * PZ i ,t ⎞
⎟⎟
Z i ,t = Z 0i ,t * ⎜⎜
Pi
⎝
⎠
− ZELS I ; T
⎛ XDTi ⎞
⎟⎟
* ⎜⎜
⎝ XDT 0i ⎠
ZXELi
(24)
3.2.2. Production Technology
The representation of production possibilities in our CE model is based on the combination of the inputoutput models and smooth production functions. We utilize the conventions and database of the inputoutput tables but depart from the traditional input-output models by allowing for various kinds of
(imperfect) substitutability between inputs and outputs groups.
Thus, like in the input-output models, the base production units are the sectors. The level of
sectorial aggregation and breakdown depends on the purpose of the model and the availability of data,
e.g., in our case there are 25 sectors. The sectorial production functions take the form of multi-stage
nested C E S functions of the intermediate inputs (sectorial commodities) and primary inputs (labour
and capital).
Sectorial indices are denoted by i and j ( i, j ∈ ℑ ), X stands for production, XHM for
intermediate inputs, L for labour and K for capital.
Johansen [1960] has introduced the simplest extension of the linear input-output model to allow for
price driven production structure.
11
In his solution, at the lower stage, labour and capital (L and K) define a composite primary input in the
form of a Cobb-Douglas production function (volume aggregator). At the upper stage, this composite
primary input and the intermediate inputs together determine then (as perfect complements) the sectorial
output according to a Leontief production function. In an open economy the intermediate inputs
( X H M ) themselves are as a rule treated as composite commodities, as C E S aggregates of domestic
and imported commodities of the same sectorial origin ( A r m i n g t o n assumption).
This Johansen-type production specification has been adopted in the ATCEM-E3 model variants,
replacing the Cobb-Douglas with a general C E S function (allowing for capital and labour substitution
elasticities smaller than one). However, because of our particular concern on
energy/economy/environment issues, we employ more complex production functions allowing for a
more differentiated treatment of input substitution possibilities. I n particular, the intermediate inputs
are further subdivided into energy inputs ( X H M i j i ∈ εΝ, j ∈ ℑ ) and non-energy (material) inputs
( X H M i j i ∈ ΝεΝ, j ∈ ℑ ).
Figure 3: The nesting structure of the sectoral production functions
The demand for non-energy intermediate inputs is computed by assuming Leontief-type fixed inputoutput coefficients. Energy inputs define a composite input according to a C E S volume aggregator.
At the next stage energy and labour are aggregated into a higher level composite input (LE) which is
then combined with capital in a similar way to define the sectorial output, which - as discussed later is in turn a composite of domestic output (XD) and exports (Z) (see Figure 3).
3.2.3. Production Decisions and Related Prices
The u s e r s price of the import-domestic composite sectorial commodity is determined as the weighted
average of the prices of the composing sources, which are, as a rule, modified by appropriate
(consumption) taxes and subsidies,
PHM i ,u =
XDi ,u * Pi + ∑ M i ,t ,u * V * PWM i ,t ,u
HTSi ,u
(21)
where:
PHMi,u – Composite good price, i ∈ ℑ , u ∈ U
XDi,u – Domestic sales from production, i ∈ ℑ , u ∈ U
Pi – Domestic producer prices, i ∈ ℑ
Mi,t,u – Import of commodity i, i ∈ ℑ , t ∈ ℑ , u ∈ U
12
PWM i,t,u – World market prices of imports, i ∈ ℑ , t ∈ ℑ , u ∈ U
HTS i,u – Domestic use, i ∈ ℑ , u ∈ U
As a result of the above description the price level of domestic sales and exports changes at different
rates even if the rate of export tariffs/subsidies remains unchanged. Therefore, unlike the traditional
input-output models, the average wholesale price in the CGE model becomes the weighted average
of the domestic and export prices, where the world market price of the export of the modelled economy
(own world market export price) may change as the volume of exports changes.
Pi =
PAi * X i − ∑ TXZ i.t * V * PZ i ,t * Z i ,t
t
(20)
XDTi
As a result of the elaborate treatment of foreign trade several price indices will appear in the ATCEM-E3
for the same sectorial commodity, e.g., exogenously defined world market export and import prices;
endogenous own world market export price; domestic export price (the former multiplied by the
exchange rate and the tariff/subsidy factor); domestic import price and their various averages.
Production decisions are represented as if the sectors acted as cost minimizing single producers. Thus, at
any level of production, X j and input prices,
PHMi,u – Composite good price, i ∈ ℑ , u ∈ U
PLi – Calculative labour costs, i ∈ ℑ
PKi – Calculative capital costs, i ∈ ℑ
the level of inputs used are defined by minimizing total cost ( T C j ) ,
TC j = ∑ PHM i * XHM i , j + PL j * L j + PK j * K j
i
subject to the constraint defined by the production function.
The multi-stage, separable nature of the production function implies a process of sequential
minimization, i.e., the determination of the minimal cost for each separate group of inputs. In the
formal description of the model the cost minimization is usually represented stage by stage, defining
various composite inputs and their minimum unit cost. I f the production function is lineary
homogenous, as it is usually assumed, the minimum average cost is the same as the marginal cost, and
they are independent of the level of production. In competitive equilibrium, under constant returns to
scale, the unit cost is, at the same time, the equilibrium price level.
As it is known, the C E S form allows the model builder to use explicit dual C E S forms to define the
minimum cost and the implied factor demand as functions of the level of production and the input
prices. As a matter of fact various alternative forms can be used to characterize the cost minimizing
solution. The choice among the alternative primal or dual forms, or their various combinations may be
important from the point of view of the stability or the speed of the solution algorithm.
The production decisions determine the producers´ supply and demand of goods and they imply
changes in their costs of production as well as in their prices, since, as mentioned above, the producer’s
price (PP) in competitive equilibrium is defined by the unit cost (due to the assumption that production
functions are homogeneous of degree 1),
PPi = ∑ PHM i * AHM i , j + (PL j * L j + PK j * K j )/ X j
i
where:
AHMi,j – Domestic sales from production, i, j ∈ ℑ
In the model specification, however, this definition would as a rule take a different form, making use
of the dual forms and the possibility for stage-by-stage definition of the cost function’s components.
For example, in the case of the Johansen production function the value added part, ( P L j * L j + P K j
* K j ) / X j can be represented by an explicit (dual C E S ) function of P L j and P K j. In the
13
case of the multistage C E S production function the cost price definitions appear in the form of a
sequence of dual C E S functions that are defining stage by stage the minimum cost of the separate input
groups (Equations (29)(33)).
Changes in sectorial producers prices reflect variations in the unit cost of sectorial inputs, labour and
capital costs and/or changes in the composition of the inputs (e.g., domestic and imported goods, labour
and capital) in response to relative price changes, depending on the model specification. If no
substitution possibility is allowed for, the price definition coincides with the familiar price equations of
the input-output models. Thus, the lower the substitution elasticities are set, the closer one stays to the
classical analysis of price changes, in which distributional effects (changes in wage and p r o f i t rates) are
at the heart of explaining changes in relative (sectorial) prices. This approach can be utilized in some
versions of the ATCEM-E3, in which changes in the wage and/or p r o f i t rates are set exogenously and
capital/labour substitutability is neglected.
Price changes will then reflect the simultaneous changes in the various cost components (labour,
capital, material, energy). Wage costs, may change both endogenously, as a result of changes in the
relative scarcity of labour (the general wage level, W) or in the consumer price index, CPI (in the case of
wage indexation) and exogenously, for example because of changing wage tax rates (W G ). A typical
formulation of the sectorial unit wage cost (PL) would be as follows,
PLi = W * WT 0 j * (1 + WG j )
(41)
where WT0j is the sectorial wage differential.
(Strictly speaking, the assumption of homogenous labour - mobile across sectors - would imply the same
wage in all sectors in the spirit of pure competitive equilibrium.
So the use of wage differentials is a practical compromise introduced by Johansen [1960].)
The cost of capital may change as the average price of capital goods changes or the rate of return
changes (reflecting, for example, a change in the relative scarcity of capital),
PK
j
=PINVS
j
(AMR j + R S j)
(42)
In some variants of the model we would depart from the neoclassical price formation rule and allow for
profit mark-ups even in equilibrium with constant return to scale (Major [1999]). In such case part of
the net earning on capital will show up in the model as pure p r o f i t rather than the cost of the capital.
Such choice of specification reflects the belief that producers can successfully protect their relative
income position by passing at least part of the changes in their costs over to the users of their
commodities. (The choices of the initial value of the normative rate of return can reflect producers’
belief on how much of these changes can be passed on to customers.)
Taking into account the amortization as well, the cost of capital, together potentially with some pure
p r o f i t (which corresponds roughly to the concept of operating surplus) will consist of the following
components,
PK j * K j / X j + PINV * PROFC j = (AMR j + RS j )* PINVS j * K j / X j + PINV * PROFC j
where P I N V and PINVSj stand for the general and the sectorial price index of capital, AMRj for the
sectorial rate of amortization, RSj for the sectorial rate of return on capital and PROFC j for the sectorial
p r o f i t mark-up.
One would derive Johansens solution by setting the p r o f i t mark-ups at zero and the sectorial rates
of return on capital, R S j as the product of a variable general rate of return (R) and fixed sectorial
differential, RS0 j, assuming that capital is mobile across sectors. If sectorial capital were fixed, the
sectorial return on capital would be defined by the scarcity of the sectorial capital (in a way, as a
residual variable). Assuming that the producers can fully pass increases in their costs on to the buyers,
one would simply drop the capital constraints and sectorial rates of return variables from the model
specification. Also, just to indicate further possibilities, one might assume that the mark-up
differentials remain constant but their general level would change in accordance with the changes in the
14
relative scarcity of capital.
Apart from pure cost and price considerations, the eventual income position of enterprises (sectors in
our models) is also influenced by various taxes and subsidies in all economies. Some taxes are clearly
meant to take away rent-like income (extra p r o f i t ) ; some of them are pure excise type of taxes to
collect income for the state budget.
The basic question the modeller has to address in this respect is: what part of the net income should
be considered as return on the capital? Thus, he has to decide which taxes/subsidies affect the users
prices and which are to be considered as pure income transfers (having no impact on prices), affecting
only the retained earning of the sectors (added to or subtracted from their operating surplus). Clearly,
these decisions determine what will be the size of the sectorial operating surplus.
Having determined the net rate of taxes/subsidies ( P T X ) that is modifying users prices one
could define the average wholesale price as follows,
PA
=P P
j
/ (1 - P T X j )
j
or, alternatively,
PAj =
∑ε PHM
j ∈Ν Ν
j ,O
* AHM j ,i +
PEi * Ei + PLi * Li + PK i * K i
+ PINV * PROFCi
Xi
1 − PTX i
(19)
3.2.4. Demand
Final demand is broken down into the usual major components in our model:
Changes in stocks,
Private consumption,
Public consumption,
Investments, and
Exports
3.2.5. Change i n Stocks
The stocks change exogenously provided because it is included in the model in order to complete the
commodity balances.
3.2.6. Private Consumption
Private consumption is modelled by means of one representative household group.
The household behaviour in the model can be described according to a neoclassical approach, making
use of the extended linear expenditure system. The nested utility function includes the choice between
labour, leisure (thus resulting in variable labour supply) and environmental quality.
UTIL = ( SHC
1
ELCW
* CLTOT
1−
1
ELCW
+ SHW
1
ELCW
1−
* LQ
1
ELCW
1 /(1−
)
1
)
ELCW
(75)
where the LQ composite is defined as
1
ELQ
LQ = ( SHL
1−
* LEIS
1
ELQ
+ SHW
1
ELQ
1−
* LQ
1
ELQ
1 /(1−
)
1
)
ELQ
(76)
As to the determination of consumption, a generalized Linear Expenditures System is used in the
ATCEM-E3 model (the substitution elasticity in a Stone-Geary type utility index function is different
from one),
15
The parameters of the above LES demand functions are not estimated by econometric techniques. Instead,
the fixed (committed) part of consumption is set to reflect the degree to which the consumption of the
given good is considered to be demand driven or supply constrained. Lower (higher) levels allow for
relatively larger (smaller) shifts in demand as prices or income change. The smaller possibilities for
change can be regarded as reflecting some short term, non-price rigidities in the adjustment of supply.
On the higher level, optimal household behaviour can also be described as cost minimizing behaviour.
Optimal behaviour (including demand for aggregate consumption, environment and leisure) is
determined by Equations (64) and (72). Non-optimal behaviour means that some (or all) of the first
order conditions for optimality (Equations (90a), (91a) and (92a)) are replaced by arbitrary behavioural
functions, e.g. by exogenous or simply wage-elastic labour supply.
Household monetary saving in the ATCEM-E3 model is either assumed to be proportional to the
disposable incomes or alternatively, private saving can be determined as a residual by fixing the level of
consumption exogenously. Being a static, short to medium term focused model, saving (investment into
housing) is treated exogenously.
3.2.7. Public Consumption
Public consumption is modelled by an exogenously given structure ( f i x e d coefficients). In most cases
the expenditure level is also exogenously defined. Public savings/deficits on the other hand are usually
determined as residual variables (what remains after accounting for all incomes and expenditures of the
public sector). Alternatively, the model user can choose government consumption level to be a variable
and prescribe a given level of d e f i c i t . Also, in some applications of the models one can compute the
“budget n e u t r a l ” e f f e c t of certain changes in the tax and benefit system. Endogenising some tax or
benefit rates can easily do this.
3.2.8. Investment
The investment demand for sectorial commodities is determined by splitting total investment into
various areas. The model follows the input-output modelling tradition and uses an investment
coefficient matrix to determine investment demand. Depending on the eventual sectorial character of
the investment, all investments are grouped by production sectors. The investments made by the state and
private households are, in this solution, translated into sectorial investments by means of investment
transfers.
The fixed sectorial coefficients d e f i n e in each investment area a specific composite capital
good and a corresponding price index. The prices of these capital goods are used to d e f i n e the current
value of the area specific capital stock, the volume of which is measured by its value at base prices.
The determination of the investment level can take d i f f e r e n t forms depending on model
specification. In particular, gross investment may be either fixed or can be treated as a freely adjusting
variable, depending on the adopted macro-closure rule. The sectorial shares in total investment are
considered constant in the model, as the sectorial decomposition of total investment has limited effect in a
static model, given that there are no future periods in which capital accumulation would be
influential.
3.3. Environment
The figure bellow provides a general scheme of the structure of the environmental submodel
of ATCEM-E3.
The use of the environmental module is optional, i.e., its use depends on the value of the parameter
(switch) E3. If it is set to 1 then the module is included, otherwise it is not used. It also affects the way
16
the users prices ( P H M U and P H M U C ) are computed. If switch E3 is equal to 0 then the tax and
abatement cost component of their formulas (Equation (15) and (16) ) are dropped.
At present the model can deal with the problem of air-pollution caused by emissions of C O 2, S O 2,
N O x and suspended particles. While some natural, non-energy, and non-pyrogeneous emissions take
place, most man-made emissions of the four pollutants are associated with combustion of energy carriers.
Thus the vast majority of the four air pollutant emissions we are investigating are associated with energy
use.
Figure 4. The structure of the environmental submodel of ATCEM-E3
The baseline combustion emissions were further decomposed by fuel type so as to obtain estimates of
emissions originating with each of the energy inputs that entered into production of j or consumption of
g. These pollutant and fuel-specific emission estimates were divided by the monetarized input of each
fuel used by each sector in the baseline yielding baseline emission coefficients for each fuel input.
These coefficients, CE p o i j and CEHpoig ( i ∈ εΝ ) are used as parameters in the model. They allow
for increasing emissions with increasing scale of production and associated increases in use of energy
17
inputs. Even more important, these coefficients link emissions to the energy mix used in production.
Thus the inter fuel s u b s t i t u t i o n s is one of the main ways in which producers can change air
pollutants emission.
Making reference emissions proportional to the fuel use is not an entirely satisfactory approach in those
sectors such as pharmaceuticals that are using energy carriers as process feedstock and not for combustion.
In such sectors we made rough adjustments upwards to the emission coefficients while at the same
time trying not to overstate the opportunity for emission reductions in that sector due to factor
substitution or pollution abatement. The assumption of proportionality, while not perfect, does allow us
to model emissions reductions via inter fuel substitutions as an economic rather than administrative
phenomenon.
Other means of emissions reduction that are incorporated into the model by virtue of this assumption of
proportionality are via general reduction in energy use by domestic industry or consumers, i.e., factor
substitution, and, as discussed below, abatement of emissions due to process equipment modifications
or extensions, e.g., addition of end-of-pipe pollution abatement equipment.
3.3.1. Emission Control Through Abatement
The baseline emission coefficients CEpoij and CEHpoig ( i ∈ εΝ ) represent the potential for pollutant
abatement beyond existing levels of control for each pollutant, p o , associated with energy input, i, in
production of output j. Such abatement, however, is costly and the greater the extent of abatement, for
example as a fraction of potential emissions, the more costly abatement becomes. Following C a p r o s , et.
al. [1995] we introduce decision variables AEIpoj ranging between 0 and 10, into F E I M . These
variables are defined as proportion (based on the abatement level) of emission p o selected by the
industry j. These proportions are embedded in average or unit cost of emissions-abated functions of the
following sort,
− BC po , j
1+GC
CAB po , j =
* (1 − AEI po , j ) po , j + KC po , j
1 + GC po , j
(7)
These average cost emissions-abated functions are increasing and monotonic, that is
∂CAB
∂ 2CAB
≥ 0 and
≥ 0.
∂AEI
∂AEI 2
The abatement cost functions are embedded in the model using the C a p r o s et. al. s [1995] so-called
cost-price strategy 4 . His report also provide a summary of the methods used to estimate costs, to
rank technologies, and to estimate a set of points on air pollution abatement average cost functions for the
sectors and major air pollutants. These data were used in the cost functions like Equation (7) and Figure
5 and 6 for each sector of the model.
For the households a similar abatement cost function can be specified but for the time being it is not
effective in the model.
18
Figure 5
Figure 6. Marginal abatement costs of NOx
Source: P.Carpos and al.
As mentioned earlier, this strategy assumes a fixed proportionality between energy use and emissions.
These coefficients, introduced above, can be used to transform energy consumption into emissions
levels. Then, using the cost functions of Equation (7), converts the emission reductions into the total
material input required to abate emissions due to energy use in production of X j . Associated with
abatement technologies are input coefficients for abatement, ABCpoij. These coefficients were based
on emission control technology input cost data contained in sources used by T a j t h y [1996b] to
construct input costs and recommended to us by him. In ATCEM-E3 these input shares are fixed across
levels of pollution abatement for a given pollutant. A shortcoming of the model as currently formulated is
that energy inputs are not directly included among the input shares. Another adjustment made in order to
operationalize ATCEM-E3 is the characterization of investments in pollution abatement as being
firstly amortized and then included as a cost along with current material inputs.
An expression for material inputs, ABIij required to abate emissions of all pollutants in sector j is
shown by
In principle similar function can be used for the households too (Equation (11). The price indices for
abatement inputs by pollutants and sectors are shown by
19
Remaining emissions after abatement is shown by
for sectors and
for households.
Based on a study (Kaderjak [1996]) environmental quality endowment ( Q E N D O W ) of the
households is a (linearly approximated) function of (changes in) industrial emissions,
where the DAMAGIp o and DAMAGHp o – the marginal damage coefficients were estimated on the basis
of the same study.
With this augmented price, the variables AEIpoj become decision variables and producers problem is
constrained cost minimization with augmented energy prices. In the model equations, as implemented,
more implicit forms of AEIpoi are used in order to assist numerical solution.
3.3.2 Environmental Load Fees
We model environmental load fees (ELF in the followings) as pollutant emission taxes. We compute
the ELF payments of each sector as the product of the pollutants ELF tax rate and the sectors pollutant
emissions.
We therefore characterize the ELFs as excise taxes with a single, uniform rate. This model structure
allows one to specify different ELFs (tax rates) for each pollutant and each production sector.
Emission tax rates are either exogenous or endogenously computed so that a certain emission
reduction target be met (Equations (1)(3) versus Equations (1)(3)).
The ELF revenues are inversely related to AEIpo,j. Thus, in selecting cost minimizing levels of
AEIpo,j. the producers of the sector implicitly select optimal pollution abatement levels such that the
marginal cost of a pollutants abatement is equal to the marginal savings from reductions in that
pollutants ELF payments. Thus the abatement ratio is determined by the following alternative
formulas,
20
where the marginal cost can be computed as
1.
Energy
21
3.4 Balance (market clearing) Conditions
At the core of the CGE models there are various resource balances and accounting identities. In a fully
neoclassical model the supply of various sectorial commodities has to match their total demand, labour
and capital markets must clear, etc. By making use of the build in switches not strictly neoclassical
variants of the ATCEM-E3 model can be formulated as well, i.e., in which labour and capital markets do
not have to clear; instead it is to be assumed that the utilization level of the capacities would change.
These options are technically realized as if the overall productivity of labour and/or the efficiency of
capital are endogenously changed.
Apart from the commodity balances there are various financial balances in the models as well, such
as the balance of foreign trade, the balance of foreign payments, the budget of the private households, the
state household and the production units (sectors).
3.5. Income D i s t r i b u t i o n and Public Finance
There as build in option in the model covering the major aspects of public finance including all substantial
taxes, social policy transfers, public expenditures and deficit financing instruments. By switching in this
option, given an appropriate database, a detailed investigation of the income distribution-redistribution
components can be done.
Equations (35)&(38) represent the budgets of the economic agents (households, sectors, state
household and foreigners) and are implicitly defining the monetary savings as the difference between
disposable incomes and expenditures. As a result primary income is received not by labour and capital,
but directly by the households (wages), the enterprises (capital cost and p r o f i t mark-ups), the state
(wage tax, net indirect taxes on consumption, imports and exports). Secondary income generation takes
place in the form of transfers between the above agents. Most of the transfers are assumed to be
22
proportional to some activity level (and a price index for the valorisation). The transfer amounts are given
exogenously only in few cases, namely when a simple behavioural rule cannot be identified or they are
determined by some dynamic effects (past commitments and anticipations) that cannot be properly
captured in a static model.
It is to be noted that the system of equations is satisfying the Walras-law as general equilibrium
models usually do. As a result, total net monetary savings equals zero so that savings-balancing
requirement is implicitly given.
Also to be noted is that all model equations introduced so far are homogenous in the nominal (price
or value type) variables. That is, once the equations hold they would continue to do so even if the same
constant multiplied all nominal variables.
Most C G E models exhibit such price homogeneity (money neutrality) , thus, there is a need to
e x o g e n o u s l y set the general price level (choose a n u m e r a i r e ) ,
4. Model’s Implementation
4.1. Nomenclature/Dimension of the model
4.1.2. Agents and sectoral dimension
Economic agents
The model considers 4 economic agents: households, firms, government and foreign sector.
Government revenue and income flow categories:
Direct taxation, indirect and VAT taxation
Energy and environmental taxation
Property taxes, capital taxes
Social security, social benefits
Production subsidies
Import duties and foreign sector transfers
Revenues from state owned enterprises.
2 primary production factors: labour & capital
Branches
The model code allows for a user-defined aggregation of sectors and traded products. The following
table shows the industrial classification chosen for the model.
Table 1. The 25 sectors industrial classification of current ATCEM version
No
Sector Name
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
COAL
OIL and GAS
ELECTRICITY
REFINERIES
FORESTRY
STEEL
OTHER METALS
ENGINEERING
BUILDING MATERIAL
FERTILISERS
PLASTIC MATERIALS
OTHER CHEMICALS
LIGHT INDUSTRIES
AT I/O table (year 2000,
2 digits code–see Table 6)
10
40b
40a
11, 23
2
½ from 27, ½ from 28
½ from 27, ½ from 28
29,30, 31,32,33,34,35
26
½ from 24
½ from 24
25
17, 18, 19, 20, 21, 22
FOOD INDUSTRIES
14.
CONSTRUCTION
15.
AGRICULTURE
16.
17. TRANSPORTATION
OTHER MINING
18.
TRADE
19.
WATER
20.
21. OTHER MATERIALS
22. FINANCIAL SERVICES
OTHER SERVICES &
23.
TELECOMMUNICATION
WELFARE
24.
PUBLIC SERVICES
25.
15, 16
45
1, 5
60, 61, 62, 63
14
50, 51, 52, 55
41
72
65, 66, 67
64, 70, 71, 73, 74, 93, 95
80, 85
90, 91, 92
23
4.2. Model Output
•
Distribution of income and transfers in the form of a social accounting matrix.
•
Employment, capital, investment by sector.
•
Public finance, tax incidence and revenues,
•
Current account
•
Consumption matrix by product and investment matrix by ownership branch.
•
Atmospheric emissions, pollution abatement capital, purchase of pollution permits and damages.
Six primary pollutants: CO2, SO2, NOx, VOC, PM and one secondary pollutant, O3.
5. Data base compilation for ATCEM-E3: sources and methods
Here we want to summarize the procedures used and sources applied to the database of the ATCEME3 and specifically to present the statistical methodology, data availability, the information sources,
estimation of missing data, reconciliation (adjustment techniques) of inconsistent data, etc.
5.1. Data sources
The ATCEM-E3 model needs benchmark year data on the production technology (incl. emission of
air pollutants), consumer preferences (patterns), prices, income distribution, savings, final demands.
Due to the data availability we have chosen the year 2000 as a benchmark. To compile the database
we used the following main sources:
5.1.1. Data in industrial sector (or commodity group) break-down
One of the main data source for computing database for the ATCEM-E3 model is the I/O Tables
2000, Statistik Austria, dataset that includes the following tables at the level of 56 sectoral (or as
indicated explicitly in the list of tables) disaggregation:
Table 1. Domestic output at basic prices (Total)
Table 2. Domestic output at basic prices (Sector: General government)
Table 3. Domestic output at basic prices (Sector: NPISH)
Table 4. Domestic output at basic prices (74 products x 75 industries)
Table 5. Intermediate consumption at purchasers' prices (Total)
Table 6. Intermediate consumption at purchasers' prices (Sector: General government)
Table 7. Intermediate consumption at purchasers' prices (Sector: NPISH)
Table 8. Intermediate consumption at purchasers' prices (74 products x 75 industries)
Table 9. Intermediate consumption at basic prices (Total)
Table 10. Intermediate consumption at basic prices (Sector: General government)
Table 11. Intermediate consumption at basic prices (Sector: NPISH)
Table 12. Value added at basic prices (Total)
Table 13. Value added at basic prices (Sector: General government)
Table 14. Value added at basic prices (Sector: NPISH)
Table 15. Final uses at purchasers' prices (Total)
Table 16. Final uses at purchasers' prices (74 products)
Table 17. Final uses at basic prices (Total)
Table 18. Imports cif: intermediate consumption (Total imports)
Table 19. Imports cif: intermediate consumption (Imports EU)
Table 20. Imports cif: final uses (Total imports)
Table 21. Imports cif: final uses (Imports EU)
Table 22. Wholesale trade margins: intermediate consumption
Table 23. Retail trade margins: intermediate consumption
Table 24. Transport margins: intermediate consumption
Table 25. Taxes on products: intermediate consumption
Table 26. Subsidies on products: intermediate consumption
Table 27. Wholesale trade margins: final uses
Table 28. Retail trade margins: final uses
Table 29. Transport margins: final uses
Table 30. Taxes on products: final uses
Table 31. Subsidies on products: final uses
Table 32. Input-output table at basic prices (product by product), domestic output and imports
Table 33. Input-output table at basic prices (product by product), domestic output
Table 39. Employment (Industries)
Table 40. Employment (Products)
Table 41. Gross fixed capital formation at purchasers' prices (Total)
Table 42. Gross fixed capital formation at purchasers' prices (Sector: General government)
Table 43. Gross fixed capital formation at purchasers' prices (Sector: NPISH)
Table 44. Gross fixed capital formation at basic prices (Total)
Table 45. Gross fixed capital formation at basic prices (Sector: General government)
Table 46. Gross fixed capital formation at basic prices (Sector: NPISH)
24
Table 47. Gross fixed capital formation at cif-prices (Total imports)
Table 48. Gross fixed capital formation at cif-prices (Imports EU)
Since the listed I-O tables (Table 32 and 33) are not in the desired industry-by-industry breakdown1, we
had to estimate this from the other tables (mainly the Supply and Use tables and the indirect tax-subsidy
and trade and transport margin tables) essentially according to the methodology described in the
SNA’68 (see later).
Note that the above tables contain quite important data on the factors (amortization, employment) and as
a residual the total investment taxes (by investing industry) can be computed2 (by subtracting the column
totals of the investment matrix at basic prices from the column totals of the investment matrix at
purchasers prices).
On the other hand, the social benefits in kind (education, health care, etc.) are not separated out of the
government consumption, and the matrix of investment (indirect) taxes is broken-down only by
investment categories and not by investing industries.
An unpleasant fact is, that the I-O table figures for exports contain some imports too. This re export is
difficult to explain in a CGE model.
Since the ATCEM-E3 model will be used for energy and environmental policy analysis, of NACE sector
40 (Electricity, gas and hot water supply) sub sector 402, i.e. the (manufactured and distributed) gas had
to be separated out. In the above tables only the Supply and Use tables (tables 4, 8 and 16) had such a
disaggregation. However, since these Use tables are only at purchasers’ prices and show the domestic
and imported products together, we had to use various proportionality assumptions to determine the
values of the uses of sector 402 at basic price.
The ATCEM-E3 model requires data in industry breakdown for the following categories not included in
the above I-O related tables:
- Fixed capital assets by sectors
- Interest payments and dividends by sectors (industries)
- Profit tax by sectors (industries)
- Other current income by sectors (industries)
- Investment- and capital transfers by sectors (industries)
- Stock accumulation data by accumulating sectors (industries)
- Net lending/borrowing by sectors (industries) (or opening and closing value of financial wealth
or book value of the equity)
- Household sector related data (incl. investment and the mixed income and operating surplus in
the household sector)
- Macroeconomic scalar data for INTROW, TURIST, BOP, GINTE (interest, transfer, current
balance of the government and the foreign sector)
- Air pollution data by fuel and sector
To estimate the industry beak-down of the above categories and to do sector 402’s separation out of
sector 40 we used the following auxiliary data:
- Foreign trade by commodities (for the time being we had access only to the 2001 version)
- Loans by branches
- Survey of self-employed (reported in the Statistical Yearbook)
- Energy balance sheets
- National inventory report (IPCC) for air pollutants
5.1.2. Data in institutional sector break-down
The NPISHs in the integrated Austrian national accounts for 2000 are aggregated with the household
sector (while in the I-O table they have separate columns in the final consumption). So in the model we
had to treat the NPISHs as part of the household sector too. Another problem with the NA data is that
the figures differ from the I-O table data - most significantly (about 10-15%) in the case of the foreign
1
Note that product-by-product I-O tables are mainly good for multi-country, technology oriented, dynamic models, while our
model is primarily used for tax policy and other income distribution and pricing related issues.
2
Although during this we detected an inconsistency in the given tables, i.e. that research services for investment is taxed but its
only user, the government does not pay any investment taxes
25
trade flows, but to a less extent it is so in the case of the gross outputs, wages, and stock accumulation.
The NA also showed some particularities that are difficult to take into account in CGE models, i.e., the
foreign sector had wage income3 (from which industry?), SSC income, it paid indirect taxes and
received indirect subsidies (estimated tax content of the expenditures of the inbound tourists?).
It is t be stated that the NA contain very useful information on the secondary income distribution, i.e.
what follows from the creation of the operating surplus. We arranged the NA data in a matrix of income
distribution as shown in the following table:
Table 2. Income distribution based on National Accounts
Income distribution, 2000
Output
Intermediate use
Domestic indirect subsidies
Domestic indirect taxes
Custom duties
Export tax-subsidy
Other production tax
Other production subsidy
Gross wage
Employers Soc.Sec.Contr.
Other production subsidy
Other production tax
Gross wage distributed
Employees’ soc.sec.contr.
Distribution of soc.sec.contr.
Adjustment for pension funds
Social benefits in cash
Income tax
Other (capital) taxes
Interest paid
Interest received
Reinvested foreign direct inv. exp.
Reinvested foreign direct inv.inc.
Dividend received
Dividend paid
Insurance income
Other property inc. (rent)
Non-life Insurance claims
Other current income rec.
Net non-life Insurance premiums
Other current income exp.
Other current income correction
In-kind social benefits
Capital transfers received
Capital transfers paid
Import
Export
Final consumption
Fixed capital formation
Change in stocks, val.-s
Non-prod.,non-fin. assets
Write-offs, revaluations, etc.
3
Reference
Enterpr.
Household Foretotal
Corporates Financial State +NPISH ign Total (SNA,etc.)
227184
17388 34717 81488
360777
244572 OUTPUT
P.1.
-120304
-6989 -10157 -34772
-172222
-127293 INTERM
P.2
-3013
-382
-3395
0 DOMSUB
D.31.
23718
1514 25232
0 DOMTAX
D.21
0 IMPTAX
0
0
0 EXPNTX
0 GDP=
-4536 PRODTX
-4179
-357 -623 -1378
-6537 D.29,
2126 PROSUB
1993
133
1349
3475 D.39,
-67201
WAGE
-61668
-5533 -22829 -17191 -1335 -108556 D.11,
0 SSCOST
0 D.12,
0 PRSUBE
-2841
-634 -3475 =-J11
0 PRTAXI
6537 =-J10
6537
0 WAGEIN
107894 662 108556 =-J12
0 SSCWOR
-37882 -253 -38135 D.61.
2895 SSCDIS
1559
1336 34881
146 213 38135 =-J17
-457 SSCCOR
0
-457
457
D.8.
-2289 CASHBE
-1559
-730 -39007 41275
21
0 D.62.
-4646 INCTAX
-3582
-1064 27445 -22799
0
D.5.
0 OTHTAX
0 D.91.
111
-111
-29490 INTEEX
-8179 -21311 -7722 -3812 -9447 -50471 D.41.
29581 INTEIN
2124
27457 1737 6737 12416 50471 =-J24
-944
-926
-18
-129 -1073 D.43.
129 INTEIN2
77
52
944 1073 =-J26
7642 DIVIIN
6306
1336 1137 11917 2168 22864 D.42.,
-21135 DIVIEX
-17257
-3878
-1729 -22864 =-J28
-2658 INSURIN
132
-2790
2658
0 D.44.
-201 PROPIN
-201
201
0 D.45.
0 D.72.
-2097 INSUCL
805
-2902
2097
1008 OCURIN
844
164 26114 3349 6476 36947 D.73.-75.
2097 INSPRE
0 D.71.
-805
2902
-2097
-4322 OCUREX
-2816
-1506 -28236 -2461 -1928 -36947 =-J33
0
0 INCORR
0
0 INKBEN
0 D.63.=P.31.
-23282 23282
3349 CTRAIN
2910
439 2304 2486 983
9122 D.92.,D.99.
-886 CTRAEX
-242
-644 -7032
-371 -833 -9122 =-J38
92691 92691
0 IMPORT
P.7.
-95595 -95595
0 EXPORT
P.6.
-15369 -142786
-158155 P.31.-32.
0 CONSUM
-32236
-1804 -3121 -10714*
-47875
-34040 INVEST
P.51.
-772
-5
-4
-677
-1458 P.52.-53.
-777 STACCU
-925 BOPERR
-925
0 898
0
27
0
K.2.
0 WRIOFF2
0
Note that official statistical data do not reflect properly the extensive (mainly illegal) employment of foreign labour
26
10498 BORROW
11717
-1219 3436 -8084 -5850
Net borrowing
* of which 10374 is the investment of the households, and 340 is the investment of the NPISHs
0
B.9.
5.2. Processing of the data
5.2.1. Computation of the Input-Output table
The I-O table had to be compiled at basic prices, in 57-industry breakdown (sector 402 separated out).
Aggregation to 25 sectors took place afterward. The related data were processed in the
ATDATABASE.XLS file. It contains not only values but the formulas and the necessary cell comments
too. So it is rather transparent but rather complex file for which the user needs some orientation. The file
is organized as follows:
In its SUT (Supply-Use Tables)4 worksheet we essentially followed the method found in the SNA’68
handbook (pages 48-51.). This explains how the symmetric I-O tables can be computed from the Supply
and Use tables valued at basic prices. Since we had these matrices at basic prices, we could apply the
suggested method only with the following modifications:
1. We display the individual components of the value added and the final demand
2. The import matrices were also known, so the import commodities could be treated as separate
products)
The following table shows the arrangement of the initial data:
Table 3. Arrangement of the Supply-Use Tables according to the SNA'68 symbols & table
V industries (MAKE matr.)
g (industries)
Y
…
h (value added items)
E
q (commodities)
U: USE matrix
g
z
q
Note: totals (1'z=1'h)
In the above table the symbols denote the following categories (as they can be found in pages 48-51 of
the SNA’68 volume):
V:
the output part of the supply table (also called as the MAKE matrix), whose vi,j general
element shows how much (in value) the i-th industry produces of the j-th product (commodity)
g:
the row-totals of V, i.e. a column vector containing the total gross outputs of the industries
q:
the column-totals of V, i.e. a row vector containing the total gross outputs of the products
Y:
the matrix of value added, whose yk,i general element shows how much (in value) of the k-th
component of the value added (wages, taxes, etc.) was generated in the i-th industry
h:
the row-totals of Y, i.e. a column vector containing the macroeconomic totals of the individual
value added categories
U:
the intermediate consumption block of the so-called USE (or absorption) matrix, whose uj,i
general element shows how much (in value) the i-th industry used of the j-th product
E:
the final use block of the so-called USE (or absorption) matrix, whose ej,f general element shows
how much (in value) of the j-th product was used by the f-th final demand category (private and
government consumption, accumulation and exports)
z:
the column-totals of E, i.e. a row vector containing the macroeconomic totals of the individual
final demand categories)
Note, that 1’z=1'h (1 stands for the summation vector, i.e. whose all elements are 1), since both vectors
4
Supply and use tables:
In addition to the flow accounts and balance sheets, the central framework of the National Accounts System also contains detailed supply
and use tables in the form of matrices that record how supplies of different kinds of goods and services originate from domestic industries
and imports and how those supplies are allocated between various intermediate or final uses, including exports. These tables involve the
compilation of a set of integrated production and generation of income accounts for industries - that is, groups of establishments as distinct
from institutional units - that are able to draw upon detailed data from industrial censuses or surveys. The supply and use tables provide an
accounting framework within which the commodity flow method of compiling national accounts - in which the total supplies and uses of
individual types of goods and services have to be balanced with each other - can be systematically exploited. The supply and use tables also
provide the basic information for the derivation of detailed input-output tables that are extensively used for purposes of economic analysis
and projections.
27
show the (although different) breakdown of the macroeconomic total value added. Also note, that in the
above scheme ‘value added’ is used in a generalized way (in the same way as the ‘lower part’ of the
input-output tables), so that if U is measured at basic prices, then the intermediate indirect taxes on
intermediate consumption are accounted in a separate row of the Y matrix (and then the indirect taxes in
the final demand has to be filled into the denoted central block of Table 3 – see the more detailed
discussion of the indirect taxes in the subsequent paragraphs)
After arranging the data according to the presented table, the derivation of the industry x industry
symmetric input-output table takes place. This reclassification of the input breakdown from products to
industries can be found in the ATDATABASE.XLS file’s SIOT (Symmetric Input-Output Table)
worksheet. Here we first computed the D = V <q>-1 “source pattern” matrix, where <q>-1 is the inverse
of the diagonal matrix of q (i.e. which divides each column of V by the corresponding total) we get the
industry by industry I-O table as shown column total, so that dj,i = dj,i / qi )
The reclassification – using the so called industry technology assumption, i.e. that the industries have
their own technology irrespective of the products they produce (see the SNA’68 volume) - can be done
by multiplying U and E by D from the left. The ATDATABASE.XLS file’s SIOT worksheet contains
the results according to the following table’s schematic arrangement:
Table 4. The Reclassified Use Table (the “industry by industry” symmetric I-O table)
D·U
Y
Ii
D·E
g (industries)
z (final demands)
D
g (outputs by industries)
h (value added items)
m (imports by commodities)
Sums of the coloured elements (first 2
blocks)
If
In this table we displayed the matrices of the intermediate and final use of imports too. Ii denotes the
matrix of intermediate demand for imports (product by industry), while If denotes the matrix of the final
demand for imported products. Note, that - although not displayed in the above scheme, - the matrices of
the EU-imports are also displayed in the SIOT worksheet (rows 132-191).
In the above table D·U is the industry-by-industry estimate of the intermediate demand for domestic
products. Similarly, and D·E is the industry by final demand category estimate of the final demand for
domestic products. Also note that (as indicated in the last cell of the matrix) the row and column totals
refer only to the first 2 x 2 blocks range of the above matrix (i.e. the coloured blocks).
In the above table it is not displayed either, but in the following (132-142194-204.) rows of the SIOT
worksheet, we calculated the 57 sector data of various categories. Of these we have to mention here the
profit tax data, which we found in branch break-down in the Statistical yearbook and which we further
disaggregated to the 57 sectors according to their share in the net operating surplus.
The ATDATABASE.XLS file’s “IndTax” worksheet contains the original and reclassified matrices of
the indirect taxes and subsidies (the first column represents the intermediate uses, while the second one
the final demands).
Table 5. Original and Reclassified Indirect tax and subsidy matrices
D·(T-S)
T
S
D·(F-W)
F
W
t (net indirect tax by industries))
f (indirect tax by products)
s (indirect subsidies by products)
Here the symbols denote the following categories:
T:
the matrix of indirect taxes on intermediate consumption (its tj,i general element shows how
much indirect tax was levied on the i-th industry’s use of the j-th product)
S:
the matrix of indirect subsidies on intermediate consumption (its sj,i general element shows how
much indirect subsidy was applied to the i-th industry’s use of the j-th product)
F:
the matrix of indirect taxes on final demands (its fj,f general element shows how much indirect
tax was levied on the f-th final demand category’s use of the j-th product)
W:
the matrix of indirect subsidies on final demands (its wj,f general element shows how much
indirect subsidy was applied to the f-th final demand category’s use of the j-th product)
The transformation of the inputs from products to industries (of origin) was done in a similar way as in
28
the case of the domestic flows.
Note, that we tried to estimate the (net indirect) investment-tax matrix by supplier and investing
industries, but the present results cannot be regarded as final (partly due to the mentioned observed
inconsistency in the data).
The ATDATABASE.XLS file’s “Dom-Aggr” and “Imp-Aggr” worksheets contain the aggregation of
the previously presented 57 sector data to the model’s 25 sector. Below the I-O table related part of the
“Dom-Aggr” worksheet the households’ and the government’s operating surplus and the profit taxes are
aggregated to 25 sectors.
The aggregation is done by the following method:
The aggregation starts from a G aggregator matrix (N rows, n columns, where N is the number of the
original sectors, while n is the number of the aggregated sectors - in our case 25). This G matrix has to
be filled so that IF P proportion of the i-th original sector (output, consumption, investment, export,
whatever category is just to be aggregated) will go to the j-th aggregate THEN it should be P (in most
cases in each row there will be a 1 while the other elements of G will be 0). If the aggregation is pure
aggregation (i.e. P=1 or P=0) then the filling procedure can be simplified by designing a V column
vector in which the i-th element's value is just j. Then G is computed from V the automatically so that
G(i,j) = 1 if V(i)=j, otherwise G(i,j) = 0.
Then the aggregate of the A (NxN) matrix can be obtained by the G*AG matrix product, where G* is
the transpose of G (so multiply A from the left by G* while by G from the right). A matrix that has to be
aggregated only from one side has to be multiplied by G or G*. More precisely if the rows have to be
aggregated, then G*A has to be used, and if the columns, then the AG matrix product gives the result.
The advantage of this approach is that if you change your mind of the aggregation scheme (or if some
original sectors even has to be split or actually disaggregated) you have just to modify V, and then the
Excel formulas do all the necessary changes in an instant.
The Excel file that contains the aggregation scheme from the 58 (57+ 1 “Private households with
employed persons” sector) sectors of the I-O table to the model’s 25 sectors.is in the sema58_25.xls
file. In this file V is in the D5:D62 cells (yellow), of which the rows of the not splittable sectors (which
are coloured red as for example the N20:O20, and J23:K24 cells) in matrix G (E5:AC62) are computed
automatically (although in a little bit tricky way by using the ROUND function and a big exponent to
replace the time consuming IF-s by a proxy characteristic function).
The aggregation of the investment matrix is done in the ATDATABASE.XLS file’s “Composite”
worksheet. Also in this worksheet (in the upper part) we compute the uses of the “composite”
commodities by adding together the matrices of the domestic and import uses
Note that if certain sectors even have to be split during the aggregation, then ideally separate matrices
have to be used for the domestic and the import, in which the respective shares are used for the splitting
of the domestic and import flows. However, for the time being we use only one aggregation scheme in
which for a given commodity (i.e. sector 402 and the rest of sector 40) the same shares are assumed for
the domestic and import sources.
The actual aggregation scheme is shown in the following table:
Table 6. Allocation of the AT I/O table (year 2000, 2 digits code) to the 25 sectors of ATCEM-E3
01
02
05
10
11
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
Products of agriculture, hunting
Products of forestry, logging
Fish, other fishing products
Coal and lignite; peat
Crude petroleum, natural gas, metal ores (1)
Other mining and quarrying products
Food products and beverages
Tobacco products
Textiles
Wearing apparel; furs
Leather and leather products
Wood and products of wood
Pulp, paper and paper products
Printed matter and recorded media
29
Coke, refined petroleum products
Chemicals, chemical products
Rubber and plastic products
Other non-metallic mineral products
Basic metals
Fabricated metal products
Machinery and equipment n.e.c.
Office machinery and computers
Electrical machinery and apparatus
Radio, TV and communication equipment
Med., precision, opt. Instruments; watches, clocks
Motor vehicles, trailers and semi-trailers
Other transport equipment
Furniture; other manufactured goods n.e.c.
37
40a
40b
41
45
50
51
52
55
60
61
62
63
64
65
66
67
70
71
72
73
74
75
80
85
90
91
92
93
95
digits code) to the 25 sectors of ATCGEM-E3
Recovered secondary raw materials
Production and distribution of electricity;
AT I/O table (year 2000,
No
Sector Name
Steam and hot water supplies
2 digits code)
Manufactured gas and distribution of gaseous fuels
1.
COAL
10
Water; distribution services of water
2.
OIL and GAS
40b
Construction work
3.
ELECTRICITY
40a
Trade and repair services of motor vehicles etc.
4.
REFINERIES
11, 23
Wholesale and comm. trade serv., ex. of motor vehicles
5.
FORESTRY
2
Retail trade serv., repair serv., exept of motor vehicles
6.
STEEL
½ from 27, ½ from 28
Hotel and restaurant services
7.
OTHER METALS
½ from 27, ½ from 28
Land transport services and transport via pipeline
8.
ENGINEERING
29,30, 31,32,33,34,35
Water transport services
9. BUILDING MATERIAL
26
Air transport services
10.
FERTILISERS
½ from 24
Supporting transport services; travel agency services
11. PLASTIC MATERIALS
½ from 24
Post and telecommunication services
12. OTHER CHEMICALS
25
Financial intermediation services, FISIM
13.
LIGHT INDUSTRIES 17, 18, 19, 20, 21, 22
Insurance and pension funding services
14.
FOOD INDUSTRIES
15, 16
Services auxiliary to financial intermediation
15.
CONSTRUCTION
45
Real estate services
16.
AGRICULTURE
1, 5
Renting services of machinery and equipment
17.
TRANSPORTATION
60, 61, 62, 63
Computer and related services
18.
OTHER MINING
14
19.
TRADE
50, 51, 52, 55
Research and development services
20.
WATER
41
Other business services
21.
OTHER MATERIALS
72
Public administration services etc.
22. FINANCIAL SERVICES
65, 66, 67
Education services
OTHER
SERVICES
&
Health and social work services
23.
64, 70, 71, 73, 74, 93, 95
TELECOMMUNICATION
Sewage and refuse disposal services etc.
24.
WELFARE
80, 85
Membership organisation services n.e.c.
25.
PUBLIC SERVICES
90, 91, 92
Recreational, cultural and sporting services
Other services
Private households with employed persons
Allocation of the AT I/O table (year 2000, 2
Source: Table 32. Input-output table at basic prices, domestic output and imports; Statistik Austria - I/O Tables 2000
5.2.2. Construction of the input file of the model
Of the above data we could start the construction of the input file of the model, which is called SAMAT.XLS. Here in the “gamsfile” worksheet we fill in (or directly estimate) the 25-sector data of the
model. Since the 25-sector data are too big, in the following table we present the relevant (statistical)
part of its 3-sector aggregation as it can be found in the “26->3 sector” worksheet of the file.
Table 7. Three sectors version of the model’s input file data
INTERMEDIATE CONSUMPTION at basic prices
(AHM0 block):
Rows: product (by sectors of origin), columns: user
branch
TABLE
AHM0(I,J)
ENERG
ENERG
INDUS
OTHER
;{totals:
TABLE
The meaning of the rows of the AA block:
other current transfers
employers SSC contribution (incl. unemployment ins.)
net production taxes+ net domestic indirect taxes on
intermediate consumption less FTX
30
OTR
SSC
6063,85
631,82
1076,92
7772,59
AA(*,J)
ENERG
2048
679
PTX
236
INDUS
OTHER
2586,09
4442,23
40442,19 24766,84
21624,67 73503,61
64652,95 102712,68
various
sector
INDUS
OTHER
6109
16650
4218
16386
629
4191
TOTALS:
13.092
65.841
96.205
175.138
24.807
21.283
5.056
Fuel excise tax on the intermediate consumption by paying
sectors
Net export taxes by sectors (incl. a proportionate part of all
non-refundable indirect taxes on domestic products)
Net interest expenditure
Income tax
Net lending
Stock accumulation by acccumulating sectors
Amortization at current prices (not at historical costs as are in
the books)
Households housing investment expenditure (treated as transfer
to the housing sector)
Consumption transfers (presently empty)
Net investment transfers, taxes and grants
Employment (equivalent number)
Capital stock (at current prices, gross, i.e. representing the
capacity)
FTX
34
192
1251
1.478
ETX
-4
-64
-229
-297
INT
ITX
LEN
STR
210
191
-813
-119
1258
1174
-1693
932
-1559
4134
-7992
405
-91
5.499
-10.498
1.218
AMO
1664
4651
23258
29.574
HOU
0
0
-10374
-10.374
CTR
ITR
EMP
0
11
34
23882
0
80
638
139290
0
-508
2742
1785995
0
-417
3.414
1949167
CAP
Import from non-EU countries
IME
0
0
0
0
Total Imports by commodities (later only from EU countries)
Import duties for non-EU imports
Total import duties
Indirect taxes - subsidies on household consumption
(VAT+excise+local+e.t.c.-subsidies)
Government consumption (later if possible only social
consumption)
Stock change by commodities
Net export taxes on non-EU exports
Non-EU Exports at domestic basic prices (f.o.b.-net export
taxes)
Exports at domestic basic prices (f.o.b.-net export taxes)
check row: should be 0 when the income-expenditure balances
hold
Non-EU imports used in the household consumption
Duties on non-EU imports used in the GFCF investment
Total (later only EU-) imports used in the household
consumption
Duties on total (later only EU) imports used in the household
consumption
Non-EU imports used in the GFCF investment
Duties on non-EU imports used in the household consumption
Total (later only EU) imports used in the GFCF investment
Duties on total (later only EU) imports used in the GFCF
investment
IMP
5144
62313
12095
79.552
DUE
DUT
0
0
0
0
0
0
0
0
VAT
2815
8855
4727
16.397
SCO
10
909
38780
39.699
STC
ETE
-119
0
932
0
405
0
1.218
0
EXE
596
21705
6852
29.153
EXP
697
36347
10843
47.887
A60
4
64
229
297
ICE
DIE
0
0
0
0
0
0
0
0
IMC
589
13974
651
15.215
DUC
0
0
0
0
IIE
DCE
IMI
0
0
12
0
0
10987
0
0
722
0
0
11.722
DUI
0
0
0
0
;{OUTPUT:
12810
82105
238722
333.636
INVESTMENT MATRIX (BHM block):
Rows: product (by sectors of origin), columns: invesing
branch
TABLE
BHM(I,J)
ENERG
ENERG
INDUS
OTHER
;{totals:
TABLE
Labour income (without employers SSC):
Rows: strata, columns: industry (branch)
ALL_HH
TABLE
Household consumption (without employers SSC):
Rows: strata, columns: industry (branch)
ALL_HH
TABLE
Various household related transfers (LL block):
31
5
953
527
1485
W0(G,J)
ENERG
1677
C0(G,I)
ENERG
6840
LL(*,G)
INDUS OTHER
20
3879
2476
6375
223
12459
24059
36740
248
17.291
27.061
44.600
INDUS OTHER
19946
83764 105.387
INDUS OTHER
31953
82249 121.042
other transfers
employees and other social security contribution (after
unemployed, maternity grant, etc.)
interest income
interest tax
(Empty)
in-kind benefits (presently empty)
infrastructure development contribution (presently empty)
Income taxes
housing investment plus investment of self-employed (noncorporate) entrepreneurs
net borrowing
EMISSIONS OF INDUSTRIES
Rows: pollutant and fuel, columns: polluting branches
carbon dioxide
carbon monoxide
OTR
ALL_HH
70698
SSC
-16599
INT
ITX
MOR
COT
INF
PIT
2925
0
0
0
0
-22910
INV
-10374
BOR
-8084
TABLE
CE
(PO,EN,J)
{ktons}
ENERG INDUS
OTHER
CO.ENERG
14996
13132
27871 55.999
CM.ENERG
4,17
165,13
240,90
410
NO.ENERG
15,26
23,15
167,80
206
PT.ENERG
19,54
7,77
8,87
36
SO.ENERG
12,97
8,23
1,33
23
TABLE CE
H(PO,EN)
{ktons}
ENERG
CO
9783
CM
298,46
NO
14,17
PT
0,00
SO
7,14
TABLE
ZZ(*,I)
ENERG
INDUS
OTHER
CFS
0,60
0,60
0,57
HHINT
0
0
1
IEL
-1,00
-1,00
-1,00
MEL
0,50
0,36
0,30
MELE
0,10
0,10
0,10
REL
0,30
0,30
0,30
QM
1,00
1,00
1,00
QME
0,10
0,10
0,10
QZ
1,00
1,00
1,00
QZE
1,00
1,00
1,00
ZELDW
-4,26
-1,53
-1,79
ZELSW
2,88
-1,41
-1,05
ZELDE
-4,22
-4,22
-4,22
ZELSE
-0,93
-0,93
-0,93
ZXELW
1,00
1,00
0,79
ZXELE
1,00
1,00
1,00
EFUEL
0,90
0,90
0,90
ENEL
0,50
0,50
0,50
YEAR
SGOV0
target
value
for gov
nitrogene oxides
suspended particulates
sulphur dioxide
Household emissions
Rows: pollutant, , columns: fuel
carbon dioxide
carbon monoxide
nitrogene oxides
suspended particulates
sulphur dioxide
MISCELLANEOUS SECTORAL PARAMETERs
Fixed consumption share within total
Dummy for the banking sector
(not used)
Import-domestic price elasticity
SCALARS
budeget surplus / deficit
Target for wage+environmental tax (for recycling the tax by
reducing SSC)
WTX
M1B
INTROW
BOP
GINTE
net interest income of the rest of the world
current balance of foreign payments
net interest expenditure of the government
32
target
value
for wage
money
net
balance
governme
supply
interest
of
nt net
(change)
income
payments
interest
TABLE
carbon dioxide
nitrogene oxides
suspended particulates
sulphur dioxide
CO
NO
PT
SO
TABLE BC
carbon dioxide
nitrogene oxides
suspended particulates
sulphur dioxide
CO
NO
PT
SO
TABLE GC
carbon dioxide
nitrogene oxides
suspended particulates
sulphur dioxide
CO
NO
PT
SO
TABLE KC
carbon dioxide
nitrogene oxides
suspended particulates
sulphur dioxide
CO
NO
PT
SO
TABLE
CO.OTHER
NO.OTHER
PT.OTHER
SO.OTHER
AEIEX
(PO,J) prescribed
ENERG
INDUS
OTHER
0,0
0,0
0,0
0,3
0,3
0,3
0,68
0,68
0,16
0,83
0,42
0,42
(PO,J) Abatemen
t func
ENERG
INDUS
OTHER
0,0
0,0
0,0
0,208
0,208
0,208
0,036
0,036
0,036
0,013
0,013
0,013
(PO,J) Abatemen
t func
ENERG
INDUS
OTHER
0,0
0,0
0,0
-1,913
-1,913
-1,913
-1,07
-1,07
-1,07
-1,083
-1,083
-1,083
(PO,J) Abatemen
t func
ENERG
INDUS
OTHER
0,0
0,0
0,0
-0,184
-0,184
-0,184
-0,515
-0,515
-0,515
-0,139
-0,139
-0,139
ABC
(PO,I,J)
Abatem
ENERG
INDUS
OTHER
1,00
1,00
1,00
1,00
1,00
1,00
1,00
1,00
1,00
1,00
1,00
1,00
The meaning of the rows of the LL block:
OTR
other transfers
SSC
employees and other social security contribution (after unemployed, maternity grant, etc.)
INT
interest income
ITX
interest tax
MOR
Empty
COT
in-kind benefits (presently empty)
INF
infrastructure development contribution (presently empty)
PIT
Income taxes
INV
housing investment plus investment of self-employed (non-corporate) entrepreneurs
BOR
net borrowing
The meaning of the rows of other categories:
W0:
wages
C0:
household consumption at consumers prices
SGOV0
budeget surplus / deficit
WTX
target for wage+environmental tax (for recycling the tax by reducing SSC)
INTROW0 net interest income of the rest of the world
BOP
current balance of foreign payments
GINTE0
net interest expenditure of the government
CO
carbon dioxide
NO
nitrogene oxides
PT
suspended particulates
SO
sulphur dioxide
CM
carbon monoxide
33
The meaning of the labels of the ZZ block is the same as the meaning of the parameters with identical
name (see the parameter list of the HUGE model documentation).
In the “gamsfile” worksheet first we fill in the (composite) intermediate consumption (AHM0 block),
the (composite) investment matrix (BHM block).
In the AA block (where positive elements represent expenditures, while negative elements mean
revenues) we fill in the data of the value added in a category breakdown appropriate for the model. It
means primarily, that the (excise, etc.) indirect taxes on the intermediate fuel consumption is filled into
the row of FTX (by the paying industry), while the rest of the indirect taxes-subsidies on the
intermediate consumption are put together with the production taxes-subsidies in the row of PTX (the
model treats them as proportional to the gross output). Export related indirect taxes are put into the row
of ETX. Social security contribution data is directly taken from the I-O table value added section and
put into the row of SSC.
Then net interest expenditures are estimated (row of INT) from more aggregated data of the ATCredits.xls file, in the disaggregation the output shares are used.
Already computed 25-sector profit tax, household investment and amortization data are filled into the
row of ITX, HOU and AMO respectively (HOU is the revenue of the corresponding sector – i.e.
presently the real estate services).
Employment data by industries was given in Table 39 of the I-O table related matrices. This contained
the employment (in self-employed and employee break-down) data both in number of jobs and in fulltime equivalent persons. In the model we use the latter and filled them into row ‘EMP’ of the AA block.
Capital stock data by industry was estimated by dividing the corresponding industry’s amortization data
(‘Consumption of fixed capital’ in Table 12 of the I-O table related matrices) by the given sector’s
estimated amortization rate. With the exception of the housing (real-estate services) sector these rates
were assumed to be equal to the ones we had estimated for Hungary for 1998. For the housing sector
(where amortization data were quite significant) we assumed a 1 % amortization rate. The resulting data
were filled into row ‘CAP’ of the AA block5.
Components of the final demand (as found in the aggregate I-O table) are filled into the subsequent (5172.) rows. Here exports and imports are split to EU and non-EU parts, while the imports are further split
into main areas of use, i.e. consumption (denoted by C postfix), investment (I postfix) and other uses
(not displayed here but computed residually by the CALIBRAT file of the model’s GAMS program).
The last (70-72.) rows of the AA block, i.e. the rows of ROW, TIM and INK are reserved for the
possible future use, when the corresponding data for the tourist exports, imports and social in-kind
benefits should be separated out. Of the most important domestic final use components the social
consumption, the stock accumulation by industry origin, are filled in the SCO and STC rows
respectively. For a lack of better information, we assumed that all stock accumulation takes place in the
sectors of origin (i.e. the produces has to store them)6. By this assumption we determined the row of
STR. Net indirect taxes on private consumption (as computed in the I-O table’s indirect tax matrix) is
filled into the row of VAT. Of these the purchaser price of the private consumption can be determined
also, as shown by the row of C0 (below the BHM and W0 blocks). Since no duties are accounted for the
corresponding rows (starting with DU…) are empty7.
The W0 block is determined as the sum of the wages and the households mixed income (estimated value
of the imputed rent is separated out so that here only the labour related income is taken into account.
The row of the net investment transfers (ITR) contains only the net indirect taxes on investment minus
the investment expenditure of the government and NPISHs. The model treats these latter as transfers
from the budget and from the households to the corresponding industries (i.e. irrespective of the
institutional sector affiliation of the recipient organization).
Net lending data (row of LEN) of the financial services was taken directly from the NA. The net lending
5
Note, that in this way we estimated the total gross fixed capital stock in 2000 to have been 1949 billion €, which seems to be
consistent with the € 645 billion net capital stock estimated by Christophe Kamps (2005) in his ‘New Estimates of Government
Net Capital Stocks in 22 OECD Countries 1960–2001’ IMF Staff Papers (forthcoming) (Net capital stock is the sum of the
written-down values of all the fixed assets still in use is described as the net capital stock; it can also be described as the
difference between gross capital stock and consumption of fixed capital. Gross capital stock is the value of all fixed assets still
in use, at the actual or estimated current purchasers’ prices for new assets of the same type, irrespective of the age of the assets)
6
7
In any case we observed that services do not accumulate stocks, which is consistent with our solution.
Although the EU-collects duties on the external trade, no information is available how much of it can be related to the products used in the
individual member countries.
34
data of the NA for the corporate sector were disaggregated to the remaining 24-sectors according to a
sectoral proxy, which was computed as the difference between the disposable income (without taking
into account the OTR other transfers, which will be determined residually) and the accumulation (stocks
and fixed capital).
The other transfers by sectors (row of OTR in the AA block) were determined as residual. Its theoretical
content can be seen in the income distribution table: non-interest property income (dividend, retained
profits of foreign direct investment, insurance income, rent, etc.) +other current and capital transfers8 +
net sale of non-produced non-financial assets (land, gold, etc.).
The macroeconomic net interest income and deficit/surplus data for the government and the foreign
sector (SGOV0, INTROW0, BOP and GINTE0 in the input file) were taken from the NA.
5.3. Construction of the SAM for ATCEM-E3
All above data, formulas, which were used to compute the corresponding cell’s figure&
residually will be found in the input file to construct a square SAM (see Table 8). The original
of this SAM is in the “26->3 sector” worksheet of the ATDATABASE.XLS file.
Table 8.
8
Note that in the row of ITR we could take into account only parts of the investment related transfers.
35
Note, that the format of the presented SAM is somewhat unusual.
To visualize the difference, the following table shows the usual format and content of a SAM matrix:
SAM-AT
ENERG
Industries
INDUS
OTHER
W0(G,J)
Labour
SSC
LEN
ENERG
Capital
INDUS
OTHER
STR
PTX
FTX
ETX
ITX
Government
ITR
DUT
VAT
SSC
Households HOU
Foreign
IMP
Transfers
INT
OTR
Totals
Industries
ENERG INDUS
6064
2586
632
40442
1077
21625
1666
18760
679
4218
0
0
1485
6375
Labour
Capital
GovernmentHouseholdsForeign
Transfers
TOTALS
SCO
C0(G,I)
EXE EXP
INT OTR
OTHER W0(G,J) SSC LENENERGINDUSOTHER STC
4442
5
20
223 -119
10
6840
596 697
21365
24767
953 3879 12459 932
909
31953
24238 38806
179969
73504
527 2476 24059 405
38780
82249
6856 10850
262406
65464
85891
16386
21283
0
0
2032
2032
1485
6375
24059
24059
-119
932
405
1218
236
629
4191
5056
34
192
1251
1478
-4
-64
-229
-297
0
3
24541
0
24545
11
80
-508
0
-417
0
0
0
0
2815
8855
4727
16397
21283
21283
0
0
-10374 85891
0
0
0
75517
5144
62313 12095
334
79886
0
0
0
773
773
91
195
93
380
19811 167143 244814 85891 21283 0 1485 6375 36740 1218 40472
121042 31690 52385 334
0 830683
Table 9: ATCEM-E3 SAM according to ESA 95 methodology.
The construction of the SAM is the starting point of the model building work. In the base year, the definition of the set of prices is such that the balance of flows in
the SAM is satisfied in both constant and current money. The balance is conceived as the equality between the sum by row and the sum by column. In addition, a
SAM ensures the fulfilment of the Walras law in the base year, since by construction the algebraic sum of surplus or deficits of agents is equal to zero.
The SAM of ATCEM-E3 represents flows between production sectors, production factors and
economic agents. The production sectors produce an equal number of distinct goods (or services), as
in an Input-Output table. Only primary production factors namely, labour and capital are included, in
the SAM, and these are taking their respective shares in the sectoral value added. The economic
agents, namely households, firms, government and the foreign sector, are owners of primary factors,
so they receive income from labour and capital. In addition, there exist transactions between the
agents, due to taxes, subsidies and transfers.
The agents use their income for consumption and investment, and the consumption part is determining
final domestic demand. The foreign sector also makes transactions with others sectors. These
transactions represent imports (as a row) and exports (as a column) of goods and services. The
difference between income and spending (in consumption and investment) by an economic agent
determines its surplus of deficit.
Combining the Input-Output data, adapted to market prices and to national concept (instead of
domestic concept), and the data of the National Accounts by sector is allowing building the Social
Accounting Matrix. The allocation of the adapted Input-Output totals to the different sectors,
household, government, firms and rest of the world is rather straightforward from the NA data, and
can be summarised as follows:
• the total labor value added is allocated to the households except for the part going to the ROW
• the capital income is distributed between household, firms and government as in the NA
• the transfers of income by the government (interest payments, social security and other welfare
transfers) are all paid to the household, with the exception of the amount received by the ROW, the
same for the income/transfer payments by firms
• household are paying their social security contributions both to the government and to the firms
• households and firms pay direct taxes to the government
• the transfers received by the ROW are all coming from the government and the ROW payments are
allocated to the government
After this general discussion of the meaning and the purpose of SAM construction we have to explain
the differences between the SAM as used in the model and the described above standard SAM.
By purpose the SAM as used in the ATCEM-E3 model the usual distinctions between activities,
industries and consumption categories is not there, and hence our SAM does not contain the big blocks
of transformations of activities, industries and consumption categories from one breakdown to another.
Another unusual feature of our SAM is that the operating surplus is distributed further in the columns
of the industries, and the part spent on investment is given to their own industry’s investment account,
so that this account can spend it according to the typical industry’s (investment good) structure. In this
way the investment transformation matrix became the part of the SAM.
In our SAM there are only 2 collecting accounts (as the last rows): one for the interest payments, the
other is for the other transfers.
However in so far as the model reads in the necessary data directly from the mentioned blocks
(AHM,AA, BHM, C0, W0, LL, etc.) of the input file, these differences in the SAM are for checking
and illustrative purposes only.
Data files (stored in D:\ATCEM-E3\April27)
Name of the file
ATDataBase.xls
SAM-AT.XLS
SAM-AU00.xls
Meaning
Contains all manipulations of
the original I-O table related
multi-sectoral data)
Excel version of the models
direct input file
Income distribution based on
National Accounts
sema58_25.xls
Aggregation scheme from 58 to
25 sectors
AT-Credits.xls
Bank loans by branches
Remark
Make sure that the following files are open:
H:\April27\Matrizen_englisch\Table 8,
Table 9. and Table 18, 19 and 21
See above
(see Table....)
See 5.2.1. Computation of the Input-Output
table
Has to be disaggregated
37
5.4. Compilation of the electricity industry and emission related data of the model
5.4.1. Compilation of the electricity technologies related data of the model
Electricity is produced by different technologies, e.g., classic polluting coal power plants and cleaner
gas and biomass technologies. Energy policy frequently aims at shifting the technological structure of
electricity production (mainly toward ‘safer’ or ‘cleaner’ technologies). Therefore in the model we
disaggregated the inputs of the ‘electricity and heat production by the following technologies (named
after the type of energy source): biomass, oil, natural gas, coal, hydro and the other renewable sources
(wind, solar, geothermal, etc.). In the SAM-AT.XLS data file the ARAM block shows this
disaggregation.
However, these data are not included in the official data sources like the Input-Output Table and
related tables (Supply and Use matrices). In general, these data have to be estimated by relying heavily
on technological information and on related international literature. Therefore our estimates, outlined
below, were occasionally made by rather rough proportionality assumptions, so this block will have to
be revised in the future or at least when we want to model the shift between these technologies. Apart
from the problems with data availability, the interpretation of the available data (e.g. the meaning of
the ‘capital costs’) was sometimes difficult and the disaggregation required further methodological
considerations. Of these the most important thing was, that the ‘electricity and heat production’
industry includes not only the power plants, but also the distribution of the electricity (the grid, the
capacity balancing installations, the distribution networks, etc.) and the heat distribution pipelines.
Since we do not present these auxiliary activities as separate parts of the industry, each technology
should include a proportionate part of these activities (and their costs). A further important problem
was that in the I-O table the imported electricity is treated as intermediate input of the electricity
sector, so this amount of imported electricity also had to be distributed somehow across the given
technologies (both as costs and revenues).
After all these considerations, we determined the elements of the ARAM matrix in the following
sequence:
First, the output value of the largest technology (i.e. the hydroelectricity) is determined by multiplying
the yearly production of 41909 GWh (Statistical Yearbook’s Table 22.02.) and the estimated 0,08
Euro/kWh unit price (which contains also the cost of the mentioned auxiliary activities). To assess the
reliability of this figure, we determined the average (augmented) cost of the electricity by dividing the
EUR 6.447 Mill - an I-O table (activity) output value - of which we removed the 361 M € worth of
import (the price of which was much lower than the average) - by the60.162 GWh domestic
production as reported by Eurostat Energy balance. The resulting 10,1 € cent/kWh price is higher than
the hydroelectricity price which conforms the general view of the industry experts9.
Secondly, the output value of the renewables were determined in the way that its share is based on
table 22.02 of the Statistical Yearbook, where GWh data are converted to 3,6 TJ (for geothermal, suncollectors and heat pump’s output is not published but instead we estimated by the input).
The output of the remaining 4 technologies is estimated from the Austrian Greenhouse-gas Inventory
Report (Umweltbundesamt, 2006) in which Table 1.A(a) shows the fuel-input structure for heat and
electricity generation. We corrected these inputs by the estimated electricity generation efficiency
ratios derived from the Eurostat Energy balance and from 22.04 chart of the Statistical Yearbook.10
In the third step, we distributed the fuel costs across technologies: (almost) by definition all coal costs
were allocated to the coal-based electricity and heat generation technology. Similarly, gas input was
allocated exclusively to the gas-based technology, and oil to the oil-based technology. Wood is to be
used only by the biomass-technology11. Electricity input of the ‘electricity and heat production’
9
Note that by a similar method a 3.76 cent/MJ average price for heat could be estimated (which corresponds to 1 cent/kwh)
although in the heat production the loss ratio is lower, the price of one TJ of heat is also lower than the same amount of
electricity, so not the engineering efficiency is attempted to measure, but the economic efficiency: how much value is
produced of one TJ fuel
11
Note that the I-O table contained only 0.2 million Euro wood cost of the industry as opposed to the 19409 TJ shown by the
inventory report.
10
38
industry (which also includes the imported electricity) was distributed among the technologies
proportionately to their outputs12.
In the fourth step, we assessed the profitability of the technologies. Considering the data and the price
subsidy for wind energy, we concluded that the pure costs of biomass and renewable technologies are
equal to their output value (so that the price subsidy is needed to generate some return after the
considerable amount of invested capital). For the fossil-energy based technologies we assumed that
their profitability is equal to the industry’s average. From these profitability assumptions and the fuel
costs we could compute the sum of the O&M costs and amortization costs. O&M costs are the ‘nonfuel operating and maintenance costs’, which were split into their O&M and ‘amortization’
components according to the shares which we estimated from the Canadian Energy Research
Institute’s ‘Electricity Generation Technologies: Performance and Cost Characteristics’ paper written
in August 2005.
In doing so the original ‘capital cost’ figures (containing 5% return) were divided by 2 in order to
estimate the amortization cost (i.e. which contains 0% return). The O&M components were further
disaggregated to ‘material’ and ‘wage’ according to the proportions of the electricity industry total.
Note that during the calibration the obtained ‘material’ cost figures are further disaggregated (by
technologies) to the individual commodities (branch products) of the model according to the
proportions of the electricity industry total.
Finally, the costs of the hydroelectricity were determined as residual (total minus the rest). The largest
technology, the hydroelectricity was selected as residual so that the sum of errors that have taken place
in the estimates of the costs of the other technologies would result in a lowest percentage error in the
biggest hydroelectricity technology. In any case, this estimating procedure has to be revised in due
course; especially at present the amortization cost of the hydroelectricity industry looks too low
(although most of the dams are so old that their capital is fully depreciated and formally no
amortization is accounted for any more).
5.4.1. Compilation of the air pollution related data of the model
The abatement function parameters for marginal damage (ABC, DAMAGI, DAMAGH, BC, GC, KC)
are based on the GEM-E3 model’s values13 (which in turn are based on international literature). The
original figures based on 1995 € were adjusted by the inflation over the 1995-2000 period.
The air pollution data (TABLE CE, CEH, CETI, CETH, BIOEMI) are based on the mentioned
Austrian inventory report (CE and CEH are the point-source emissions of the production and the
households, while CETI and CETH are the transportation-related emissions of the production and the
households respectively). However, in many cases we had to disaggregate the available data not only
by the 25-sectors of the model, but in the case of the non-Greenhouse gases (CO,SO2,NOx) also by
fuel types – data that were lacking in the inventory report.
On the other hand, the inventory report did contain technological process-related emissions that are
not related to fuel combustion ( Table 2(I) ). After disaggregating these data to the model’s 25 sectors
(in a similar way as we had done with the fuel-related emissions), the resulting figures we filled into
the table of TECHEM. So far it has not been decided how the model should treat these emissions14 (in
any case, the importance of the technological process-related emissions is usually not too big, with
some notable exceptions like the CO2 -emission by metallurgy industry.
The inventory report also contained data on the CO2 and other greenhouse-gases emission of the
combustion of biomass (mainly wood)15. After disaggregating the production-related part to the
model’s 25 sectors (in a similar way as we had done with the fuel-related emissions) we filled the
resulting figures into table BIOEMI. The household related biomass-emissions are presented in the
COHHBI scalar. It is even more problematic how these biomass-related emissions can be treated in
12
Note that this ‘own-consumed electricity’ of the I-O table amounts to 35-40% of the totel output, while in the Energy
balance sheet only about 10% own-use and distribution loss of electricity is reported.
13
See the GEME3NOx and GEME3SO2 tables in the last rows of the SAM-AT.XLS file (which are “below the line”
matrices, i.e. they are not put to the input file)
14
As a preliminary solution they can be treated as a component of the PTX production-tax rate.
15
The other (CO,SO2,NOx,PT) biomass-related emissions are not separated out of the CE and CEH blocks, even more they
are distributed among the other fuels.
39
the model: the wood can not be treated as a normal fuel, partly because it is not properly marketed and
recorded (accounted in the I-O table), partly because wood is used for many other purposes, which
does not result in emission (furniture in the light industry, scaffolding in the construction industry,
etc.).
Indirectly from the energy balance sheets and the background information one can separate the plantand transportation related emissions (point sources and mobile sources). The transport (and fuel)
related emissions are further disaggregated to gasoline and gas-oil (diesel-oil) related emissions.
Since these emission data are only accounted and do not have any feedback to the rest of the model,
one can refine them separately whenever appropriate data become available.
5.5.The elasticities and other non-statistical data of the model
The values of elasticities used in this ATCEM version are taken from “Review of Literature on CGE
Models and Empirical Evidence on Elasticities” and via a formal calibration procedure are setting the
values for all other exogenous parameters.
The model uses the following elasticities and other non-statistical data:
CFS(I): The share of fixed consumption in the total personal consumption of the given good
This so-called “Frisch-parameter” can be a regulator parameter
HHINT(I): It tells which sector is the banking sector (if its value is 1, while the rest are zeros)
IEL(I): Interest elasticity (not used in the present version)
MEL(I),MELE(I): Relative price (or substitution) elasticity of the EU- and non-EU imports
REL(I): Capital – (Labour-Energy) elasticity of the nested production function
QM(I),QME(I): If the benchmark import-domestic rato was not optimal, it tells how many times
higher ratio would have been optimal
QZ(I),QZE(I): If the benchmark export-domestic ratio was not optimal, it tells how many times higher
ratio would have been optimal
ZELDW(I): Relative price elasticity of the non-EU exported good’s demand
ZELSW(I): Relative price elasticity of the non-EU export supply
ZELDE(I): Relative price elasticity of the EU exported good’s demand
ZELSE(I): Relative price elasticity of the EU export supply
ZXELW(I),ZXEL(I): Output-elasticity of the export supply
EFUEL(I): Inter-fuel relative price elasticity
ENEL(I): Substitution elasticity of energy and labour
The chosen values of elasticities are based on econometric estimates, on the international literature and
on speculative assumptions about the resulting shifts in behaviour. However, the model’s simple
functions are clearly far from any realistic behaviour, so the parameterisation can be viewed as rather
arbitrary. Also, the parameter values depend heavily on whether they express only direct or also
indirect effects. Long-run elasticities are usually higher (substitution has limits in a short run), so when
choosing the value, one has to bear in mind the time horizon of the simulation. In general, concrete
values have to be subjected to a sensitivity analysis to see if the whole set of parameters together
produce the expected results.
After completing the data set of the SAM-AT.XLS file, one can create the
H:\ATCEME3\ATCGE1\DATA.SAM text input-file for the GAMS program by running the “gams25”
macro of the Excel file. When doing this the macro program ask 2 questions. For the first, Yes has to
be given, while for the second “No” so that the input file may overwrite the possibly already existing
DATA.SAM file, but not to overwrite it in Excel format.
6. Model Calibration and Use
The calibration procedure requires data for a single year, which is considered as the base year of
simulation.
The calibration procedure is defined in such a manner that the model reproduces exactly the observed
statistics of the base year. The data for the base year 2000 are based on STATISTIK-Austria data and
on the database provided by the Austrian National Bank.
The first step in calibrating of the ATCE M - E 3 model is to define values for the elasticities that
determine all coefficients that do not correspond directly to observable variables and then to run the
calibration procedure. Most of such elasticities and control (regulating) parameters can be found in the
40
data (input) file described in the previous section. The rest will be presented in the next subsection.
6.1. Calibration of the data
The Calibration procedure, written as a separate module, has a recursive structure. A fundamental
equivalence may be drawn between the equations of the CGE model and the benchmark flows of value
in a SAM by assuming that in the benchmark year all prices are equal to unity. Therefore all prices are
treated as index numbers with a value of unity in the benchmark and all flows in the SAM are treated
as benchmark quantities. These assumptions allow the technical coefficients and elasticity parameters
of the utility and production functions to be solved for directly.
The calibration takes place in the CALIBRAT file of the model’s GAMS program. This file contains
many comments (either in lines starting with ‘*’ character or within the { } brackets). They explain
almost everything, so we summarize only the most important things and describe the logical structure
of this file.
First, coefficients for the individual technologies of electricity and heat production are computed by
using the input file’s ARAM block’s absolute data.
In the second part various scalar shares and elasticity parameters are set exogenously. These are
related to the generalized welfare (utility) function (environmental quality – leisure – consumption)
and the sectoral profit determination. Although in a long run (e.g. during the 20th century) higher
welfare involved higher demand for leisure (therefore less working hours) we do not attempt to
explain the employment by this theory. Instead the user of the model can set the employment level
either exogenously, or endogenously by using real-wage elasticities of the labour supply (ELS
parameter) or by leaving it a free variable16.
The determination of the profit also can be chosen from alternative solutions (see the model
description). The corresponding scalar parameter values (set in the CALIBRAT file) are the
following:
RRC : normal rate of return to capital (presently 3% of the gross fixed capital ; what is left above this
as surplus will be treated as a output-value proportional mark-up)
PRT : marginal general profit tax rate (presently 40% ; the profit tax of the benchmark real profit is
determined as the ration of the benchmark profit tax and the benchmark profit)
In the next section of the CALIBRAT file consistency of the input data are checked and if necessary
adjustments are made (the interest and the net lending of the banking sector is determined so that they
balance such payments in the whole economy; the other transfers of the sectors balance their incomes
and expenditures)
Then the scalar and sector specific parameters of the model are declared and then defined by setting
their values by the appropriate formulas. Here the benchmark output levels, unit wages and prices, tax
and subsidy rates, expenditure patterns, distribution shares, input coefficients, total factor supplies are
determined straightforwardly from the benchmark I-O table and SAM data. By taking into account the
computed prices, the product flows (quantities or volumes) are computed at the so-called ‘uniform’
(i.e. domestic basic) prices. Subsequently the share- and scaling parameters of the CES and CETfunctions are determined. As we presented in the model description, such functions represent the
production function and the household utility.
The environmental parameters of the model are determined in the ENVIRON file. Here apart from the
calibration, we find the declaration of the environmental variables of the model too.
16
Although the employment can be determined indirectly rather efficiently by fixing the capital supply and setting the
(energy-) labour-capital substitution elasticity at a low (about 0.3) level.
41
6.2. Using the model
Static Calibration to reproduce observed
equilibrium
Baseline Scenario to reflect
2000, and projections for the
future
Specification of counterfactual
scenario modifying assumptions of
the baseline to reflect:
• Policy change
• Institutional regime
Elasticities (taken from
the literature)
Benchmark
Equilibrium Data
S t
Policy appraisal, based on
comparison between references
Figure 7: Using the ATCE M - E 3 model
Once the model is calibrated, the next step is to reproduce as accurately as possible the base year for
which observations are available - 2000. Then we will define a baseline scenario for a future year that
is the final year of the model simulation (usually 2010). This simulation defines the model baseline
projection against which the policy simulations can be evaluated.
The “counterfactual” equilibria can be computed by running the model under assumptions that diverge
from those of the baseline. This corresponds to scenario building. In this case, a scenario is defined as
a set of changes of exogenous variables, for example a change in the tax rates. Changes of institutional
regimes, that are expected to occur in the future, may be reflected by changing values of the
appropriate elasticities and other model parameters that allow structural shifts (e.g. market regime).
These changes are imposed in the baseline scenario thereby modifying it. To perform a counterfactual
simulation it is not necessary to re-calibrate the model. The exact process of calibrating and running
ATCE M - E 3 is illustrated in figure 3.
6.3. Solution Algorithm
The model is formulated as a simultaneous system of equations with an equal number of variables.
However, the model uses the GAMS software, which can solve non-linear programming
(optimisation) problems (NLP). For the time being we instruct the GAMS program to treat the model
as an NLP problem and use the CONOPT17 algorithm to solve it (i.e. to find a feasible solution, which
happens to be the only solution). An alternative would be to use the PATH algorithm, which redefines
the model as a mixed non-linear complementarity problem18.
17
This solver is good for ‘scarce’ cross-effect matrices, which are characteristic of economic models
18
But for example in June 2007 the PATH could not find a solution for the base case, probably due to some unusually small data (close to 0)
42
6.4. Description of model’s files and the order of their opening/run
6.4.1. Data and Program files
(Stored in D:\ATCEM-E3\April27)
D:\ ATDataBase.xls (make sure that the following files are open:
H:\April27\Matrizen_englisch\Table 8, Table 9. and Table 18; This file contains all manipulations of
the original I-O table related multisectoral data)
D:\ SAM-AT.XLS (its cell values are visible when it is opened first, but when other referred files are
also open you have to make sure that the following files are also open19:
H:\April27\Matrizen_englisch\Table 8, Table 9. and Table 18.)
D:\SAM-AU00.xls (see Table....)
D:\sema58_25.xls (see the instructions for using aggregator matrices in the D:\Datahandling.doc
file)
D:\AT-Credits-1.xls
Program files (the GAMS software files are in the D:\GAMS\GAMS22.4 folder), while the
ATCE model’s GAMS coded application files are stored at H:\April27\ATCGE1 directory:
6.4.2. Running the model to reproduce the base year
The base-run of the ATCGE model can be done the easiest way by starting the
H:\April27\ATCGE1\CG.BAT file. This reads in the main program file called cgemodel.gms.
The latter invokes the following files (by an $include filename statement in the GAMS code):
Run
Name of the file
Function of the file
remark
sequence
1
declare
2
data.sam
3
calibrat
4
environ
5
model
{invoked by the model file }
6
Closure
7
Initval
After solving the model the main program (cgemodel.gms) reports the results by using the following
files (H:\ATCEME3\ATCGE1):
Preport
in.put
Print
The results as generated by GAMS can be found in the cgemodel.lst file. In this file one can find those
parameter and variable values (incl. statistics), which in the program were listed by a DISPLAY
statement. Besides these more appropriately formatted and arranged results (including the various
indices, differences, sums, averages and other statistics) are displayed in the RESULTS.BAS and
RESULTS.SIM files (base simulation and counterfactual simulations respectively). However, if the
program contain errors, they are listed too in the cgemodel.lst file marked with 4 stars (****) and their
line (serial) number, so that by searching these string one can locate and fix the problem easier.
The RESULTS.BAS file displays the value of the parameters and variables according to the following
structure20:
- option parameters (to check the actual closure specification)
- industry specific relative parameters (indices, shares, coefficients, etc.) (in alphabetic order,
19
If all the data files are stored in a separate directory and the Excel file cell-references are refreshed accordingly, then – according to our
experiment – the opening of Table 8 suffices)
20
Postfixes in lower case letters denote certain elements of the given category: e=EU, w=non-EU, o=”other uses” (i.e. apart from household
consumption and investment), no=NOx, so=SO2, pt=PT (particulates). Wholly lower case categories represent derived categories
(statistics) which are neither parameters nor variables
43
-
see GROUP1-GROUP6)
scalar relative parameters related to households (STRATA1)
scalar absolute parameters related to households (STRATA2)
industry specific absolute parameters (measured in monetary values (M Euro) (in alphabetic
order, see GROUP7-GROUP8)
other scalar parameters (SCALARS1-SCALARS6)
price variable values (PRICES1, PRICES2)
absolute variable values (USES1, USES2)
household consumption by groups (CONSUMP)
sectoral income distribution matrix (SAMSEC)
institutional sector’s income distribution matrix (SAMSUM)
main macroeconomic indicators (use categories, factor utilization, factor prices, savings,
environmental categories21, household utility levels)
The meaning of the rows of the SAMSEC and SAMSUM matrices are the following:
SOURCES_
: sources (gross output and import for the foreign sector)
INTERM_C
: intermediate consumption (at basic prices)
FT_PR_DI
: foreign price difference (import at domestic-world market prices)
NET_PR_T :
net product and production tax on intermediate use
ENVIRO_T:
environmental taxes (incl. the FTX fuel tax on intermediate uses)
EXP_SUBS:
net export subsidies
SOC_SEC_ :
social security contribution
GR_EARNI
: Gross earning (labour income)
INTEREST :
net interest income
INCOMETA
: income taxes (personal and corporate)
CON_TRAN
: consumption transfers in the industries (empty)
INV_TRAN
: net investment transfers
ASSET_TR :
asset transfers (empty)
OTH_TRAN
: other transfers
N_BORROW
: net borrowing
STOCK_AC
: stock accumulation
FINAL_DE :
other final demand (investment, export, final consumption)
COLTOTAL
: column total of the above (should be zero)
Gr_op_su
: gross operating surplus (sum of the first 9 rows)
Ret_earn
: retained earning (sum of the first 14 rows)
The PREPORT file shows how the cells of the SAMSEC and SAMSUM matrices are actually derived
from the model parameter and variable values. Note that due to the model’s special (appropriate to the
analysis) definitions, certain categories of the official statistics can only be approximated by the
model’s categories (e.g. the GDP which is at users prices, while the model does not compute for
example the user price of the investment).
6.4.3. Simulations by the model
By starting the H:\ATCGE1\CGR.BAT file we can run counterfactual simulations with the ATCGE
model. This reads in all the information generated by the base run (stored in internal format files) and
invokes the main program file called cgecont.gms. This in turn invokes (in its first line) the
SCENARIO file. In this file one can modify the values of the option parameters (i.e. effectively
changing the model specification) and of the normal parameters.
The processed and formatted output can be found RESULTS.SIM file.
The structure of the RESULTS.SIM file is the following:
The first part is the same as in the RESULTS.BAS file (but obviously now containing the values used
or computed in the simulation), but after the “CLOSURE OPTIONS and RESULTING INDICES
21
In the case of the industry specific categories the electricity sector’s value is displayed
44
(RUN 1 / BASE)” title the simulation/base run ratios are displayed22.
Finally the macroeconomic indicators are listed again, displaying not only the values computed in both
runs (base and simulation), but in separate columns their ratio and difference can be found too.
7. Validation of the model by Scenario building and analysis of the results
7.1. Scenario building
We will be distinguishing two cases: the Base and the Scenario cases.
The model output for the base case matches the economic data for the year 2000.
To evaluate changes in the main economic variables we will be constructing a scenario case for the
year 2010.
The starting energy related table 1 has been taken from the “Vorstudie für einen nationalen
Biomasseaktionsplan für Österreich”. We have assumed the following framework values for energy
from biomass:
Table 10. Energy from biomass
2000
2010
2020
Heat produced from wood 100 PJ 140 PJ 200 PJ
Electricity from wood
3,2 PJ 44 PJ
55 PJ
Bio fuels
1,0 PJ 34,1 PJ 75 PJ
The estimated Real GDP Growth Rates and Estimated End-Use Sector Growth Rates (%) – Base, Low
and High Scenarios are presented in Table 2 below:
Table 11.
Type of Growth Rate
BASE Scenario
GDP Austria
LOW Scenario
GDP Austria
HIGH Scenario
GDP Austria
2001 2002 2003 2004 2005 2006-2010 2011-2015 2016-2020
-
1.00 1.20 2.80 2.20 2.20
2.10
2.00
1.90 1.80
1.00 1.20 2.80 1.96 1.96
1.88
1.5
1.43 1.00
1.00 1.20 2.80 3.20 3.20
3.00
2.90
2.80 2.60
Sources: WIFO (2002), NOBE (2002), own estimates
7.2. Scenario assumptions for the 2000-2010 period:
According to WIFO forecast total GDP increases by 2.5 per cent a year or over 2000-2010 period with
the total of 28 %.
We assume that the electricity and heat consumption will increase with the same rate.
From the energy balance for 2000, the total (net) electricity and heat consumption was 191 PJ and 44.6
PJ respectively. Therefore, according to our assumption, by 2010 the consumption of electricity and
heat will reach the 244.5 PJ and 57 PJ level.
Within that the electricity from wood will reach the amount of 44 PJ according to the national biomass
plan (AEE).
Heat produced from wood in 2010 can be estimated at 25 PJ (the initial about 5 PJ will increase
between the 46 per cent increase of total wood consumption and the 14 fold increase of wood
produced electricity).
Therefore the total electricity and heat industry output generated from wood is 44 PJ+25 PJ.
Since heat is cheaper than electricity (the wood to electricity conversion efficiency is around 50 % of
the wood to heat conversion efficiency) we can compute the economic equivalent output of the
electricity and heat industry in 2010 as 244.5+0.5*57=277.8 PJ.
22
It has to be stressed especially in the case of the price variables, the value of which are also mostly around 1 in the base
year, so the user can easily confuse it with these ratios
45
Within that 44+0.5*25 = 66.5 PJ is produced from wood. This is 66.5/277.8 = 0.237 i.e. 23.7 per cent
of the total electricity and heat output.
In the scenario file we modified the production structure of the electricity and heat industry so that in
the database for 2000 the corresponding shares for 2010 are applied. Since the output was € 7.792 Mill
and the original biomass (wood) produced output was estimated (as described above) to have been €
413 Mill, the increase (shift) could be estimated as 7792*0.237 – 413 = € 1.438 Million this shift in
production should be balanced by the other energy production technologies have. We assumed that by
2010 electricity and heat produced by coal would practically cease to exist. It means €1300 Mill worth
of output. The remaining € 138 Mill is taken out of the oil-based electricity and heat production.
For the households we assumed that in their preferences the weight of the forestry products (wood) is
doubling by 2010. This we could introduced in the input file by the recalculation of the ACg,i
parameter values. Also we assumed that the price of wood would appreciate by 50%, which we could
achieve by increasing the forestry’s PROFC mark-up parameter by 0.5.
In the Scenario we modified the default setting of the closure (i.e. which assumed that households
optimise their leisure-consumption-environmental quality composite utility) so that the households
environmental expenditure (QEXP) and savings rate be exogenous.
Since the shadow price of the generalized household utility (LAMBDA) has become irrelevant, its
value is also set exogenously to 0.
These closure settings correspond to equations equation (91b), (92b) and (93a) and in the GAMS-code
of the model (in the CLOSURE file) these formulas are selected by the following setting of the option
parameters: OPTC=1013, OPTE=10122, OPTU=2011.
To properly address the Austrian situation we assumed that the real-wage is exogenous (trade-unions),
so that labour- supply adjusts to the demand (mainly through immigrant workers). This is achieved by
the OPTW=2211 parameter setting and corresponds to equation (90c).
Also to take into account that Austria’s main trading partners are also in the Euro-zone, we assumed
that the real (foreign) exchange rate does not change. This is achieved by the setting OPTV=2241 and
corresponds to equation (86b). As a consequence the trade balance is exogenously provided.
In CGE models growth is based on the increase of the factor supply. The 30 % increase of the GDP in
the 2000-2010 period (about 2.5 % a year growth rate) forecasted by the WIFO is implemented by
increasing the capital supply (index) to 1.3 (see the S ("KU")=1.30; equation in the SCENARIO file).
Note that - as mentioned – labour supply is exogenous, so it is irrelevant to modify the labour supply
related parameters.
Nevertheless, we still had to decide how the government consumption (or deficit) will develop. For the
time being, we assumed that it will grow in parallel to the GDP, i.e., by 30%. This is implemented into
the scenario by the TG=TG*1.3; equation. The same 30 % increase we assumed for the government
expenditures on cash benefits (pensions, unemployment aid, child benefit, etc.). This is implemented
into the scenario by the GOTRAN=GOTRAN*1.3; equation.
As related to the behaviour of the rest of the world we assumed that world demand increases by 30 %
for each trade zone and commodity. This assumption is implemented into the scenario by the
zd(i,"w")=zd(i,"w")*1.3; and zd(i,"e")=zd(i,"e")*1.3; equations.
All the listed settings can be found in the SCENARIO file.
Finally we have to emphasize that when analysing the effect of energy-environmental policy
scenarios, it is only of secondary importance how the baseline (macroeconomic) forecast is made, i.e.
in which all parameter changes are included with the exception of the energy and environmental policy
related parameter values. The important is to compare the results of the baseline with the full forecast
to be able to assess the differential effect of these energy and environment related (policy and nonpolicy parameter) changes.
7.3. Analysis of the results
The results are stored in the file RESULTS.SIM. An extract of the most important of them is presented
46
in the following table.
Table 12.
Main simulation results
Data are in M €, if not indicated (emissions in kt, for CO2 Mt), In the last column the index is divided by
the assumed GDP growth (1.3)
Simul to
Index, %
Code
Base Scenario Difference
Base/GDP
Category
Simul./Base
growth
Imports total
M
79396 105573
26177
133
1,0231
Imports from EU
Me
53025
70578
17554
133
1,0231
Exports total
Z
77252
98173
20921
127
0,9769
Exports to EU
Ze
48018
60639
12621
126
0,9692
Gross output
X
362916 480882
117965
133
1,0231
Energy consumption
E
16204
20108
3904
124
0,9538
Private consumption
ct
104610 140677
36067
134
1,0308
Variable consumption volume
CVOL 43815
79883
36067
182
1,4000
Government consumption
CGOV 39699
51609
11910
130
1,0000
Capital investment
GINV
44291
60044
15753
136
1,0462
Balance of trade
BTR
-2511
-3674
-1163
146
1,1231
GDP at uniform price
GDP
187674 246853
59180
132
1,0154
Labor utilization index
LU
100
133
33
133
1,0231
Capital utilization index
KU
100
130
30
130
1,0000
Standard return to capital index
R
100
104
4
104
0,8000
Foreign exchange rate index
V
100
100
0
100
1,0000
Standard wage index
W
100
100
0
100
1,0000
Consumer price index
CPI
100
100
0
100
1,0000
Financial saving of industries
SSEC -10178 -11779
-1601
116
0,8923
Financial saving of government
SGOV
-3794
-4463
-669
118
0,9077
Financial saving of the foreign sector
SROW
5864
7013
1149
120
0,9231
Financial saving of the households
SHOU
8082
9713
1631
120
0,9231
Exports to non-EU countries
Zw
29235
37535
8300
128
0,9846
Imports from non-EU countries
Mw
26372
34995
8623
133
1,0231
Terms of trade index non-EU zone
TTRw
99
102
3
103
0,7923
Terms of trade index, EU zone
TTRe
100
105
5
105
0,8077
Balance of trade, EU-zone
BTRw
2561
3206
645
125
0,9615
Balance of trade, non-EU-zone
BTRe
-5072
-6880
-1808
136
1,0462
Price index of coal
PCOAL
100
100
0
100
0,7692
Price index of natural gas
PNGAS
100
100
0
100
0,7692
Price index of electricity
PELEC
100
101
1
101
0,7769
Price index of refinery products
POILS
100
100
0
100
0,7692
Total point-SO2-emission of industries
TEISO
20,79
25,82
5,03
124
0,9538
Total point-NOx-emission of industries
TEINO
78,7
101,17
22,47
129
0,9923
Total point-CO2-emission of industries
TEICO 50,75
55,59
4,85
110
0,8462
Total point-CO-emission of industries
TEICM 267,51 351,97
84,46
132
1,0154
Total point-SO2-emission of households
TEHSO
7,13
5,85
-1,29
82
0,6308
Total point-NOx-emission of households
TEHNO 14,16
11,6
-2,56
82
0,6308
Total point-CO2-emission of households
TEHCO 16,36
17,72
1,36
108
0,8308
Total point-CO-emission of households
TEHCM 298,24 244,33
-53,91
82
0,6308
Total transport-related SO2 emission
KOZLSO 0,58
0,75
0,18
131
1,0077
Total transport-related NOx emission
KOZLNO 80,48
105,05
24,57
131
1,0077
Total transport-related CO2 emission
KOZLCO 16,61
21,69
5,07
131
1,0077
Total transport-related CO emission
KOZLCM 104,67 136,62
31,95
131
1,0077
From the results one can see that macroeconomic categories are developing in a reasonable way.
For example, in relation to the assumed GDP growth (30%) the labour demand increases by 2,31 %.
47
Here is to mention that employment is measured in equivalent employees, where higher productivity is
expressed as higher number of employees with initial productivity.
Investment (GINV) in relation to the GDP increases by 4,62 %, the total exports with 0,9769 stayed
approximately constant with the EU exports - 0,9692 - a bit lower, import increases by 2,31 % (mainly
due to the highly import intensive boost in investment), private consumption increases by 3,08%. The
financial saving of government decreased by 9,23% those of the foreign sector and of the households
by 7,69%. The standard rate of return to capital (R) increases by 4% (i.e. from 4% to 4.16%), while
the low trade deficit (BTR) grows by 12,31%.
As far as energy and environmental categories are concerned, we can highlight the following:
Intermediate demand for energy increases by 28%, or 8 % less than GDP growth (i.e. the GDPelasticity is 0,75). Within that coal consumption decreases by 38%.
As a result of the low energy consumption growth, emissions (adding together the pointsource emissions of the industries, of the transportation and of the households) increase much
less than the GDP.
The government budget deficit grows only by 16 %, just half as fast as GDP growth. As a
consequence, the deficit/GDP ratio decreases considerably. Saving of the foreign sector (balance of payment) also increases less (20%) than GDP growth, so its ratio to GDP also
improves.
In general, the results seem to be encouraging. However, one has to bear in mind that if we
had adjusted each quantity- and real-income related parameters by the same ratio as we did
with the capital supply, then the results would show very small structural changes, only the
differential effect of the biomass-related changes would cause structural changes. So in the
future such scenarios will have to be developed and the results can be decomposed to the
various causes or explanatory factors according to the comparison of their results.
Naturally, the data and the model need some more revision and testing, but with the support
of interested institutions a quite adequate policy analysis tool can be developed within a rather
short time.
As far as the forestry-related sectors future is concerned, first we observe that (due to the
higher PROFC profit margin) the domestic producer price of forestry increases dramatically.
This leads to the fall of demand for forestry products, so domestic delivery increases only by
11 % (much less than GDP growth). Import and export prices do not follow this development,
so a substitution between import and domestic products and between domestic supply and
export supply takes place. This is partially counterbalanced by the assumed shift in the
consumer’s preferences toward the forestry products. Eventually the output of the forestry
sector decreases by 2.8 %. Higher prices for forestry products is built into the price of the
wood and paper products. So demand for them also increases less than the average.
48
Table 13. Main simulation Scenario results for the forestry-related sector
Simulated Scenario data are in percentage indexes relative to the base year, or in units indicated
Base year data are in M €, if not indicated (emissions in kt, for CO2 Mt); in the last three columns the index is divided by the assumed GDP growth (1.3)
Category
Imports of the given products
Imports of the given products from EU
Exports total
Exports to EU
Gross output
Energy consumption
Private consumption
Intermediate consumption
Deliveries of the forestry-related products to the
Capital investments
Labour demand, thousand persons
Demand for Capital of the sectors
Domestic use of the given product
Average producer price index
Domestic producer price index
Export price index
Import price index
Price index of the domestic and import composite
Consumer price
Gross operating surplus of the sectors
Base case Forestry
Wood- Pulp and paper
products
industry
Forestry/1,3
Woodproducts/ 1.3
Pulp &
paper /1.3
Code
M
Me
3629
2370
FORES
1,627
1,627
WOOD
1,414
1,414
PAPER
1,323
1,323
FORES
1,251538
1,251538
WOOD
1,087692
1,087692
PAPER
1,017692
1,017692
Z
Ze
X
E
CT
IC
5840
3922
12880
1955
771
9064
0,670
0,670
0,972
0,883
2,196
0,971
1,191
1,191
1,215
1,099
1,289
1,265
1,241
1,241
1,243
1,159
1,316
1,283
0,515385
0,515385
0,747692
0,679231
1,689231
0,746923
0,916154
0,916154
0,934615
0,845385
0,991538
0,973077
0,954615
0,954615
0,956154
0,891538
1,012308
0,986923
B
502
1,356
1,356
1,356
1,043077
1,043077
1,043077
L
K
HTS
PA
P
PZ
PWM
PHM
PC
75
22703
10670
1,000
1,000
0,999
1,002
1,001
1,241
1,059
1,007
1,110
1,670
1,682
1,303
0,998
1,455
1,594
4,732
1,291
1,249
1,274
1,071
1,080
1,059
0,998
1,060
1,062
1,267
1,289
1,260
1,286
1,030
1,033
1,031
0,998
1,015
1,012
1,270
0,814615
0,774615
0,853846
1,284615
1,293846
1,002308
0,767692
1,119231
1,226154
0,993077
0,960769
0,98
0,823846
0,830769
0,814615
0,767692
0,815385
0,816923
0,97461
0,991538
0,969231
0,989231
0,792308
0,794615
0,793077
0,767692
0,780769
0,778462
0,97692
49
References:
1 . Tamás Révész, Ernö Zalai & A t t i l a Pataki, “The Hungarian General Equilibrium
(HUGE) Model”, Corvinus University of Budapest, M a y , 1 9 9 9
2. Prof. Ernö Zalai, Dr. Tamás Révész, “Sources and methods of data base compilation
for GEM-E3 type multi-country CGE-models”, Corvinus University of Budapest,
Revised Draft, December 2006
3. Ernô Zalai, “The General Equilibrium Modelling Approach”, Budapest University of
Economic Sciences, Harvard Institute for International Development (HIID),
Hungary Environmental Economics Policy Program, January 6, 1998
4. Morris, Glenn E., Tamas Revesz, and Ernö Zalai, “Integrating Environmental
Taxation with Fiscal Reform in Hungary: Simulations with a Computable General
Equilibrium Model (FEIMX)”, Harvard Institute for International Development,
Hungary Environmental Economics Policy Program,
5. Prof. Ernö Zalai, Dr. Tamás Révész, “Review of Literature on CGE Models and
Empirical Evidence on Elasticities”, Last revision October 2005
6. BUES, ERASME, NTUA, KUL, PSI, ZEW, “ G E M - E 3 General Equilibrium Model for
E nergy-E conomy-E nvironment interactions”, the project CORE, 2000
7. Richard Rosenthal, GAMS – A User’s Gude, GAMS Development Corporation, USA,
DC, 2006
8. Paul Schreyer & Colin Webb, Capital Stock Data at the OECD – status and outlook,
OECD, 2006
9. Laurent Crategny, Mark Horridge & Thomas Rutherford, Solving MPSGE Models Using
GEMPACK, May 2004
10. Thomas Rutherford, Applied General Equilibrium Modeling with MPSGE as a GAMS
Subsystem, March 1997
11. Christoph Böhringer, Thomas Rutherford & Wolfgang Wiegard, Computable General
Equilibrium Analysis: Opening a Black Box, DP No. 03-56, ZEW, Mancheim
12. Prof. P. Carpos & all, The GEM-E3 model: Reference Manual, Technical Uni of Athens
& the European Union’s Core team
13. Austrian National Bank - Financial Accounts, 2007:
http://www.oenb.at/en/stat_melders/datenangebot/gesamtwirtschaftliche_finanzierungsre
chnung/geldvermoegen/geldvermoegen_und_verpflichtungen.jsp#tcm:16-4964
14. STATISTIK AUSTRIA, Statistisches Jahrbuch 1999-2007: http://www.statistik.at/
15. Umweltbundesamt, Austria’s National Inventory Report 2006, Submission under the United Nations
Framework Convention on Climate Change, 2007, http://www.umweltbundesamt.at/en/
16. 1993 System of National Accounts, UN Statistics Division,
http://unstats.un.org/unsd/nationalaccount/sitemap.htm
17. Standard-Dokumentation Metainformationen (Definitionen, Erläuterungen, Methoden, Qualität) zur
Integrierten NAMEA (National Accounting Matrix including Environmental Accounts), Diese
Dokumentation gilt ab Berichtszeitraum: 2003; Bearbeitungsstand: 20.11.2006; Statistik Austria;
Bundesanstalt Statistik Österreich; www.statistik.at
18. Ian Sue Wing, Computable General Equilibrium Models and Their Use in Economy-Wide Policy
Analysis: Everything You Ever Wanted to Know (But Were Afraid to Ask), Center for Energy &
Environmental Studies and Department of Geography & Environment, Boston University
19. Canadian Energy Research Institute, “Electricity Generation Technologies: Performance and Cost
Characteristics, Prepared for the Ontario Power Authority by Abbas Naini & al., August 2005,
http://www.powerauthority.on.ca/Storage/15/1107_Part_4.3_CERI_Report_to_OPA_August_24_2005_
D.pdf
50
Glossary
Basic prices: The amount received by the producer for a unit of goods or services minus any taxes
payable plus any subsidy receivable on that unit as a consequence of production or sale (i.e. the cost of
production including subsidies). As a result the only taxes included in the basic price are taxes on the
production process - such as business rates and any vehicle excise duty paid by businesses - which are
not specifically levied on the production of a unit of output. Basic prices exclude any transport charges
invoiced separately by the producer.
Government final consumption: This class of expenditure presents public spending on Public
Administration (including Defence) and Health services. Among the others Government expenditure
covers the provision of Education, Social Work, Public Recreation services, etc.
Changes in Inventories: Inventories consist of finished goods (held by the producer prior to sale),
work in progress and products (materials and fuel) acquired from other producers to be used for
intermediate consumption or resold without further processing.
Compensation of Employees: The total remuneration payable by an employer in return for their
employees' labour. In addition to wages and salaries, this classification also records payment in kind
and employers' contributions to social security funds and privately funded insurance schemes.
Distribution Margins: Transportation, storage and distribution do not change the physical appearance
or nature of goods but change their time or place. The value added by the distributive industries is
calculated as the difference in value of the good when it started and when it finished being held or
moved i.e. the actual receipts from sales less the purchase of goods for resale less recurrent losses due
to wastage, theft, etc plus net change in distributors' inventories.
Final Consumption: The expenditure on goods and services that are used for the direct satisfaction of
individual needs or the collective needs of members of the community, no further value is added to
these goods and services by domestic end consumers.
Financial Intermediation Services Indirectly Measured (FISIM):
The output of many financial intermediation services is paid for not by charges, but by an interest rate
differential, i.e. the difference between interest rates offered to borrowers and investors. The value
added of these industries is shown including their interest receipts less payments, in effect imputing
charges for their services.
Intermediate Consumption: Purchases made by industry for use in their production processes.
Gross Fixed Capital Formation (GFCF): Gross Fixed Capital Formation relates principally to
investment in tangible fixed assets such as plant and machinery, transport equipment, dwellings and
other buildings and structures. However, it also includes investment in intangible fixed assets,
improvements to land and also the costs associated with the transfer of assets. The investment relates
to assets which are used repeatedly in the production process for more than one year and as such
covers such purchases as: software, mineral exploration and purchases of dairy cattle.
Gross Operating Surplus:
This classification is broadly analogous to profit but is more accurately defined as the surplus (or
deficit) on production activities before account has been taken of interest, depreciation, rents or
charges. This class includes mixed income where, particularly in the case of sole proprietors,
separately identifying profits and wages may not be possible.
Household Final Consumption Expenditure:
Household final consumption expenditure represents consumer spending. It includes imputed rent for
the provision of owner-occupied housing, income in kind and consumption of own production. Also
included is spending by non-Austrian households being presented within the relevant tourist
expenditure category. The household sector covers not only those living in traditional households, but
also those people living in communal establishments, such as retirement homes, boarding houses and
prisons.
NPISH (Non-Profit Institutions Serving Households):
NPISH are organisations that provide goods or services to households either free or at prices that are
not economically significant. There are three broad types of NPISH:
51
1. Academic establishments, principally universities, higher education and further education
colleges;
2. Associations which provide benefit primarily for their members and are financed mainly by
subscriptions, including professional and learned societies, trade unions, churches and
religious societies, housing associations, non-collecting friendly societies, social or
recreational organisations and sports clubs. This class excludes those bodies where
membership gives a right to a predetermined range of goods or services; for example book
clubs and the Automobile Association.
3. Bodies that serve the interests of people other than their members, including charities and
similar relief and aid organisations financed by donations from the public, government and
business.
Purchasers' prices:
The prices paid for products at the point of sale, after the addition of any taxes less subsidies on
products and after the addition of any other costs such as distributors' trading margins. The Supply
Table shows the transition from basic to purchasers' prices.
Taxes less subsidies on Production:
These are compulsory and unrequited payments levied on the production and importation of goods and
services, the employment of labour, the ownership and use of buildings or other assets used in
production. They are payable whether or not profits are made. Subsidies on production are defined as
payments made to producers with the objective of influencing their level of production, their prices or
to assist with purchases of the factors of production.
Taxes less subsidies on Products:
The main taxes on products are value added tax (VAT), and taxes on alcoholic drinks, tobacco,
hydrocarbon oil, betting, and stamp duties. Subsidies on products are payable per unit of good or
service produced, with the aim of reducing the purchase price to the consumer. The main product
subsidies, which are paid by the European Union and Central/Local Government, relate to agriculture,
transport and housing.
Valuables:
Goods of considerable value that are not used primarily for production or consumption but are held as
stores of value over time. They consist of precious metals, precious stones, jewellery, works of art, etc.
52
ANNEX – Model’s Equations
53
3
3.1
Model Equations
Environmental Modul
T XEN Vpo,j = TXENV0po,j
(1)
T XEN V Hpo = TXENVH0po
(2)
GEN V T Xpo = 0
(3)
or alternatively
T XEN Vpo,j = GEN V T Xpo
(1’)
T XEN V Hpo = GEN V T Xpo
(2’)
T EIpo = EMBASEpo ∗ (1 − EMREDUpo )
(3’)

T XREN Vpo = CP I ∗ 
X
∗
i∈EN
X
j

T XEN Vpo,j ∗ (1 − AEIpo,j ) ∗
CEpo,i,j ∗ XHMi,j
+ CP I ∗ T XEN V Hpo ∗
T EIpo =
X
j
T EHpo =
"
X
g
CABpo,j =
(1 − AEIpo,j ) ∗
"
X
i∈EN
(1 − AEHpo,g ) ∗
!
X
g
(1 − AEHpo,g ) ∗
CEpo,i,j ∗ XHMi,j
X
i∈EN
CEHpo,i ∗ Cg,i
X
i∈EN
#
#
−BCpo,j
∗ (1 − AEIpo,j )1+GCpo,j + KCpo,j
1 + GCpo,j
CABHpo,g =
−BCHpo,g
∗ (1 − AEHpo,g )1+GCHpo,g + KCHpo,g
1 + GCHpo,g
(4)
CEHpo,i ∗ Cg,i
(5)
(6)
(7)
(8)
M ABCOSpo,j = AEIpo,j ∗ BCpo,j ∗ (1 − AEIpo,j )GCpo,j + CABpo,j ∗ P ABpo,j
(9)
19
ABIi,j =
X
po
ABHi,g =
"
ABCpo,i,j ∗ CABpo,j ∗ AEIpo,j ∗
X
po
"
i∈N EN
CEpo,i,j ∗ XHMi,j
X
i∈EN
CEHpo,i ∗ Cg,i
#
(10)
#
(11)
P HMi,O ∗ ABCpo,i,j
X
P ABHpo,g =
i∈EN
ABHCpo,i ∗ CABHpo,g ∗ AEHpo,g ∗
X
P ABpo,j =
X
i∈N EN
(12)
TXCi ∗ P HMi,C ∗ ABHCpo,i
(13)
P HM Ui,j = P HMi,O ∗ (1 + FTX0i,j ∗ IF T X) +
X
+
CP I ∗ T XEN Vpo,j ∗ (1 − AEIpo,j ) ∗ CEpo,i,j +
(14)
po
+
X
po
P ABpo,j ∗ CABpo,j ∗ AEIpo,j ∗ CEpo,i,j
i ∈ EN
P HM U Ci,g = TXCi ∗ P HMi,C +
X
+
CP I ∗ T XEN V Hpo ∗ (1 − AEHpo,g ) ∗ CEHpo,i +
(15)
po
+
X
po
P ABHpo,g ∗ CABHpo,g ∗ AEHpo,g ∗ CEHpo,i
P HM U Ci,g = TXCi ∗ P HMi,C
0=










AEIpo,j − AEI0po,j













i ∈ EN
i ∈ N EN
(16)
if

1−EMAXpo,j

AEIpo,j − P CEpo,i,j ∗XHM


i,j


i∈EN













M ABCOSpo,j − CP I ∗ T XEN Vpo,j



AEHpo,g = AEH0po,g
if
if
((OPTAEpo,j = 0
or TXENV0po,j ≤ 0)
and EMAXpo,j = 0)
or po = 1
(OPTAEpo,j = 0
or TXENV0po,j ≤ 0)
and EMAXpo,j > 0
and po > 1
OPTAEpo,j > 0
and TXENV0po,j > 0
and po > 1
(17)
(18)
20
3.2
Prices
P
j∈N EN
P Ai =
Pi =
P Zi,t =
P Ei ∗Ei +P Li ∗Li +P Ki ∗Ki
Xi
+ P IN V ∗ PROFCi
1 − P T Xi
P A i ∗ Xi −
P HMi,u =
3.3
P HMj,O ∗ AHMj,i +
P
t
TXZi,t ∗ V ∗ P Zi,t ∗ Zi,t
(20)
XDTi
XDi,u ∗ Pi +
Zi,t
ZDi,t
(19)
1
ZELDi,t
P
t
Mi,t,u ∗ TXMi,t,u ∗ V ∗ PWMi,t,u
HT Si,u
∗ PWZi,t
(21)
(22)
Volumes (Behavioural Equations)
Mi,t,u = MHi,t,u ∗
Zi,t = Z0i,t ∗
Pi
V ∗ TXMi,t,u ∗ PWMi,t,u
TXZi,t ∗ V ∗ P Zi,t
Pi
−ZELSi,t
∗
MELi,t,u
∗ XDi,u
(23)
ZXELi,t
(24)
XDTi
XDT0i
CGi = CGSi ∗ CGOV
Bi =
X
j
(25)
BHMi,j ∗ SECIN Vj
(26)
− 1
ZBETAi
−ZBETAi
Xi = ADi ∗ XDTi−ZBETAi + AZi ∗ Zi,W
+ Zi,E
(27)
− 1
BETAi,u
−BETAi,u
−BETA
HT Si,u = Mi,E,u + AHi,u ∗ XDi,u
+ AMi,u ∗ Mi,W,u i,u
(28)
K j = Xj ∗
1−RELj
RKj
∗
P Rj ∗ AKj
P Kj
RELj
21
(29)
LEj = Xj ∗
Lj = LEj ∗
Ej = LEj ∗
P Rj ∗ ALEj
P LEj
1−ENELj
RLj
1−ENELj
REj
XHMi,j = AFi,j ∗
RELj
∗
∗
(30)
P LEj ∗ ALj
P Lj
ENELj
(31)
ENELj
(32)
P LEj ∗ AEj
P Ej
P Ej
P HM Ui,j
EFUELj
∗ Ej
i ∈ EN
Cg,i = CFg,i + (CLg + HENVEXg ∗ P Q ∗ QEXP ) ∗ ACg,i ∗
TXCi ∗ P HMi,C if E3 = 0
where
phmuci,g =
P HM U Ci,g
if E3 = 1
3.4
(33)
CP ISg
phmuci,g
CEL
(34)
Values (Budgets or Definition of Savings)
SHOUg +
X
i
phmuci,g ∗ Cg,i = DISP INg − P IN V ∗ HINVESg + CP I ∗
(35)
∗ (HCONTRg ∗ GCONTR + HMORSUg ∗ GMORSU + HINTg ∗ (1 − HINTXRg ))
where
phmuci,g as previously.
SGOV +
=
X
j
+
+
+
X
j
P HMj,O ∗ CGj =
(36)
W ∗ WT0j ∗ W Gj ∗ Lj +
XX
j
g
W ∗ W0g,j ∗ Lj ∗ (HPITg + HSSCg ) +
j
g
(Cg,j + abhj,g ) ∗ P HMj,C ∗ (TXCj − 1) +
XX
X
j
AAROWj ∗ P HMj,C ∗ (TXCj − 1) +
22
+
"
XX X
j
+
X
j
+
X
j
+
X
j
u
t
"
#
(TXMj,t,u − 1) ∗ V ∗ PWMj,t,u ∗ Mj,t,u − (TXZj,t − 1) ∗ V ∗ P Zj,t ∗ Zj,t +
IF T X ∗
X
i∈EN
#
FTX0i,j ∗ P HMi,O ∗ XHMi,j +
P Aj ∗ IP T X ∗ PTX0j ∗ Xj +
[CP I ∗ OTRANSj + INCTXj + CP I ∗ CONTRAj ∗ Xj ] +
+ P IN V ∗ 1 −
+ CP I ∗
X
g
1
GHOUSU
X
∗
HINVESg +
g
HINTXRg ∗ HINTg −
− CP I ∗ (GINTE + GCONTR + GMORSU + GOTRAN) − V ∗ TURIST +
X
+
txrenvpo − LU M T OT
po
where
abhj,g =
where
txrenvpo
ABHj,g if
E3 = 1
0
otherwise
T XREN Vpo if
E3 = 1
=
0
otherwise
SROW + V ∗ BT R +
X
j
AAROWj ∗ P HMj,C ∗ TXCj =
(37)
= V ∗ (INTROW + TURIST)
−CREDITj +
X
i
[P HMi,I ∗ BHMi,j ∗ SECIN Vj + P HMi,O ∗ DKi ∗ INVENTj ]
(38)
=P Kj ∗ Kj + P IN V ∗ Xj ∗ PROFCj − CP I ∗ Xj ∗ INTEREj −
−CP I ∗ OTRANSj − IN CT Xj − CP I ∗ CONTRAj ∗ Xj +
P
g
HINVESg
+P IN V ∗ INVTRAj ∗ SECIN Vj + P IN V ∗ HOUINVj ∗
+
GHOUSU
X
+HHINTj ∗
CP I ∗ Xi ∗ INTEREi + CP I ∗ GINTE −
−CP I ∗
X
g
i
HINTg − V ∗ INTROW
23
3.5
Behavioural Equations for Supplementary Variables
P T Xj = IP T X ∗ PTX0j − LU M T OT ∗
SSLUMPj
P A j ∗ Xj
(39)
W Gj = IW G ∗ WG0j
(40)
P Lj = W ∗ WT0j ∗ (1 + W Gj )
(41)
P Kj = P IN V Sj ∗ (AMRj + RSj )
(42)
IN CT Xj = P IN V ∗ INCTX0j + PRT ∗ (P Kj ∗ Kj + SPR ∗ P Lj ∗ Lj +
(43)
+ P IN V ∗ Xj ∗ PROFCj − CP I ∗ Xj ∗ INTEREj −
− P IN V ∗ AMRj ∗ Kj − P IN V ∗ PROFT0j )
3.6
RKi = RK0i
(44)
RLi = RL0i
(45)
REi = RE0i
(46)
Definitions of Supplementary Variables
1
1−ENEL
ENELj
ENELj
j
P LEj = ALj
∗ (P Lj ∗ RLj )1−ENELj + AEj
∗ (P Ej ∗ REj )1−ENELj
(47)
 1
P

1−EFUELj 1−EFUELj


AFi,j ∗ P HM Ui,j

i∈EN
P Ej =
1

P

1−EFUELj 1−EFUELj


AFi,j ∗ P HMi,O
if
E3 = 1
(48)
if
E3 = 0
i∈EN
1
REL
REL
1−RELj 1−RELj
P Rj = AKj j ∗ (RKj ∗ P Kj )1−RELj + ALEj j ∗ P LEj
P IN V Sj =
X
i
P HMi,I ∗ BHMi,j
(49)
(50)
24
X
1
∗
P HMi,I ∗ Bi
GIN V
P IN V =
(51)
i
DISP INg = (1 − HPITg − HSSCg ) ∗ W ∗
X
j
W0g,j ∗ Lj +
(52)
+ CP I ∗ HOTRANg ∗ GOTRAN −
X
INVTRAj ∗ SECIN Vj +
− P IN V ∗ HINFRAg ∗
j
+ SHLUMPg ∗ LU M T OT
 1
1−CEL

P

1−CEL

ACg,i ∗ TXCi ∗ P HMi,C

i
CP ISg =
1

1−CEL
P

1−CEL


ACg,i ∗ TXCi ∗ P HM U Ci,g
if
E3 = 0
(53)
if
E3 = 1
i
P
j
CP I =
"
TXCj ∗ P HMj,C ∗
P
j
GIN V =
X
"
PC0j ∗
P
P
Cg,j
g
Cg,j
g
#
#
(54)
SECIN Vj
(55)
j
BT R =
XX
j
LU =
t
"
P Zj,t ∗ Zj,t −
X
u
PWMj,t,u ∗ Mj,t,u
#
(56)
1 X
∗
Lj
TL
(57)
1 X
∗
Kj
TK
(58)
X
(59)
j
KU =
j
CLT OT =
CLg
g
25
LEIS = LEIS0 + W0AV ∗ TL ∗ (1 − LU )
W RIN D =
PP
g
j
(60)
W ∗ W0g,j ∗ Lj ∗ (1 − HPITg − HSSCg )
P
W0AV ∗ Lj
(61)
j
P CIN D =
P
g
CP ISg ∗ CLg
P
CLg
(62)
g
P Q = P CIN D
(63)
1
1−ELQ
P LQ = SHL ∗ W RIN D1−ELQ + SHQ ∗ P Q1−ELQ
(64)
1
1−ELCW
P U T IL = SHC ∗ P CIN D1−ELCW + SHW ∗ P LQ1−ELCW
(65)
CLOP T = U T OP T ∗ SHC ∗
ELCW
(66)
ELCW
(67)
ELQ
(68)
P U T IL
P CIN D
LQOP T = U T OP T ∗ SHW ∗
P U T IL
P LQ
LEIOP T = LQOP T ∗ SHL ∗
P LQ
W RIN D
QOP T = LQOP T ∗ SHQ ∗
P LQ
PQ
ELQ
QEXP = Q − QEN DOW
QEN DOW = QENDOB −
−
(69)
(70)
X
po
DAMAGIpo ∗ (T EIpo − TEI0po ) −
po
DAMAGHpo ∗ (T EHpo − TEH0po )
X
26
(71)
U T OP T =
SP EN D
P U T IL
(72)
SP OP T = U T IL ∗ P U T IL
(73)
SP EN D = P CIN D ∗ CLT OT + W RIN D ∗ LEIS + P Q ∗ Q
(74)
1/(1− 1 )
1
1
1
1
ELCW
U T IL = SHC ELCW ∗ CLT OT 1− ELCW + SHW ELCW ∗ LQ1− ELCW
(75)
11
1
1
1
1
LQ = SHL ELQ ∗ LEIS 1− ELQ + SHQ ELQ ∗ Q1− ELQ 1− ELQ
(76)
HT Si,u
 P
Cg,i + AAROWi
if



 g
Bi
if
=
EFUELj

P
P
E

j

AFi,j ∗ phmui,j
∗ Ej + CGi + DKi if

j
where
HT Si,u
phmui,j =
P HM Ui,j
P HMi,O
if
if
E3 = 1
E3 = 0
 P
[Cg,i + abhi,g ] + AAROWi
if


 g
Bi
if
=
P


AHMi,j ∗ Xj ∗ abii,j + CGi + DKi if

j
where
where
XDTi =
X
abhi,g =
abii,j =
u=C
u=I
(77)
u=O
i ∈ EN
u=C
u=I
u=O
(78)
ABHi,g if
E3 = 1
0
otherwise
ABIi,j
0
if
E3 = 1
otherwise
XDi,u
i ∈ N EN
(79)
u
SSEC = −
X
CREDITj
(80)
j
SECIN Vj = CRESC ∗ SECINVEj
(81)
27
3.7
Closure
IW G = SIW G
(82a)
IP T X = SIP T
(83a)
IF T X = SIF T
(84a)
LU M T OT = LUMTOT0
(85a)
BT R = TRB
(86a)
V = SV ∗ CP I
(86b)
CGOV = TG
(87a)
SGOV = SGOV0 ∗ CP I
(87b)
RSj = R ∗ RS0j
(88a)
Kj = K0j
(88b)
R = SR
P
(89a)
R=
j
RSj ∗ Kj
P
Kj
(89b)
j
KU = SKU
(89c)
1
1
ELQ
U T IL ELCW
LQ
W RIN D = LAM BDA ∗ SHW ∗
∗ SHL ∗
LQ
LEIS
ELS
W RIN D
LU = SLU ∗
CP I
W = SW ∗ CP I
28
(90a)
(90b)
(90c)
1
1
U T IL ELCW
LQ ELQ
P Q = LAM BDA ∗ SHW ∗
∗ SHQ ∗
LQ
Q
Q = SQ ∗ Q0
(91a)
(91b)
1
U T IL ELCW
P CIN D = LAM BDA ∗ SHC ∗
CLT OT
CON SC = SCL
(92b)
LAM BDA = 0
(93a)
U T IL = SU ∗ U0
(93b)
GIN V = TI
(93c)
CLg = CON SC ∗ TCg
(94a)
(92a)
SHOUg = CON SC ∗ HSAVRg ∗ DISP IN +
(94b)
CP I = SP C
(95a)
+ CP I ∗ (HMORSUg ∗ GMORSU + HINTg ∗ (1 − HINTXRg ))
29
4
4.1
The Model’s Parameters and Variables
Variables
ABHi,g Additional input required for the reduction of abatable emission, i ∈ I, g ∈ G
ABIi,j Additional input required for the reduction of abatable emission, i, j ∈ I
AEHpo,g Ratio of abated to potential emission in household g, po ∈ PO, g ∈ G
AEIpo,j Ratio of abated to potential emission in sector j, po ∈ PO, j ∈ I
Bi Investment of i-th sector origin, i ∈ I
BT R Balance of trade.
Cg,i Total personal consumptions of commodity i, g ∈ G, i ∈ I
CABpo,j Scaling factor of the unit abatement cost sectors, po ∈ PO, j ∈ I
CABHpo,g Scaling factor of the unit abatement cost households, po ∈ PO, g ∈ G
CGi Government consumption, i ∈ I
CGOV Level of social consumption.
CLg Level (utility) of variable consumption, g ∈ G
CLOP T Optimal variable consumption.
CLT OT Total variable consumption.
CON SC Scaler for personal consumption or household savings.
CP I Consumer price index.
CP ISg Shadow price index of variable consumption, g ∈ G
CREDITi Sectoral net borrowings (= - saving), i ∈ I
CRESC Investment scaler.
DISP INg Households net disposable income, g ∈ G
Ei Index of energy use, i ∈ I
GEN V T Xpo General uniform emission tax rate, po ∈ PO
GIN V Level of gross capital investment.
HT Si,u Domestic use, i ∈ I, u ∈ U
IF T X General index of fuel tax rates.
30
IN CT Xi Income tax by sectors, i ∈ I
IP T X Index of production tax.
IW G Index of wage surtax.
Ki Capital stock in sector i, i ∈ I
KU Capital utilization level.
Li Labor used in sector i, i ∈ I
LAM BDA Shadow price for the household utility.
LEi Composite utility of labor and energy, i ∈ I
LEIOP T Optimal level of leisure.
LEIS Total free time of households (measured by potential employment).
LQ Leisure-Environment composite.
LQOP T Optimal leisure-environment composite.
LU Labor utilization level.
LU M T OT Lump sum recycling of environmental tax revenue.
Mi,t,u Import of commodity i, i ∈ I, t ∈ T , u ∈ U.
M ABCOSpo,j Marginal abatement cost, po ∈ PO, j ∈ I
Pi Domestic producer price, i ∈ I
P Ai Average sales price, i ∈ I
P ABpo,j Abatement price deflators sectors, po ∈ PO, j ∈ I
P ABHpo,g Abatement price deflators households, po ∈ PO, g ∈ G
P CIN D Average consumer price index.
P Ej Average energy price by sectors, j ∈ I
P HMi,u Composite good price, i ∈ I, u ∈ U
P HM Ui,j User’s cost of energy inputs (including abatement costs) sectors, i, j ∈ I
P HM U Ci,g Consumers’ cost of commodities (energy includes abatement costs), i ∈ I,
g∈G
P IN V Investment price index.
31
P IN V Sj Investment price index by sectors, j ∈ I
P Ki Calculative capital costs, i ∈ I
P Li Calculative labour costs, i ∈ I
P LEi Shadow price of energy-labor composite, i ∈ I
P LQ Shadow price of the leisure-environment composite.
P Q Price of environmental goods.
P Ri Composite cost of labour and capital per unit output in sector i, i ∈ I
P T X Rate of price modifying taxes and subsidies.
P U T IL Shadow price of household utility (optimal case only).
P Zi,t Export price in dollar (converted by base exchange rate), i ∈ I, t ∈ T
Q Environmental quality index.
QEN DOW Endowment of households of environment.
QEXP Environmental related household expenditure.
QOP T Optimal demand for environment.
R Net rate of return on capital.
REi Efficiency (scaling) parameter of energy in prod.f., i ∈ I
RKi Efficiency (scaling) parameter of capital in prod.f., i ∈ I
RLi Efficiency (scaling) parameter of labour in prod. f., i ∈ I
RSi Rate of return to capital by sector, i ∈ I
SECIN Vi Fixed capital investment by sectors, i ∈ I
SGOV Government saving.
SHOUg Households saving, g ∈ G
SP EN D Household spending including leisure.
SP OP T Optimal spending.
SROW Savings of the Rest Of World (= -foreign balance of payments)
SSEC Total of sectoral savings.
T EHpo Total household emission of pollutant po, po ∈ PO
32
T EIpo Total industrial emmission of pollutant po, po ∈ PO
T XEN Vpo,j Tax rate on pollutant po in sector j, po ∈ PO, j ∈ I
T XEN V Hpo Tax rate on pollutant po for households, po ∈ PO
T XREN Vpo Tax on pollutant po, po ∈ PO
U T IL Household utility.
U T OP T Optimal utility of households.
V Exchange rate index.
W Wage index.
W Gj Surtax on wages (SSC and UIC paid by employers), j ∈ I
W RIN D Average wage rate index.
Xi Gross output of sector i, i ∈ I
XDi,u Domestic sales from production, i ∈ I, u ∈ U
XDTi Total domestic supply, i ∈ I
XHMi,j Energy demand of sector j, i, j ∈ I
Zi,t Export of sector i, i ∈ I, t ∈ T
4.2
Parameters
AAROWj Domestic consumption of tourists by commodity, j ∈ I
ABCpo,i,j Abatement input coefficients in production, po ∈ PO, i, j ∈ I
ABHCpo,i Abatement input coefficients in consumption, po ∈ PO, i ∈ I
ACg,i Share parameter in the variable consumption, g ∈ G, i ∈ I
ADi Domestic share parameter in the export supply function, i ∈ I
AEi Share parameter of the energy input, i ∈ I
AEH0po,g Initial household abatement ratios, po ∈ PO, g ∈ G
AEI0po,j Initial industrial abatement ratios, po ∈ PO, j ∈ I
AFi,j Share parameters of the interfuel substitution function, i, j ∈ I
AHi,u Domestic share parameter in the import demand function, i ∈ I, u ∈ U
AHMi,j Input- output coefficients, i, j ∈ I
33
AKi Capital share parameter in the production function, i ∈ I
ALi Labour share parameter in the production function, i ∈ I
ALEi Share parameter of the energy-labor composite, i ∈ I
AMi,u Foreign share parameter in the import demand function, i ∈ I, u ∈ U
AMRi Amortization rate, i ∈ I
AZi Foreign share parameter in the export supply function, i ∈ I
BCpo,j Parameters in the scaling factor of the unit abatement cost, po ∈ PO, j ∈ I
BCHpo,g Parameters in the scaling factor of the unit abatement cost, po ∈ PO, g ∈ G
BETAi,u Elasticity parameter in import CES functions, i ∈ I, u ∈ U
BHMi,j Investment matrix coefficients, i, j ∈ I
CEpo,i,j Potential emission per unit of use of energy i in sector j, po ∈ PO, i, j ∈ I
CEHpo,i Potential emission per unit of use of energy i in households, po ∈ PO, i ∈ I
CEL Substitution elasticity of variable consumption.
CFg,i Fixed part of personal consumption, g ∈ G, i ∈ I
CGSi Commodity structure of govermental consumption, i ∈ I
CONTRAj Consumption transfer coefficients (per unit of output value), j ∈ I
DAMAGHpo Marginal damage (impact on Q) of household pollution, po ∈ PO
DAMAGIpo Marginal damage (impact on Q) of industrial pollution, po ∈ PO
DKi Inventory change of the i-th product, i ∈ I
EFUELj Elasticity of interfuel substitution, j ∈ I
ELCW Leisure-environment composite versus consumption substitution elasticity.
ELQ Elasticity of substitution between environment and leisure.
ELS Elasticity of labor supply.
EMAXpo,j Maximum allowed emission by pollutants and sectors, po ∈ PO, j ∈ I
EMBASEpo Base year industrial emission by pollutant, po ∈ PO
EMREDUpo Emission reduction rates by pollutants, po ∈ PO
ENELi Substitution elasticity between energy and labor, i ∈ I
34
FTX0j,i Fuel tax rates by using sectors (initial), i, j ∈ I
GCpo,j Parameters in the scaling factor of the unit abatement cost, po ∈ PO, j ∈ I
GCHpo,g Parameters in the scaling factor of the unit abatement cost, po ∈ PO, g ∈ G
GCONTR Government transfer of in-kind consumption (incl. private deprec.).
GHOUSU 1+Tax rate on private housing investment.
GINTE Government net interest exp.
GMORSU Government subsidy of private mortgages (write offs repayment subs.).
GOTRAN Government tranferred other transfers (foreign inter h.h. net tax etc.).
HCONTRg Share of households of in-kind consumption transfers, g ∈ G
HENVEXg Share of households of total environment related expenditures, g ∈ G
HHINTi Share of sectors of interest payment to households, i ∈ I
HINFRAg Share of households of total investment subsidies to sectors, g ∈ G
HINTg Households net interest income, g ∈ G
HINTXRg Interest tax rate, g ∈ G
HINVESg Housing investment, g ∈ G
HMORSUg Share of households of total government mortgage subsidies, g ∈ G
HOTRANg Share of households of other transfers (mainly cash benefits), g ∈ G
HOUINVj Share of sectors in households housing investment transfers, j ∈ I
HPITg Households Personal Income Tax rate, g ∈ G
HSAVRg Households current monetary savings rate, g ∈ G
HSSCg Households social security contribution rate, g ∈ G
INCTX0i Base real profit tax, i ∈ I
INTEREi Interest expenditure per output value, i ∈ I
INTROW Net interest income of Rest Of the World.
INVENTi Share of sectors from total inventory accumulation, i ∈ I
INVTRAj Share of investment transfers in investment expenditures, j ∈ I
K0i Capital endowment by sector, i ∈ I
35
KCpo,j Parameters in the scaling factor of the unit abatement cost, po ∈ PO, j ∈ I
KCHpo,g Parameters in the scaling factor of the unit abatement cost, po ∈ PO, g ∈ G
LEIS0 Base leisure.
LUMTOT0 Lump-sum environmental tax recycling.
MELi,t,u Import demand elasticities, i ∈ I, t ∈ T , u ∈ U
MHi,t,u Ratio of western import to domestic supply, i ∈ I, t ∈ T , u ∈ U
OPTAEpo,j Control parameter for optimal abatement, po ∈ PO, j ∈ I
OTRANSi Other transfers (net at constant prices), i ∈ I
PC0i Consumer prices in the base year, i ∈ I
PROFCi Profit mark-up coefficients, i ∈ I
PROFT0i Profit in the base year, i ∈ I
PRT Marginal general profit tax rate.
PTX0i Product and production taxes (net) per unit of output value.
PWMi,t,u World market prices of imports, i ∈ I, t ∈ T , u ∈ U
PWZi,t World market prices of exports, i ∈ I, t ∈ T
Q0 Base environmental utility.
QENDOB Base environment endowment of households.
RE0j Energy intensity by sectors, j ∈ I
RELi Elasticity of substitution between capital and labor, i ∈ I
RK0i Efficiency (scaling) parameter of capital in prod.f., i ∈ I
RL0i Efficiency (scaling) parameter of labour in prod. f., i ∈ I
RS0i Initial (base) rate of return to capital by sector, i ∈ I
Sl Scaling parameters or prescribed indexes, l ∈ L
SECINVEj Initial level of investments by investors, j ∈ I
SGOV0 Target value for government saving.
SHC Share of consumption of base utility.
SHL Share of leisure of base leisure-environment composite.
36
SHLUMPg Share of housholds of recycled environmental tax, g ∈ G
SHQ Share of environment of base leisure-environment composite.
SHW Share of leisure-environment composite of base utility.
SPR Share of profit of labor cost.
SSLUMPj Share of sectors of recycled environmental tax, j ∈ I
TCg Exogenously set level of variable consumption, g ∈ G
TEH0po Base total household emission by pollutants, po ∈ PO
TEI0po Base total industrial emission by pollutants, po ∈ PO
TG Level of governmental consumption (if fixed).
TI Level of gross investment (if fixed).
TK Total capital supply target.
TL Total labor supply target.
TRB Trade balance target.
TURIST Transfer income of the Rest Of the World (net).
TXCi Net domestic taxes on personal consumption (ad valorem), i ∈ I
TXENV0po,j Tax rate on pollution in production, initial value, po ∈ PO, j ∈ I
TXENVH0po Tax rate on pollution in consumption, po ∈ PO
TXMi,t,u Import duties (ad valorem ), i ∈ I, t ∈ T , u ∈ U
TXZi,t Export subsidy rates (ad val.), i ∈ I, t ∈ T
U0 Base utility.
W0g,j Initial ratio of wage of the households to sectoral employments, g ∈ G, j ∈ I
W0AV Average net wage rate.
WG0i Net rate of return on wages (soc. sec. contribution), i ∈ I
WT0i Initial sectoral wage rate, i ∈ I
XDT0i Domestic sales of production in the base, i ∈ I
Z0i,t Base value and shift parameter of the western export supply function, i ∈ I,
t∈T
37
ZBETAi Elasticity parameter in the western export supply function, i ∈ I
ZDi,t Shift parameter of the western export demand function, i ∈ I, t ∈ T
ZELDi,t Western export demand price elasticity, i ∈ I, t ∈ T
ZELSi,t Price elasticity in western export supply function, i ∈ I, t ∈ T
ZXELi,t Domestic sale elasticity in western export supply function, i ∈ I, t ∈ T
4.3
Index Sets
Housholds: G
Users: U ≡ {C, I, O}
Sectors: I
Energy sectors: EN ⊂ I
Non-energy sectors: N EN ≡ I\EN
Pollutants: PO ≡ {CO2 , N Ox , suspended particulates, SO2 }
Trade areas: T ≡ {W, E} (W : western trade, E: eastern trade)
Index set of miscallenious parameters: L
38