# GTAP-E: An Energy-Environmental Version of the GTAP Model Jean-Marc Burniaux* and Truong P. Truong ** 

## GTAP Technical Paper No. 16

Revised January 2002

Jean-Marc Burniaux was on leave from the OECD as vi siting Associate Professor with the Center for Global Trade Analysis, Purdue University, West Lafa yette, Indiana when this paper was written. Truong P. Truong was visiting Associate Professor with the Ce nter for Global Trade Analysis, Purdue University, West Lafayette, Indiana, on leave from the School o f Economics, University of New South Wales, Sydney NSW 2052, Australia. This technical paper is a revi sed version of an earlier GTAP Technical Paper writ ten by T.P. Truong (Truong, 1999). GTAP stands for the Global Trade Analysis Project w hich is administered by the Center for Global Trade Analysis, Purdue University, West Lafayette, Indian a, USA 47907-1145. For more information about GTAP, please refer to our Worldwide Web site at: ht tp://www.agecon.purdue.edu/gtap/, or send a request to conner@agecon.purdue.edu

# GTAP-E: An Energy-Environmental Version of the GTAP Model

Jean-Marc Burniaux and Truong P. Truong ∗ 

## GTAP Technical Paper No. 16



# Abstract
Energy is an important commodity in many economic activi ties. Its usage affects the environment via CO 2 

### emissions and the Greenhouse Effect. Modeling the energy-economy-environment-trade linkages is an important objective in applied econo mic policy analysis. Previously, however, the modeling of these linkages in GTAP has been incomple te. This is because energy substitution, a key factor in this chain of linkages, is absent fro m the standard model specification. This technical paper remedies this deficiency by incorpo rating energy substitution into the standard GTAP model. It begins by first reviewing some of th e existing approaches to this problem in contemporary CGE models. It then suggests an approa ch for GTAP which incorporates some of these desirable features of energy substitution. The approach is implemented as an extended version of the GTAP model called GTAP-E. In addition , GTAP-E incorporates carbon emissions from the combustion of fossil fuels and this revise d version of GTAP-E provides for a mechanism to trade these emissions internationally. The policy relevance of GTAP-E in the context of the existing debate about climate change is illustrated by some simulations of the implementation of the Kyoto Protocol. It is hoped t hat the proposed model will be used by individuals in the GTAP network who may not be themselve s energy modelers, but who require a better representation of the energy-economy linkage s than is currently offered in the standard GTAP model.

∗ The authors are indebted to Tom Hertel for original ly suggesting the topic of this study, and for his continued support and encouragement. Thanks are due also to Kevin Hanslow, and Mustafa Babiker for providing many helpful comments and suggestions on an earlier draft. Ken Pearson was quite indispensab le in providing the technical advice for the illustrative experiments. Finally, note that th is paper is a revised version of the GTAP Technical Paper No. 16 by Truon g (see Truong, 1999).ii 

# Table of Contents

1. Introduction ......................................

................................................... ..................................................... 1
2. Review of existing approaches.......................

................................................... ....................................... 1 2.1 The CETM model - Rutherford et al. (1997)...................................................................................... 2 2.1.1 The structure of CETM .............................

................................................... ............................. 2 2.1.2 The Linkage of ETA to MACRO .........................

................................................... .................. 4 2.1.3 Comments on the structure of CETM..................

................................................... ................... 7 2.2 The MEGABARE Model and the “Technology Bundle' Approach.

................................................... 9 2.2.1 Description of the technology bundle approach.....

................................................... ................ 9 2.2.2 Comments on the technology bundle approach.........

................................................... ........... 11
2.3 The OECD’S GREEN model ........................................................................................................... 12 2.3.1 Dynamics in GREEN...............................

................................................... ............................. 13 2.3.2 Inter-fuel substitution .........................

................................................... .................................. 14 2.3.3 Fuel-factor substitution.......................

................................................... .................................. 18 2.3.4 Comments on the GREEN model............................................................................................ 19 2.4 The Babiker-Maskus-Rutherford (BMR) model..........

................................................... .................. 20 2.5 Borges and Goulder (1984) model.....................

................................................... ......................... 207
3. Towards a GTAP model with energy substitution .........

................................................... ................... 259 3.1 Top-down versus bottom-up approach .................

................................................... ........................ 29 3.2 The issue of energy-capital substitutability or complementarity ................................... ...................................30 3.2.1 Importance of the issue ....................

................................................... ................................................... ..30 3.2.2 Empirical estimates of ) EK .................................................. ................................................... .................31 3.3 The structure of inter-fuel and fuel-factor sub stitution in GTAP-E ................................ ..................................32 3.3.1 Production structure with energy substitutio n ................................................. ........................................32 3.3.2 

### Consumption structure

.................................................. ................................................... ...................38



### 4. Illustrative scenarios...........................

................................................... ................................................. 40 4.1 Alternative implementations of the Kyoto Protocol....

................................................... .................. 41iii 

### 4.2 Macroeconomic results.................................................................................................................... 45

5. Conclusion.......................................

................................................... .................................................... 47 References .........................................

................................................... .................................................. 62

Annexes Annex 1 : General equilibrium elasticities in GTAP-E and GT AP. Annex 2 : Specifying country-specific carbon reductions w ith no emission trading in GTAP-E Annex 3 : Specifying emission trading in GTAP-E
Figures Figure 1: Structure of CETM .............................

................................................... ....................................... 3 Figure 2: MACRO production nest.........................

................................................... .................................. 4 Figure 3: MACRO consumption nest.......................

................................................... ................................. 4 Figure 4: ETA - MACRO linkage ............................

................................................... ................................. 8 Figure 5: Technology bundle approach....................

................................................... ................................. 9 Figure 6: Composition of the technology bundle for the electr icity industry...................................... ...... 10 Figure 7: Composition of the technology bundle for the steel i ndustry............................................ ......... 10 Figure 8: The structure of production in GREEN..............

................................................... ..................... 18 Figure 9: Energy and backstop technologies in GREEN ........

................................................... ................ 19 Figure 10: The structure of household demand in GREEN ....................................................................... 20 Figure 11: Substitution elasticity when total output is held constant. .......................................... ............. 22 Figure 12: Structure of production in the Babiker-Maskus-Ru therford (1997) model .............................. 23 Figure 13: Structure of final demand in the Babiker-Maskus-R utherford (1997) model .......................... 24 Figure 14: Structure of production in Borges and Goulder (1 984) model.......................................... ....... 25 Figure 15: Standard GTAP production structure ...........

................................................... ......................... 30 Figure 16: GTAP–E production structure .................

................................................... ............................ 304 Figure 17: GTAP–E capital-energy composite structure .....

................................................... ................... 34 Figure 18: GTAP-E government purchases ..................

................................................... .......................... 39 Figure 19: GTAP-E household private purchases...........

................................................... ........................ 40 Figure 20: Emission trading among Annex 1 countries ........

................................................... .................. 43 Figure 21: Worldwide emission trading..................

................................................... ................................ 43iv



### Figure 22: Welfare decomposition of implementing the Kyoto P rotocol with no use of the flexibility mechanisms ..............................

................................................... .............................................. 47 Figure 23: Welfare decomposition of implementing the Kyoto P rotocol with trading among Annex 1 countries....................................

................................................... ................................................ 47 Figure 24 : Welfare decomposition of implementing the Kyoto P rotocol with worldwide emission trading ....................................

................................................... .................................................. 48



## Tables Table 17.1: List of Technologies in ETA..................

................................................... ................................ 6 Table 17.2: List of Important Equations in ETA ............

................................................... .......................... 8 Table 3 Summary Characteristics of CETM ............................................................................................. 11 Table 4: Summary Characteristics of MEGABARE............

................................................... ................... 15 Table 5: Summary Characteristics of GREEN...............

................................................... ......................... 23 Table 6: Summary Characteristics of BMR Model...........

................................................... ...................... 27 Table 7: Summary Characteristics of Borges and Goulder Mode l .................................................. .......... 29 Table 8: Estimates of the partial Hicks-Allen elasticities of substitution ( ) ) and factor shares ( ) ).......... 32 Table 9: Energy substitution elasticities in GTAP-E and ot her models.......................................... ........... 35 Table 10: Elasticities of substitution between different f actors of production................................. ......... 36 Table 11: The relationship between inner ( ) KE-inner 

### ) and outer ( ) KE-outer 

### ) elasticities of substitution for the cases of Japan and the US............................................................................................................... 37 Table 12: Elasticities of substitution between domestic an d foreign sources ( ) D 

### ).................................. 347 Table 13: Elasticities of substitution between different r egions ( ) M 

### )........................................................ 36 Table 14: Marginal costs of achieving the Kyoto targets with and without using the flexibility mechanisms. ........................................

................................................... .................................................... 44 Table 15: Macroeconomic impacts of implementing the Kyoto Protocol .......................................... ...... 461



# GTAP-E: Incorporating Energy Substitution into GTAP Model

1. Introduction
Energy is an important commodity in many economic activit ies. Its usage affects the environment via CO 2 

### emissions and the Greenhouse Effect. Modeling the energy-economy-environment-trade linkages is an important objective in applied econo mic policy analysis. Up to now, however, the modeling of these linkages in GTAP has been incomple te. This is because energy substitution, a key factor in this chain of linkages, is absent fro m the standard model specification. This paper remedies this deficiency by incorporating energy su bstitution into the standard GTAP model. It begins by first reviewing some of the existing appr oaches to this problem in contemporary CGE models. It then suggests an approach for GTAP which incorporates some of these desirable features of energy substitution. 

### The approach is implemented as an extended version of the GTAP model called GTAP-E. In addition, GTAP-E incorporates carbon emissions from the combustion of fossil fuels as well as a mechanism to trade these emissions intern ationally. The policy relevance of GTAP-E in the context of the existing debate about climate change is illustrated by some illustrative simulations of the implementation of t he Kyoto Protocol. This technical paper is a revised version of a earlier paper written by T.P. Truong (Truong, 1999). Compared with this version, the model used here is derived f rom the version 6.1 of the GTAP model based on 1997 data (version 5 of the GTAP data base ). In addition to inter-fuel substitution, this model incorporates some further impr ovements, such as the computation of a Social Account Matrice (SAM) which provides a f ull account of the carbon tax revenues and expenditures and a more specific treatment of c arbon emission trading. 

# 2. Review of Existing Approaches
In this section, we review some of the existing appr oaches to incorporating energy substitution into AGE models. The purpose of this section is not to undertake an exhaustive review of the literature, but rather, to select some typical appr oaches and examine their important features for possible incorporation into the GTAP model. There ar e three main models to be considered in this section, and these are: (1) the CETM model by Rutherford et al . (1997), (2) the MEGABARE model by ABARE (1996), and (3) the OECD’s GREEN model by Burniaux et al. (1992). Some other models are also considered in sub-sectio n 2.4.2



## 2.1 The CE T

M Model - Rutherford et al. (1997) This model represents an attempt to bridge the gap be tween the (top down) economic models often used by economists, and the (bottom-up) proce ss models used by engineers and environmentalists in studying the effect of energy policies on the environment. Recognizing that full integration of these two types of models is me thodologically and computationally difficult, the authors of CETM attempted a ‘partial’ link. This means, firstly, the construction of a partial equilibrium ‘process model’ of the energy sector (E TA) (which is based on the MERGE model of Manne and Richels (1996)). The model is then link ed to a general equilibrium model called MACRO. The process of linking the two sub-models is through the process of passing the energy price and quantity variables between the two sub-mo dels and iteration until the ‘input reference quantities’ from ETA are close to the solutions of the MACRO model (Rutherford et al (1997, p6)). In light of the fact that the energy sector ma kes up only a small fraction (less than 5%) of the gross output of most economies, ‘convergence’ of the two sets of results from ETA and MACRO is considered most likely. This is because if en ergy is only a small part of the industry cost structure then the changes in the prices and q uantities of energy demand within ETA will affect only marginally the overall results of indus try costs and prices within MACRO. This means convergence of the two sets of results from ET A and MACRO can be achieved through an iteration process as described above, rather than b y having to solve the optimization problems of the two sub-models simultaneously. 

### 2.1.1 The Structure of CETM The structure of CETM is described in Figure 1. Withi n this structure, the MACRO sub-model is a conventional computable general equilibrium (CGE) model, which has 5 internationally traded commodities and five industries: Y - Other manufac tures and services, NFM = Non-ferrous metals, PPP = Pulp and paper, TRN = Transport industries, OTH = Other energ y intensive sectors. The first industry is an aggregate of non- energy intensive industries, and the other four represent energy-intensive industries. Factors of p roduction include: land, labor, capital, electricity, and non-electric energy. The latter two energy i nputs are linked to ETA. There are nine regions in MACRO: USA, JAPAN, CANZ (Ca nada, Australia, New Zealand), OECDE (Other OECD), CHINA, INDIA, EFFSU (Ea stern Europe and Former Soviet Union), MOPEC (Mexico and OPEC countries), and ROW (The rest of the world). With eleven ten-year time periods, this model begins the period of simulation from 1990 (benchmark year) and ends in 2100. The structure of industry production in MACRO is as described in Figure 2. First, capital and labor are combined via a Cobb-Douglas productio n function 1 

### . So are electric and non-electric energy inputs. The composite of non-energy material inputs, however, is combined using Leontief technology. The overall aggregation of compo site primary factors, energy inputs, and non-energy materials is CES with an elasticity of substitut ion of 0.5.

1 Figure 3 in Rutherford et al (1997, p. 15) did not show land but the text (p. 9 ) mentioned land as one of the factors of production.3Figure 1 Structure of CETMSource: Rutherfordet al.(1997), Figure 1, p. 7.ENERGY CONVERSIONTECHNOLOGIESETAMACROSECTORSENERGY RESERVES ANDNATURAL RESOURCESLABOR, LANDELECTRIC, NON-ELECTRICENERGY (E, N)ENERGY COSTS (EC)INTERMEDIATE AND FINALDEMAND (D)INVESTMENT (I)CAPITAL (K)1



### F igure 2 MACRO Production Nest

Source: Rutherford et al. (1997), Figure 3, p. 15.



### Figure 3 MACRO Consumption Nest

Source: Rutherford et al. (1997), Figure 2, p. 14.


### Consumption in MACRO is described as CES-nested agg regate of energy and nonenergy composite goods. Composite energy is a Cobb- Douglas aggregate of electric and nonelectric inputs, while composite non-energy is a Co bb-Douglas aggregate of the five industrial goods. Consumers substitute composite energy and no n-energy inputs with an elasticity of substitution of 

### ) end 

### = 0.5, which is chosen to approximate the own-pric e elasticity of demand for energy. 

### C rt

ELEC NONELEC Y NFM PPP TRN OTH σ =1 σ =1 σ =0.5 

### Y irt

ELEC NONELEC K L

σ =1 σ =0 σ =0.5

σ =1

Y NFM PPP TRN OTH2 

### MACRO is linked to ETA, a partial equilibrium sub-mo del which describes in greater details the energy sub-sector. ETA specifies the su pply functions of electric and non-electric energy. Electric energy is produced by a combination of hydro-electricity, natural gas, oil, coal, and two 'backstop' technologies: advanced high cost, and advanced low cost. Non-electric energy can be produced either from oil, gas, coal, or by n on-conventional technologies (such as carbonfree backstop, renewables, synthetic fuels). The lis t of electric and non-electric technologies in ETA are given in Table 1. 

### ETA includes the following internationally traded goods (g) : 1 OIL Crude oil 2 COAL Coal 3 GAS Natural gas 4 CRT Carbon emission rights ETA is formulated as a non-linear mathematical prog ram. The decision variables in ETA include the following: SURPLUS The non-linear programming maxim and defined as the sum of consumer and producer

surplus EC r,t Energy cost (in region r and time period t ) - trillion dollars EN r,t Composite energy demand E r,t Electric energy (total) N r,t Non-electric energy (total) PE e,t,r Production of electric energy (by source e ) - tkwh PN n,t,r Production of non-electric energy (by source n ) - exaj GASNON

t,r Gas consumed to meet non-electric demands OILNON

t,r Oil consumed to meet non-electric demands RSC r,x,t Undiscovered resources (by type x ) RSV r,x,t Proven reserves RA r,x,t Reserve additions CLEV t,r Carbon emissions level – billion tons CRLX t,r Carbon limit relaxation – billion tons EXPRT

g,t,r Exports (of goods g ) IMPRT

g,t,r Imports To understand the internal workings of ETA, a list of some of the important equations in ETA is given in Table 2. ETA solves for the aggregate shares of electric and non-electric energy. The solution is arrived at by MACRO first passing on to ETA the following variabl es and their time paths: 

### e r,t Reference path of electric energy demand (TKW) 

### n r,t Reference path of non-electric energy demand (EJ) pvcen r,t Present value unit cost of energy sector inputs pvpe r,t Present value price of electric energy pvpn r,t Present value price of non-electric energy3



### Table 1 List of Technologies in ETA No. Short Name Long Name Restrictions Electricity supply technologies ( e ): 1 HYDRO Hydro electric 2 GAS-R Existing gas-fired 3 OIL-R Existing oil-fired 4 COAL-R Existing coal-fired 5 NUC-R Existing nuclear 6 GAS-N New vintage gas-fired DLE( e ) 7 COAL-N New vintage coal-fired DLE( e ) 8 ADV-HC Advanced high-cost DLE( e ), XLE( e ) 9 ADV-LC Advanced low-cost XLE( e ) Non-electricity energy supply technologies ( n ): 10 OIL-LC Low cost oil reserves X( n ) 11 OIL-HC High cost oil reserves X( n ) 12 GAS-LC Low cost gas reserves X( n ) 13 GAS-HC High cost gas reserves X( n ) 14 CLDU Coal for direct use DLN( n ) 15 NE-BAK Non-electric backstop DLN( n ), XLN( n ) 16 RNEW Renewables XLN( n ) 17 SYNF Synthetic fuels (coal shales) DLN( n ), XLN( n ) Note: X( n ) Fossil fuels DLE( e ) Electricity technologies subject to decline limi ts, DLN( n ) Non-electric technologies subject to decline limi ts XLE( e ) Electricity technologies subject to expansion li mits XLN( n ) Non-electric technologies subject to expansion li mits 

### ETA then uses the ‘reference time path’ of energy demand to calculate other variables and parameters such as the ‘reference present value of energy demand’ en r,t 

### (equation (1)), the distributive share parameter of electric energy evls t,r 

### (equation (2)) which is then used to calculate the composite energy demand (in volume ter ms) EN r,t 

### (equation (4)), and the total of consumers’ and producers’ surplus (equation (3)). N ote that the total surplus is normally calculated as the area between the consumers’ (regi onal) energy demand curve and the marginal cost curve. However, it can also be calculated as t he total area under each region’s energy demand curve, then subtracting the total cost of en ergy supply. The demand function is assumed to have a constant own-price elasticity of ) and the function is ‘calibrated to MACRO’ (i.e. using the ‘reference present value of energy demand’ en r,t 

### as calculated from MACRO - see equation (3)). The total cost to produce energy is a linear c ombination of the direct costs to produce electric and non-electric energy, with an allowance for oil-gas price differential of OGPD = $1.25/GJ for all regions, an allowance for interreg ional trade transportation costs of $2/GJ for gas, $1/GJ for coal, $0.33/GJ for oil, and $10/tonn e for carbon emission rights (see equation (21)). ETA then optimizes the mix of electric and non-elec tric technologies by maximizing the value of the total surplus subject to all the techn ological and institutional constraints (as described in equations (7-21) of Table 2). These con straints include things like: (a) market clearing conditions (supply of fuels and energy sou rces must at least meet the demand, total imports must equal total exports, etc.) (equations ( 7-9,20)), (b) ‘side constraints’ which control4



### the ‘availability’ of different technologies, throu gh ‘expansion limits’ on new technologies, ‘decline limits’ on old (and new) technologies, and ‘exhaustion limits’ on non-renewable resources, etc. (equations (10-17)). In addition, eq uation (18) determines the carbon emission level and equation (19) specifies the limits on car bon emission rights which are given exogenously for each region and time period. Equati on (22) defines the inverse demand function for composite energy in ETA, which is linked to the r eference level in MACRO as explained in the next section below. 

### 2.1.2 The Linkage of ETA to MACRO In MACRO, the demand for composite (electric and non -electric) energy is structured as a CES function. This means the demand level for composite energy EN j 

### in sector j is related to the sector output Q

j 

### , the sector unit cost C j 

### , and the composite energy price PEN ,j 

### by the relation:

σ  ̧  ̧ 1 ·  ̈  ̈ © §

= j

j j j PEN

C kQ EN 

### (i)

where k is some constant and ) is the own-price elasticity of demand for composite energy.
Let j EN 

### , j C 

### , and j

PEN 

### be the ‘reference level’ for these variables, i.e. the level as determined in the MACRO module. The linkage of ETA t o MACRO is then defined by the following equation:

σ
−  ̧  ̧ 1 ·  ̈  ̈ © §

= j

j j j j j C

C PEN PEN EN EN 

### (ii)
which follows from the previous relation, and

j j a

N j N j N j N a

E j E j E j E j P t P P t P PEN − −  ̧

 ̧  ̧ 1 ·  ̈  ̈  ̈ © § + +  ̧  ̧  ̧ 1 ·  ̈  ̈  ̈ © § + +

= 1 ) 1( ) 1( μ μ 

### (iii)

where:

N

j E j t t , 

### are ad-valorem tax rates on electric and non-electric ener gy demand in sector j

.
N j E j μ μ , 

### are distribution margins on electric and non-electric ene rgy (cost indices).

N j E j P P , 

### are the reference prices (user costs) of electric and non-el ectric energy. The last equation is based on the assumption that the structure of the electric and non-electric energy composition is Cobb-Douglas. If energy cost is only a small proportion of the overall secto r cost, i.e.:

1 ) ( << ∂ ∂

= ⋅ j j

j j j j j C PEN

C PEN C EN PEN



### ,5



### then equation (b) can be approximated by:

σ
−  ̧  ̧ 1 ·  ̈  ̈ © §

= t

j j j PEN

PEN EN EN 

### (iv)

or

σ 1

−  ̧  ̧ 1 ·  ̈  ̈ © §

= t

j j j EN

EN PEN PEN 

### (v)
Equation (v) can be used to represent the inverse d emand function for composite energy in ETA which will come out to be close to that modeled in M ACRO. This is added to the list of equations for ETA (shown as equation (22) in Table 2).


### Table 2 List of Important Equations in ETA

tr tr tr tr tr n

pvpn e pvpe en
, , , , , . ⋅

+ ⋅ = 

### (1)

t r t r t r t r 

### en

e pvpe elvs

, ,

, , 

### ⋅ = (2)

tr tr tr tr
tr tr EC pvcen en EN en SURPLUS

, , 1

, ,

, , 1 ⋅ −  ̧  ̧ 1 ·  ̈  ̈ © §  ̧ 1 ·  ̈ © §

− ⋅ = − 

### ¦ σ σ

σ σ 

### (3)

tr elvs tr elvs tr EN N E t r t r

, 1

, , , , = ⋅ − 

### (4) ¦ = e tr

e tr PE E

,, , 

### (5)

r t bak ne r t rnew r t synf r t cldu r t r t tr PN

PN PN PN

GASNON OILNON N

,, ,, ,, ,,

, , ,

− + + + +

+ = 

### (6)

r t n gas r t n gas r tr gas r tr gas r t gas r t gas r t hc gas r t lc gas r t PE ch PE ch EXPRT IMPRT PN PN GASNON

,, ,, ,, ,, ,, ,, ,, ,, ,

− − − − − −

⋅

− ⋅ − − + + = 

### (7)

tr r t N GASNON

,

, 5.0
⋅ ≤ 

### (7b)

r tr oil r htrt r oil r t oil r t oil r t hc oil r t lc oil r t PE ch EXPRT IMPRT PN PN OILNON

,, , , ,, ,, ,, ,, ,

−

− − −

⋅ − − + + = 

### (8)6



### Table 2 List of Important Equations in ETA

r t synf r t cldu r t n coal r htrt n coal r tr coal r htrt r coal r t coal r t coal r t coal PE syntpe PN PE ch PE ch IMPRT EXPRT PN

,, ,, ,, , , ,, , , ,, ,, ,, ) 1(
⋅ + + + ⋅ + ⋅ − − =

−

− − − 

### (9)

10

, , ,1 , r

r ty dle r ty dle decf

PE PE
⋅

≥ + 

### (10)

10

, , ,1 , r

r tp ln d r tp ln d decf

PE PE
⋅

≥ + 

### (11)

r t x tr n r r t ln x PN

N nshf nxpf PN ,1 ln, 1 , 10

,, + + ≥
⋅ + ⋅ 

### (12) 

# ( ) ( ) ¦¦ ≥
⋅ + ⋅ + + xle xle r t xle tr RG rg r tp xle PE E nshf f PE ,1 , 1 , 10

, , exp 

### (13)

1 , , , , , , 1 , , 5

5 + + ⋅ − ⋅ − = tx r tx r tx r tx r RA

RA RSC RSC 

### (14)

) ( 5 ) ( 5 ,1 , 1 , , ,, , , , , 1 , , r tx tx r r tx tx r tx r tx r PN RA PN RA RSV RSV + + + − ⋅ +

− ⋅ + = 

### (15)

tx r tx r r x 

### RA RSC rdf

, , , , , 

### ≥
⋅ (16)

r tx tx r r x 

### PN RSV prv
,,

, , , 

### ≥
⋅ (17)


# ( ) ( ) r oil r t oil r t oil r gas r t gas r t gas n r t n r n e r

te r e r t

cecn

IMPRT EXPRT cecn

IMPRT EXPRT PN cecn nt PE cece et CLEV

, ,, ,, ,

,, ,, ,, , ,, , , , ,

⋅ − − ⋅

− − ⋅

+ ⋅ = 

### ¦ ¦ 

### (18)

r t r t crt r t crt r t lim car IMPRT EXPRT CLEV

, ,, ,, , ≤

− = 

### (19) 

# ( ) 0

,, ,, = − 

### ¦

r r t q r t q IMPRT EXPRT 

### (20)

¦ ¦ ¦

⋅ + ⋅ + ⋅ + ⋅ =

n r t q q r t r n r

n r t n e r

e r te r t EXPRT

exp cstc GASNON ogpd ncst PN ecst PE EC )

( )

( ) (

,, , , ,, , ,, , 

### (21)

σ 1

−  ̧  ̧ 1 ·  ̈  ̈ © §

= t

j j j EN

EN PEN PEN 

### (22)7



### 2.1.3 Comments on the Structure of CETM 2.1.3.1 The Structure of Production and Inter-fuel and Fuel -factor Substitution. The structure of production in the MACRO module of t he CETM model groups labor and capital together, and these factors are separated from the energy branch (see Figure 2). This means that energy-capital and energy-labor will have the same substitution elasticity and this implies a severe restriction (see the discussion on the issue of capital - energy substitutability or complementarity in section 3.2 below). On the other hand, the internal structure of the int er-fuel substitution in the MACRO module makes a useful distinction between electric and non-electric energy inputs. Although econometric evidence is scarce with respect to the substitution between electric and non-electric energy inputs, this distinction is useful at least from a theoretical viewpoint. This is because the choice of the electricity generation technologies m ay have an important impact on the environment (such as the emission of CO 2 

### ), and hence the focus on electric energy consumpti on level may help focus attention on the choice of these technol ogies 2



### . Different forms of non-electric energy such as oil, gas, coal (direct use), synthetic fuels, renewable fuels or the non-electric backstop techno logies, are treated as perfect substitutes in the ETA module (see equation (6) in Table 2). This assum ption is perhaps rather restrictive especially from the end-user’s point of view. Natur al gas, for example, is known to command a premium over coal because of its ease of handling. I t may also come into conflict with other assumptions made in the model such as the fact that the market share for natural gas is limited (see equation (7)). Limited market share often impl ies some difficulty of substitution rather than limitation in supply. Finally, if these non-electric energy forms are perfectly substitutable, then their marginal costs (prices) must also be set equa l to each other. These are strong assumptions. 2.1.3.2 The ‘Small’ Influence of the Energy Sector in Linkin g ETA to MACRO Relying on the fact that the energy sector makes up less than 5% of the gross output of most economies, it is anticipated that any changes in the price s and quantities of energy demand within ETA will have only a small influence on the overall industry cost (and hence prices and demand within MACRO). This means that convergence of the r esults of ETA and MACRO can be achieved fairly rapidly. But this is likely to depen d also on the assumptions regarding supply and demand elasticities. If the supply elasticity is muc h greater than the absolute value of the demand elasticity then convergence can be assured. However, if the converse is true, then even if energy is only a small proportion of the overall industry costs, it can still act as a constraint on consumption activities, and can give rise to signif icant fluctuations in energy prices and demand, and therefore, will not help for convergence (see F igure 4). Since ETA is a process model rather than a conventional econometric model, the concept of ‘supp ly elasticity’ cannot be clearly

2 Furthermore, as Hogan (1989, p. 54) noted, the grou ping of all energy forms together in an aggregate e nergy demand function may mask the historically important trend of ‘electrification’ in an energy economy (such as that observed in the US economy during the period from 1960 to 1982)

.8



### Figure 4 ETA - MACRO Linkage

defined and tested. However, the general concept of supply responsiveness to price and demand changes may still be an important factor to consider when look ing at the issue of convergence. 2.1.3.3 ‘Dynamic Adjustment Constraint’ on Technol ogies could be Linked to Endogenous Factors within the MACRO economy



### . Equations (10-13) represent the ‘dynamic adjustment c onstraints’ on new and existing technologies. They define the limits to which existi ng technologies can be retired (because of sunk capital costs) or new technologies to be intro duced (because of the difficulty of market penetration). These constraints reflect economic as well as institutional factors within the current and future markets, and therefore, they could also be determined ‘endogenously’ within the model rather than being set exogenously. For exampl e, the rate of market penetration for new technologies may be dependent on the differences in production costs between existing and new technologies. The rate of retirement for existing te chnology can also be specified as a function of the expected increase in future demand and supply a nd the cost of capital. In other words, the dynamic adjustment constraints could be linked to th e investment decisions within the model, rather than being specified as exogenous. Since the absence of such a linkage is largely due to practical considerations, this is probably an area for furt her research. 

### Table 3 Summary Characteristics of CETM Model Characteristics CETM Top-down versus bottom-up Bottom-up in CETM, top-do wn in MACRO Dynamic Simultaneous Inter-fuel substitution Yes Fuel-factor Substitution Yes Capital – Energy complementarity/substitutability Energy and capital are substitutes in the MACRO pro duction structure, but can be complements within the energy sub-module CETM. ENERGY COSTS

ENERGY QUANTITY DEMAND (MACRO)
ENERGY QUANTITY

SUPPLY (ETA) DEMAND (MACRO) SUPPLY (ETA) ENERGY COSTS ELASTIC SUPPLY INELASTIC SUPPLY9



## 2.2 The MEGABARE Model and the “Technology Bundle' Approach 3 

### In building the MEGABARE model on top of the GTAP fr amework, the authors of that model made ‘a deliberate decision ...not to adopt the nes ted CES (constant elasticity of substitution) production function approach’ to energy substitution. This was because: It was believed that it was possible to improve on the nested CES approach in terms of both accuracy and transparency by introducing what has be en termed the 'technology bundle' approach. Using this approach, a level of detail ab out different technologies is introduced into MEGABARE that is normally found only in so-cal led 'bottom up' models. An attempt is made to introduce the realism in modelling substitut ion options that is a feature of 'bottom up' models while retaining extensive interactions b etween the energy and other sectors of the economy that is a feature of 'top down' models. (MEGABARE , 1996: 4). 

### 2.2.1 Description of the Technology Bundle Approach The ‘technology bundle’ approach is described below in figures 5-7. First, the intermediate inputs into production are divided into technology bundle inputs – typically primary factors and primary energy inputs - and non-technology bundle i nputs (Figure 5). The technologies for an industry (for example, coal-fired electricity, gas- fired electricity etc.) are Leontief (fixed inputoutput coefficient) combinations of technology bundl e inputs. The technology bundle for an industry is a conventional ‘smooth production funct ion’ (such as CRESH) combination of the output of each technology. Industry output is a Leo ntief combination of the technology bundle and the non-technology bundle inputs The technology bundle approach is used in the MEGABA RE model to describe the input use of the electricity generation industry (Figure 6) and the steel industry, which represent typical examples of energy intensive industries. The approach, however, can also be used to describe other energy intensive industries. With th e steel industry, the input structure differs slightly from the electricity industry: electricity and minerals are added to the input list, along with the primary factors and the primary energy inputs (Fig ure 7). 

### Figure 5 Technology Bundle Approach

Source: ABARE (1996), Figure 6, p. 22.

3 ABARE (1996), The MEGABARE model: interim documentation , February. 

### Gross output by industry

Leontief



### Technology bundle Commodity 1 Commodity

..........10



### Figure 6 Composition of the Technology Bundle for the Electri city Industry

Source: ABARE (1996), Figure 9, p. 32. 

### Figure 7 Composition of the Technology Bundle for the Steel In dustry

Source: ABARE (1996), Figure 10, p. 32. ‘EAF and ‘BOF’ stand for ‘electric arc furnace’ and ‘basic oxygen furnace’ respectively. 

### CRESH Technology bundle

Technology 1



### .................

Leontief Leonti 

### ef

K Primary energy e L Primary energy 1 Primary energy 1 

### ... K L Primary energy e 

### ... Technology

EAF BOF



### ...............

Leont 

### ief Leonti 

### ef

L Primary energy 1 Electricity K L Primary energy e 

### ... Minerals 

### CRES Technology

Electricity Minerals K11



### 2.2.2 Comments on the Technology Bundle Approach The technology bundle approach is interesting and in novative. It tries to introduce the concept of ‘substitution’ between alternative ‘technologies’ t o give a more realistic description of the nature and range of substitution occurring within the ener gy producing and energy-using industries, in contrast to the more traditional concept of substitu tion between alternative energy and nonenergy inputs. In doing so, the approach can claim the followi ng advantages: 1. it ‘ensures that the pattern of input use is consi stent with known technologies’ which usually exhibit what may be described as ‘lumpy’ or indivisibility constraints on certain inputs such as capital or labor, 2. it is highly transparent in the sense that it allow s an assessment of how some policy change can lead to ‘relative changes in the use of different technologies’ rather than a mere observation of the derived changes in inputs use (ABARE , 1996: 35). 3. the elasticity of substitution parameters in the t echnology bundle approach can be estimated “by reference to the results from 'bottom up' models” and therefore, can cover ‘a wider range of data values that might occur in a simulation’ (ABARE, 1996: 36). While in theory, it is true that the technology bun dle approach can provide a more realistic description of the constraints facing the energy pr oducing and energy-using industries than a conventional econometric approach, in practice, how ever, it is not clear how some of these potential advantages can always be implemented. In M EGABARE, for example, inputs into the technology bundles are still being specified as Leo ntief with no explicit ‘indivisibility’ or lumpy constraints imposed 4 

### . On point 3, it is not evident how the CRESH substitu tion parameter used in the MEGABARE model had been actually derived from s ome simulation experiment of a ‘bottom-up’ nature. On a more important point, the technology bundle app roach is not dissimilar to the conventional approach in econometrics where a neste d production structure is used to describe complex substitution possibilities among the inputs 5 

### . As Powell and Rimmer (1998) note: “Models in which output is produced according to a technology in which capital (K), labor (L) and energy (E) are substitutable run into the diffi culty of how to allow parsimoniously for the higher likely substitutability between K and E than between L and E”. In fact, the issue of ‘substitutability’ or ‘complementarity’ between K a nd E is a long-standing issue in the energy debate (see section 3.2 below). To handle this issue , most models allow for K and E to be separated from L. In the technology-bundle approach, although E and K are complements within a given technology structure, they are substitutes at the higher level, where technologies are substitutable for each other. Thus, given an energy price increase, although K cannot be used to replace E immediately in any given technology, a les s energy-intensive but more capitalintensive technology can be put in place, to counter the ener gy price rise, thus fulfilling the

4 The MEGABARE documentation (ABARE, 1996) does not r efer to any of these indivisibility constraints but in a different documentation (Hanslow et al. (1994:28)), a reference is made to ‘capacity const raint’ in the context of the discussion of the pricing formula for a commodity w hich is used as input into a particular 'technology '. Here, it is stated that ‘capacity constrained technology earns above n ormal returns to capital’ which is to be represente d by a ‘slack’ variable. 5 See for example, Perroni and Rutherford (1995), Pow ell and Rimmer (1998).12



### Table 4 Summary Characteristics of MEGABARE Model Characteristics MEGABARE Top-down versus bottom-up Bottom-up in technology b undle specification, top-down in the rest of the model structure Dynamic Recursive Inter-fuel substitution Indirectly through technolo gy substitution Fuel-factor Substitution Indirectly through technol ogy substitution Capital – Energy complementarity/substitutability Energy-capital are complements within a given techn ology, but can be substitutable through technology substitutio n.



### function of substitutability between K and E in the longer run. In this respect, the technology bundle approach is quite innovative and flexible. 

## 2.3 The OECD’S GREEN Model 6 

### GREEN is a global, dynamic AGE model which highlights the re lationships between depletion of fossil fuels, energy production and use, and CO 2 

### emissions. The main focus is on the energy sector and its linkage to the economy. There are three types of fossil fuels in the model - oil, natural gas, and coal - and one source of non-fossil energy - the electricity secto r. Each of these can be replaced at some future date by "backstop" technologies. These are assumed t o become available at an identical time period in all regions. Their prices are determined e xogenously and identically across all regions 7



### . This implies an infinite elasticity of supply. For each of the three fossil fuels, there are two a lternative backstop technologies: one carbon-free (e.g. biomass) and one carbon-based (sy nthetic fuel derived from shale or coal, with higher carbon content than conventional technology). For electricity, the backstop technology is carbon-free (nuclear fusion, solar or wind power, but excl uding hydro, or nuclear fission). There are eight energy-producing sectors in GREEN: Co al mining, Crude oil, Natural gas, Refined oil, Electricity-gas-water distributio n, Carbon-based back-stop, Carbon-free backstop, Carbon-free electric back-stop. The three non- energy producing sectors are Agriculture, Energy-intensive industries, and Other industries and ser vices. There are four consumption goods: Food beverages and tobacco, Fuel and power, Transport and communication, and Other goods and se rvices. These are chosen to be different from the outputs of the production sectors to highli ght the principal components of final demand

6 Burniaux, J. M., Nicoletti, G., and J. Oliveira-Mar tins (1992), “GREEN: A Global Model for Quantifying the Costs of Policies to Curb CO 2 Emissions”, OECD Economic Studies No. 19, Winter, 49-92; Lee, Hiro, Joaquim OliveiraMartins, and Dominique van der Mensbrugghe (1994), “The OECD GREEN Model: An Updated Overview”, OECD Development Centre Technical Paper No. 97.
7 Their marginal costs, however, are not identical, a nd therefore, there is a return attributed to the f ixed factor. Backstops are not traded. Their role is primarily t o limit the rise in prices, and therefore in carbon taxes.13



### for energy. Consumers are assumed to be deciding on the optimal allocation of their given disposable income on saving and the four consumptio n goods. The demands for these consumption goods are then translated into the dema nds for producer goods (and energy) via a ‘transition’ or make matrix. There are twelve regions in the GREEN model: United States, Japan, EC, Other OECD, Central and Eastern Europe, The former Soviet Union, Energy-exporting LDCs, China, India, Dynamic Asian Economies (Hong Kong, Philippines, Si ngapore, South Korea, Taiwan and Thailand), Brazil, Rest of the World (RoW). Finally, there are five different types of primary factors: labor, sector-specific "old" capital, "new" capital, sector-specific fixed facto rs (for each fossil fuel type, and for the carbonfree backstop), and land in agriculture. 

### 2.3.1 Dynamics in GREEN One special feature of the GREEN model is in its dyn amic treatment of the energy-capital complementarity / substitutability issue and also in the handling of the resource depletion issue. The dynamics in GREEN in fact come mainly from these two issues: depletion of exhaustible resources, and capital accumulation. In the resource depletion ‘sub-model’, the total (pr oven plus unproven) reserves are assumed to be determined exogenously. However, the rate at which 'unproven' reserves are converted into 'proven' reserves (rate of discovery or rate of conversion) is made sensitive to the prices of oil and gas. This affects the 'potential s upply', which is defined by the rate at which proven reserves are extracted 8 

### . Potential supply provides an upper bound on actua l supply, and if actual demand falls short of potential supply, then the difference between potential and actual supply is added to the future reserves of the fossil fuels. The resource depletion sub-model is thus recursively dynamic (i.e. based on current and past prices only) rather than forward looking (i.e. based on some expected future prices). Capital accumulation in the GREEN model is influence d by the putty/semi-putty assumption on the nature of capital. New capital (ca pital invested in current period) is putty, i.e. it is highly substitutable for other factors (elasti city of substitution is 2). Sector-specific old capital (capital invested in previous periods), on the other hand, is semi-putty and much less substitutable for other factors (elasticity of subs titution can be as low as 0.25). Sector-specific old capital is also much less mobile between sectors (implying small and sector-specific supply elasticities). This can result in equilibrium renta l values of old and new capital being significantly different from each other, and the rat io of these rental values is used in GREEN to stimulate 'disinvestment' of old capital (see Burniau x et al. (1992: 57)). Once disinvested, old capital becomes available for use in new investment . At any point in time, the stock of capital will consist of old and new capital, and the rate o f substitution between the stock of capital as a whole and other factors will therefore depend on th e vintage structure of capital. Apart from this dynamic vintage structure, GREEN does not include a ny other explicit investment behavior by firms. The total aggregate level of investment is d efined as a residual from the aggregate level of

8 Though the extraction rate is assumed constant over time, energy prices affect the potential supply of oil and gas through the price sensitive conversion rate (Burnia ux etal. 1992, vand der Mensbrugghe, 1994).14



### savings minus government sector balance and plus net capital inflows. Once the aggregate level of investment is determined, this is then distribut ed optimally to the various sectors in order to equate rates of return on new investment. 

### 2.3.2 Inter-fuel Substitution 2.3.2.1 Inter-fuel Substitution in Production In estimating the inter-fuel elasticities of substit ution, the general assumption is that energy and capital are weakly separable in production. This me ans that firms choose the cost-minimizing energy-mix given an energy-capital bundle. But this makes sense onl y if there are dual-fired or multi-energy technologies available, otherwise, inte r-fuel substitution will involve the installation of new capital and therefore, the assu mption of separability between energy and capital breaks down (Burniaux et al. (1992, p. 75)). Thus, in choosing to represent the potential for inter-fuel substitution, the GREEN model assume s that short run to medium run elasticities of substitution between alternative forms of energy ar e small, between 0.5 and 1.0 in the medium term, and only 0.25 in the short term. Long-run 9 

### elasticities of inter-fuel substitution, however, are set as high as 2.0. This latter value is said to be based on empirical estimates of elasticities based on samples which have multiple power-generating facilities (Burniaux et al. , loc. cit. ). These inter-fuel substitution elasticities apply onl y to the non-energy producing sectors and the electricity generation sector. For the rest of the energy producing sectors (coal mining, crude oil, natural gas, refined oil), there is no inter-fuel s ubstitution (see Burniaux et al. (1992, Table 3, p. 76)) The structure of inter-fuel substitution in product ion in the 1992 version of the GREEN model is as shown in Figure 9. In a subsequent versio n 10 

### , the structure is altered significantly to allow for three levels of nested substitution: (i) substitution between electricity and a 'nonelectric' composite fuel, (ii) substitution between coal and a 'non-coal' composite within the nonelectric branch, and finally, (iii) substitution be tween oil, gas, and refined fuels within the noncoal branch. All substitution elasticities are set within the range 0.25 < ) < 2, depending on whether it is short-run, medium-run, or long-run. 2.3.2.2 Inter-fuel Substitution in Household Demand Given the energy intensity of each consumer good, h ousehold demand for aggregate energy is derived from its demand for the four categories of consumer goods (see Figure 10). Once the demand for aggregate energy is known, this demand i s then allocated optimally between the different fuels with the same structure of inter-fu el substitution as in the case of producers’ demand for energy (Figure 9).

9 This long run is defined as the period over which new capital can be installed.

10 S ee Lee et al. (1994, Figure 1b, p. 49)15



### Figure 8 The Structure of Production in GREEN

Domestic Gross output

Leontief



### Importe d

Region r Region 1

... 

### Labor

Capital-Fixed factor Composite Energy

Leontief



### Fixed factor Capital16



### Figure 9 Energy and Backstop Technologies in GREEN

. (a) With elasticity of substitution ( σ =0.25) for ‘old’ capital, and ( σ =2) for ‘new’ capital, in all sectors except coal mining, crude oil, natural gas, and refined oil (se e Burniaux et. al. , 1992, Figure 1b, p. 56, and Table 3, p. 76). In Lee et al. (1994), there is some further nesting (all with 0. 25< σ< 2): between electric and non-electric’ composite, then between ‘coal’ and non-coal’ composite within the non-electric branch, and finally between oil, g as, and refined fuel in the non-coal branch (b) Elasticity of substitution between conventional and backstop technologies is ( σ =10) for agriculture, refined oil, electricity, energy-intensive industries, and other industries, as well as for consumer goods and gove rnment demand, and in the production of investment goods a nd inventories. (c) Elasticity of substitution between domestic and imported fuels is ( σ =4) for all fuels, except electricity ( σ =0.3), and crude oil ( σ = ∞ ). (d) Elasticity of substitution for fuels from differ ent regions (world trade elasticities) is ( σ = ∞ ) for crude oil, ( σ =5) for coal mining and natural gas, and ( σ =3.0) for refined oil. (e) Same as for coal. (f) Same as for coal except with ( σ = ∞ ) for domestic-imported and inter-regional substitu tions. (g) Same as for coal except there are no backstop fu els and world trade elasticities is ( σ =3). (h) Same as for coal except there is only one carbon-fr ee backstop option and world trade elasticities is ( σ =0.5). 

### Energy

CES (a)



### Coal

CES (b)

Carbon-based backstop fuel Conventional Carbon-free backstop fuel
CES (c)



### Imported Domestic

Region r

Region 1

...

CES (d) 

### Gas (e) Crude oil

(f) Electricity (h) Petroleum Products (g)17



### Figure 10 The Structure of Household Demand in GREEN

(a) Same as for transport & communication. (b) See Figure 9. 

### Disposable income

ELES

Leontief

Other producer 

### goods Energy (b)

CES



### Imported Domestic Food and Beverages Saving

Fuels & power Transport &

communication Other goods

and services



### ...(a) ...(a) ...(a)18



### 2.3.3 Fuel-factor Substitution The GREEN model assumes that capital-labor and ener gy-labor have the same (positive) elasticities of substitution. This assumption accords with empirical econometric evidence which supports substantial short-run and long-run substit utability between labor and capital on the one hand, and also between labor and energy on the othe r hand. On the issue of energy-capital substitutability or complementarity, however, empiri cal estimates seem to be more of a problem. A widely held opinion in this area is that perhaps energy and capital are complements in the short-run, but substitutes in the long-run. To incor porate this feature into the model, the approach in GREEN is to utilize a ‘vintage capital’ structur e. Thus, short run substitution between ‘old’ capital and energy can be low, while long-run substit ution between ‘new’ capital and energy can be high. The net effect will then depend on the cap ital vintage structure. Over time, the short-run elasticities will converge to the long-run elastici ties (see Figure 5 in Burniaux et al. (1992, p. 66)). The gap between short- and long-run elasticit ies and the speed of the convergence depends on the dynamics of the capital stock adjustment proc ess which in turn depends on assumptions made about depreciation rate and rate of new capita l formation. The larger the net replacement rate, the smaller the gap between short- and long-r un elasticities and the faster the convergence of the former to the latter. In GREEN, capital is combined with a fixed factor thr ough a Leontief structure before being combined with energy through a CES structure. The role of the fixed factor is to limit the substitution away from/towards capital formation in the energy-producing sectors so as to avoid an unrealistic situation where, for example, followi ng an increase in the relative price of energy, 'too much' investment will occur in these sectors ev en in the short run. The role of the fixed factor in primary-energy producing sectors is thus to impose limits on the supply elasticities of these primary energies. These supply elasticities ha ve a critical role to play, especially in energyenvironmental policy simulation studies. Substitution between energy and the fixed factor-ca pital composite is set at zero for all energy-producing sectors, except electricity. For e lectricity and other non energy-producing sectors, it is set at zero for 'old' capital, and a t a low value of 0.8 for new capital. Substitution between labor and capital-energy-fixed factor compo site is also set at zero for all energyproducing sectors including electricity. For other sectors, it is set at a low value of 0.12 for old capital and a high value of 1.0 for new capital (Burniaux at al. (1992, Table 3, p. 76). According to Borges and Goulder (1984, p. 340), to ensure that the capital-energy complementarity condition can be achieved, it is ‘su fficient’ that the elasticity of substitution between K and E within the KE nest be given a ‘subs tantially smaller (even if positive)’ value as compared to the elasticity of substitution between t he KE composite and labor (or other factors) in the ‘outer nest’. To be more precise, we can use t he following formula established for the case of a nested CES structure by Keller (1980, p. 83):

VA KE VA inner KE outer KE S σ

σ σ σ +

− =

− − /] [ 

### In this formula, S KE 

### is the share of the KE-composite in the outer (val ue-added) nest, and

inner KE

− σ 

### and outer

KE

− σ 

### stand for the inner and outer substitution elastic ities between K and E respectively. If inner KE

− σ 

### is less than ı VA 

### , then the first term on the right hand side is negative. But whether outer

KE

− σ 

### is negative (implying complementarity between K and E in the outer nest) depends on the size of S KE 

### as well. If S

KE 

### is small, then this is likely even if ı VA 

### is large. For19



### example, using the upper limit values of 0.8 and 1. 0 for inner KE

− σ 

### and ı VA 

### respectively as used in the GREEN model for the case of new capital, this r equires S KE 

### < 0.2 for outer

KE

− σ 

### < 0 (complementarity between K and E in the outer nest) . Using the lower limit values of 0.0 and 0.12 respectively for inner KE

− σ 

### and ı VA 

### for the case of old capital, this requires S KE 

### < 1.0 for

outer KE

− σ 

### < 0. The condition is always satisfied since S KE 

### is always less than 1. Overall, thus, ‘old’ capital and energy will always come out as co mplements in the value added nest of the GREEN model production structure. For ‘new’ capital , this will also be the case if the share of capital-energy-fixed factor component in the value- added nest is less than 20 percent. Note that all these discussions apply to the non energy-produ cing sectors only. For the energy-producing sectors (except electricity) there is no fuel-facto r substitution. The electricity sector is characterized by an ‘inner’ substitution elasticity of inner KE

− σ 

### = 0.8 (for new capital only), and a zero ‘outer’ substitution elasticity of ı VA 

### = 0 in the value-added nest. This implies ‘new capi talfixed factor bundle’ and ‘energy’ are always substitutes in the electricity sector. 

### 2.3.4 Comments on the GREEN Model
One innovative feature of the GREEN model is in the handling of the energy-capital complementarity / substitutability issue through the use of a dynamic capital vintage structure. Through this structure, the issue of long-run subst itutability versus short-run complementary between capital and energy is handled quite flexibl y (see the illustrative numerical calculations carried out in the previous section). This is a signi ficant improvement over many other models which do not handle this issue explicitly. The specification of the capital vintage structure i s an important first step. However, the next step can perhaps focus attention also on the i ssue of capital investment. Currently, the aggregate level of investment in the GREEN model is specified as a residual from the level of aggregate saving minus government sector balance plu s net capital inflows. Once the aggregate level of investment is determined, the aggregate le vel of new investment is then distributed optimally among the sectors. Following from this, t he ratio of the new- to old-capital rates of return is also determined, and this will then influ ence the rate of old-capital disinvestment (i.e. the rate at which old capital is transformed back i nto the pool of ‘new’ investment in the next period). All of this will affect the capital vintag e structure. Throughout this process, energy prices play an important role, in influencing the rate of ret urn on (old and new) capital, and hence on aggregate investment. However, this influence is still indirect via the aggregate return on capital. A more direct role for energy prices may be in influencing the capital vintage structure directly, for example, in bringing about a rate of investment which will ‘equalize’ the rates of return on ‘old’ and ‘new’ capital over the ‘long ru n’. This, however, implies a more ‘forward looking’ investor than is currently assumed for the GRE EN model.20



### Table 5 Summary Characteristics of GREEN Model Characteristics GREEN Top-down versus bottom-up Top-down with some bottom -up details in backstop technologies specifications. Dynamic Recursive Inter-fuel substitution Yes Fuel-factor Substitution Yes Capital – Energy complementarity/substitutability Given the vintage structure of production, capital and energy tend to be compliments in the short term and substi tution over the longer term.


## 2.4 The Babiker-Maskus-Rutherford (BMR) Model Babiker, Maskus, and Rutherford (1997) utilize a mod el for studying the economic impact of international trade and environmental policies on t he world economy. The model includes a detailed structure of the inter-fuel and energy-fac tor substitution possibilities for the firm and for the household sector (see Figures 12 and 13). The structure of production in the BMR model groups labor and capital together. This means that one cannot give to the energy-capital co mponents a different elasticity of substitution as compared to the energy-labor or capital-labor com ponents, and this is a severe restriction. On the other hand, the internal structure of the inter-f uel substitution in the BMR model does contain a rich structure, firstly with a distinction betwee n electricity and non-electricity inputs, and then further disaggregation of the non-electric inputs i nto various types of fuels using a nested-CES structure (see Figure 12) with 5 levels: oil and nat ural gas at level 0 (bottom level); coal at level 1; electricity, land, labor, and capital are at lev el 2; aggregate energy and aggregate primary factor is at level 3; intermediate input and the co mbined energy-primary factor is at level 4; and finally output is at level 5. To calculate the elasticity of substitution between any two inputs n and m at a particular level L in the nested-CES structure, we can refer to the formula deri ved by Keller (1980, p. 83): 

### ] [ 1

, 1 1 , 1 , 1

, , − −

− + = −



### − − = 

# ¦ l n l n L

K l l n K n K n nm 

### S

S S σ σ σ

where K represents the lowest level in the nested-CES struc ture at which a component exists, associated with both the n and the m inputs (the lowest common level) and L is the highest level in the nested structure at which the elasticity ı nm 

### is calculated, and the cost share S n,l 

### is defined by: 

# ¦

∈ 

### =

n i i l n 

### S S

,21



### i.e. the sum of all the cost shares associated with the aggregate input n at level l , or, in other words, the cost share of the input component n . Using this formula, and considering the production struct ure of Figure 12, we can conclude that: (1) energy-capital 11 

### substitution elasticity ı EK 

### (considered at the top level, i.e. holding output constant, L =5) is simply equal to 0.5/ S EF 

### where S

EF

### is the cost share of aggregate energyprimary factors (land, labor, capital) in the produ ction structure. Since this value is less than 1.0, ı EK 

### is greater than 0.5 - the CES substitution elasticity at leve l K= 4. (2) For inter-fuel substitution, electricity and non-elec tricity have an elasticity of substitution of: 1/ S E 

### – 0.5*[1/ S

E 

### -1/ S EF 

### ] = 0.5/ S

EF 

### + 0.5/ S

E 

### where S

E 

### is the cost share of aggregate energy in the produ ction structure. Since S E 

### is rather small, the elasticity of substitution between elect ricity and non-electricity can therefore be very large. For example, with S E 

### = 0.05, S

EF 

### = 0.70, the overall, output- constant, elasticity of substitution between electricity and non-electricity i s 10.71. (3) The elasticity of substitution between oil and gas is giv en by:
1/ S OG 

### – 0.5*[1/ S OG



### -1/ S COG 

### ] – 1*[1/ S COG



### -1/ S E 

### ] – 0.5 [1/ S

E 

### -1/ S EF 

### ] =
0.5/ S OG 

### – 0.5*/ S COG



### + [0.5/ S

EF 

### + 0.5/ S

E 

### ]
where S OG



### or S COG 

### is the cost share of inputs ( oil , gas ) or inputs ( coal , oil , gas ) in the total production structure. Again, assuming that S OG 

### = 0.010 and S COG



### = 0.015, the overall elasticity of substitution between oil and gas is then 22 + 10.71 = 32.71. This is a very large figure. The large magnitude of these output-constant (upper l evel) elasticities of substitution as compared to the composite input-constant (lower-leve l) elasticities of substitution can be explained as follows. When a composite input (such as aggregate energy E ) is held constant, there is only a limited opportunity for the various components (fuels) of this composite energy to be substituted for one another. When the level of o utput is held constant, however, there are also substitutions between different types of aggregate inputs (e.g. aggregate energy E for capital K

, or composite K-E

for labor L , etc). This increases the range of substitution (o r complementarity) between the lower-level inputs (fuels). Refer to Fi gure 11, for example, where it is assumed for simplicity that aggregate energy consists of only oi l and gas. When the level of aggregate energy is held constant, an increase in the price of oil ( relative to gas) will induce a substitution of gas for oil (movement from A to B). When the level of o utput is held constant, aggregate energy consumption may fall because aggregate energy price has increased relative to other factors: B may now move towards C. The total movement is now from A to C, which shows a larger reduction in oil consumption following an oil price increase, and therefore, it seems as though the degree of ‘substitutability’ between oil and ga s is now much larger. Furthermore, as we go up the production structure, the share of the energy inputs w ill get smaller, and since the elasticity of

11 Or energy-labor, or energy-land: since labor, land, and capital are grouped together, their substituti on elasticity with respect to energy will be the same for all three pr imary factors.22



### substitution is price elasticity ‘normalized’ by th e cost share, it will get even larger as the cost share gets smaller. The purpose of these upper- or outer-level elasticit y calculations is to show that the overall level of substitution between any two input components within a particular nest may be much larger than the magnitude of the substitution el asticities. This point is important to keep in mind when we compare different models which may have s imilar elasticities, but different nested structures.


### Figure 11 Substitution Elasticity when Total Output is Held C onstant.

Gas Composite energy input held constant

Oil Output held constant 

### A • • B

• C23



### Figure 12 Structure of Production in the Babiker-Maskus-Ru therford (1997) Model

OUTPUT

Leontief

Cobb-Douglas CES ( σ =0.5)

Land 

### Non-energy intermediaries ( plus crude inputs to refinin g)

Labor Capital Cobb-Douglas

Electricity

CES ( σ =0.5)

Oil Natural Gas Coal Cobb-Douglas24



### Table 6 Summary Characteristics of the BMR Model

Model Characteristics BMR Model Top-down versus bottom-up Top-Down Dynamic Recursive Inter-fuel substitution Yes Fuel-factor Substitution Yes Capital – Energy complementarity/substitutability E nergy is rather a compliment to capital (as is land and labor.


## 2.5 Borges and Goulder (1984) Model Borges and Goulder (1984, p. 340) assume a much sim pler structure for the inter-fuel and fuel-factor substitution possibilities. However, the model allows for labor to be separated from capital, and energy and capital are to be grou ped together in one nest. This is consistent with the approach taken in the GREEN model. To allo w for the possibility of significant complementarity between K and E, Borges and Goulder assumed a fixed-coefficient structure for the KE composite. Using the Keller formula as descr ibed in the previous section, the substitution elasticity between energy and capital at the top le vel would then be given by 

### ) EK 

### = –1*[1/ S

EK 

### – 1], where S

EK 

### is the cost share of capital and energy inputs. Si nce S EK 

### < 1, then 

### )

EK 

### < 0, i.e. capital and energy are significant complements at the top leve l of the production structure. On



### Figure 13 Structure of Final Demand in the Babiker-Maskus-R utherford (1997) Model

CES ( σ =0. 5)



### All non-energy goods Cobb-Douglas Cobb

- Douglas

Electricity Oil Natural Gas Coal 

### Final Demand25



### Figure 14 Structure of Production in Borges and Goulder (1984) Model

the issue of inter-fuel substitution, Borges and Gou lder assume a Cobb-Douglas structure, but recognize that perhaps with the petroleum product an d gas sectors, a fixed coefficient technology would be more appropriate (see Figure 14). On the household consumption side, the utility struc ture allows for substitution between ‘current consumption and future consumption’, as we ll as between ‘goods and services’ and leisure. The goods and services sector is Cobb-Dougl as with three different types of energy commodities: electricity, gas and ‘gasoline and other fuel s’. 

### OUTPUT

Cobb-Douglas

Cobb

- Douglas Leo ntief

Coal mining Non-energy intermediaries including imports Cobb

- Douglas

Electricity Crude & natural gas Capital Labor

Energy

Leontief

Petroleum refining Gas

utilities26



### Table 7 Summary Characteristics of the Borges and Goulder Model Model Characteristics Borges and Goulder Model Top-down versus bottom-up Top-Down Dynamic Simultaneous Inter-fuel substitution Yes Fuel-factor Substitution Yes Capital – Energy complementarity/substitutability Strict complementarity between capital and energy.


# 3. Towards a GTAP Model with Energy Substitution

In this section we discuss the issue of how to incor porate the important features of energy substitution as reviewed in the previous section in to the GTAP model. Currently, in the standard GTAP model 12 

### , there is no inter-fuel, nor fuel-factor (energy - primary factor) substitution, even though recent version of the model allows for a non -zero constant elasticity of substitution between all intermediate inputs. This latter feature is an impr ovement over previous versions. However, it still does not go far enough to allow for an adequate treatment of the issue of energy substitution, hence a more substantial approach needs to be t aken here. There are two important issues which must be addresse d when considering extending the GTAP model to include energy substitution in its st ructure. The first relates to the question of a choice between a ‘top-down’ versus a ‘bottom-up’ ap proach. The second relates to the question about complementarity / substitutability between energy an d capital inputs over time. 

## 3.1 Top-Down Versus Bottom-Up Approach In selecting an approach for incorporating energy-su bstitution into the GTAP model, there are generally two different approaches 13 

### . The ‘bottom-up’ (engineering) approach often start s with a detailed treatment of the energy-producing processe s or technologies, and then asks the questions: given a particular level of demand for energy services (which may be defined in terms of the level of outputs of certain activities, such as travel, heating, air conditioning, lighting, or even steel making, etc.), what is the most efficient way of going about meeting these demands in terms of the energy technologies employed and the l evel of inputs. The top-down (economic) approach, on the other hand, starts with a detailed description of the macro (and international) economy and then derives from there the demand for energy inputs in terms of the demand for various sectors’ outputs through highly aggregate producti on or cost functions. The advantage of a bottom-up approach is in the det ailed specification of the energy technologies, through which newly developed or futu re technologies can be incorporated into the

12 As documented in Hertel, T.W. and M.E. Tsigas "Str ucture of GTAP", Chapter 2 in Hertel (1997).
13 See, for example, Wilson and Swisher (1993 ).27



### analysis. This provides it with much more realism than in the econometrically-specified ‘production function’ of the top-down approach. On the other hand, the latter can claim advantage in the fact that there is historical evid ence in support of the assumed behavioral response implied in the production function specifi cation, whereas the bottom-up technology specifications may lack this behavioral content 14 

### . To utilize the advantages of both approaches, a top-down (macro-econometric or computable general e quilibrium) model can be ‘linked’ to a bottom-up process model and the two models are solv ed simultaneously. However, there are many theoretical and computational difficulties ass ociated with such a linkage. As a result, in some cases, a ‘partial link’ is pursued (such as th e ETA-MACRO link in the CETM model discussed in section 2) or a ‘simulated’ approach t o a process model is used (such as the specification of the energy-sector production possi bilities in terms of ‘technology bundles’ in the MEGABARE model, see also section 2). While there ar e certain advantages associated with these ‘partial’ approaches, the price to pay for su ch an approach is in the added complexity in model specification, and also the additional data and parameter requirements. For example, in the MEGABARE model, there is the question of what parame ters are to be used for the substitution between the ‘technology bundles’ to ensure some con sistency with observed behavior based on historical data. As a result of these difficulties, and the desire to offer a widely-accessible energy model, these approaches are not pursued here. Inste ad, it is suggested that a simple ‘top-down’ approach be used, which can incorporate most of the important features of the existing top-down models in this area, such as the GREEN or BMR models. 

## 3.2 The Issue of Energy-Capital Substitutability or Complementarity Having settled on a top-down approach to represent energy-substitution, the next question to consider is: which particular structure should be us ed to represent the substitution possibilities between alternative fuels (inter-fuel substitution) and between the energy aggregate as a whole and other primary factors, such as labor and capita l (fuel-factor substitution). In particular, the question of energy-capital complementarity or substi tutability is a major issue in this literature. In this section, we look at this issue from a theore tical viewpoint and then go on to review some of the empirical estimates of the parameters for en ergy and capital substitution /complementarity in the literature. 

### 3.2.1 Importance of the Issue According to Vinals (1984), the issue of energy-cap ital complementarity or substitutability may turn out to be a crucial one in determining the dir ection of the adjustment of aggregate output following energy price changes: ‘...the key parameter that determines whether outpu t produced goes up or down after an energy price increase is the degree of complement arity/substitutability between energy and capital, measured by ı EK 

### [the substitution elasticity between energy and capital]’ (Vinals, 1984: 237-238).

14 As a result, there would be some difficulties in gu essing what would be the future rates of penetratio n of new technologies into the market.28



### In Vinals' simple one-sector model with no distortion s, when the capital stock is given, and the wage level is flexible, energy-capital substitutabi lity ‘is a sufficient condition for output produced to decline following an energy price incre ase. Alternatively, energy-capital complementarity is a necessary condition for output produced to rise following an energy price increase’. These results point out ‘ how crucial it is for macroeconomic analysis to det ermine whether energy and capital are complements or substitutes ’ (Vinals, 1984, p 238, italics original) 

### 3.2.2 Empirical Estimates of σ EK 

### Despite the theoretical importance of the ı EK 

### parameter, empirical estimates of this parameter must overcome many difficulties. Table 4 gives some indicative values of ı EK 

### as estimated from various empirical studies. It can be seen from this Table that both the sign and magnitude of this parameter varies significantly between different studies. The problem arises partly because energy-capital sub stitutability is a long-term adjustment process, and therefore, empirical estimat es of ı EK 

### must take into account the issue of how short-term energy usage can be dynamically adju sted to a ‘theoretically optimal’ level in the long run, based on the level of investment. Convers ely, capital must also adjust to the expected level of energy prices in the long term. Hogan (198 9) has shown that where a ‘correct’ specification of a dynamic capital-energy usage str ucture is specified, more meaningful and accurate estimates of the inter-fuel and energy-pri mary factor substitution elasticities can be achieved. The key to the problem of specification i s that a model must be able to represent the flexibility (in energy usage) in the long run but a lso allow for rigidity or inflexibility in the shor t to medium term due to capital constraint: ....responses to price changes take time. Although there is overwhelming evidence of great flexibility in the use of energy and other in puts, the most important changes in energy utilization depend upon changes in energy-us ing equipment. If this equipment changes slowly, then the full response to energy price changes will take many years to unfold... Initially, the price shocks have little effect on demand per unit of output; often the effects are so small as t o suggest little response at all. But the new prices unleash forces that eventually produ ce dramatic changes in total energy demand...this demand response can be both a substantial break from trend and a confusing mixture of fuel substitutions. Analy sis of this short-run record, in the search for insights into long-run possibilities, places great emphasis on the need for a description of the dynamics of energy demand adjustment 15



### . Inflexibility in capital adjustment comes from techno logical factors (such as discrete or lumpy investments), as well as adjustment costs. To d escribe this ‘inflexibility’, one approach is to use a technology or process model. Alternatively, the long-term adjustment proces s of capital can also be specified directly in an economic model (such as in GREEN). However, it is not always easy to find empirical estimates for the par ameters of these models, hence the uncertainty surrounding the extent of energy-capital substitutabilit y or complementarity.

15 Hogan (1989, p. 54)29



### Table 8 Estimates of the Partial Hicks-Allen Elasti cities of Substitution ( ı ) and Factor Shares ( ı ). US

Berndt-Wood (1975) US

Kulatilaka (1980) US

Pindyck (1979) Europe

Pyndyck (1979) Australia

Truong (1985)

) KK -8.8 -2.75 -1.66 -0.98 -16.46

) LL -1.5 -0.22 -1.19 -0.82 -1.388

) EE -10.7 -2.70 -24.21 -13.16 -19.60

) MM -0.39 -0.222

) KL 1.01 0.69 1.41 0.69 1.02

) KE -3.5 -1.09 1.77 0.60 -2.95

) LE 0.68 0.61 0.05 1.13 1.77

) KM 0.49 0.78

) LM 0.61 0.42

) EM 0.75 0.17

 L 0.289 0.76 0.478 0.526 0.263

 E 0.044 0.10 0.032 0.055 0.023

 K 0.046 0.14 0.488 0.409 0.044

 M 0.619 0.67 K = Capital, L= Labor, E = Energy, M= Material. Source: Vinals (1984), Table 3, p. 242, and Truong (1985). 

## 3.3 The Structure of Inter-fuel and Fuel-factor Sub stitution in GTAP-E 3.3.1 Production Structure with Energy Substitution
Based on the various structures of inter-fuel and f uel-factor substitutions adopted in other models as described in section 2, the following is suggested as a go od option for GTAP-E. On the production side, energy 16 

### must be taken out of the intermediate input ‘nest’ to be incorporated into the ‘value-added’ nest (see Figure s 15 and 16). The incorporation of energy into the value-added nest is in two steps. First, f ollowing the structure in the CETM model as well as the Babiker-Maskus-Rutherford (1997) model, energy commodities are first separated into ‘electricity’ and ‘non-electricity’ groups. So me degree of substitution is allowed within the non-electricity group ( ı NELY 

### ) as well as between the electricity and the non-el ectricity groups ( ı ENER 

### ). The values of these substitution elasticities ar e shown in Table 5. These are chosen to be in the middle range of the values adopted in other models.

16 Primary energy (such as coal, gas, crude oil) can b e used, not only as a source of energy input for va rious industrial and household activities (e.g. natural gas to provi de the energy source for electricity production, co al as energy source for steel making), they can also be used as a ‘feed stock’. In this latter use, the chemical content of the energy input (such as natural gas) is simply ‘transformed’ to be come part of the output commodity (such as fertiliz er) rather than being ‘used up’ as an energy source. Similar exampl es are crude oil used as feedstock in the petroleum refinery industry, coke used as a feedstock in steel product ion, etc.30 

### Next, the energy composite is then combined with cap ital to produce an energy-capital composite 17 

### , which is in turn combined with other primary factor s in a value-added-energy ( VAE ) 18 

### nest through a CES structure (See Figure 17). The s ubstitution elasticity between capital and the energy composite ( ı KE 

### ) is still assumed to be positive (indicating energ y and capital are substitutes in the ‘inner nest’). However, provided the value of ı KE 

### is set at a level lower than
ı VAE 

### , the overall substitution elasticity (as viewed fr om the ‘outer nest’) between capital and energy may still be negative (Borge and Goulder (19 84, p. 340)). To be more precise, we can use the formula derived by Keller (1980, p. 83) which s pecifies the relationship between the ‘inner’ and ‘outer’ elasticity of substitution between K and E as follows:

VAE VAE KE VAE inner KE outer KE S S / /] [ σ

σ σ σ +

− =

− − 

### where S

KE 

### is the cost share of the KE -composite in the outer (value-added) nest, and ı KE-inner 

### and
ı KE-outer 

### indicate the inner and outer substitution elasticities bet ween K and E respectively.



### Figure 15 Standard GTAP Production Structure

17 The reason for a focus on the energy-capital compos ite was given in section 3.2. See also the discussi on in section 2.3.3 regarding the differences between energy-capi tal and energy-labor substitution. 18 The term ‘value-added-energy’ is used to emphasize the fact that energy is now present in this ne st. 21 For details on the industry sector aggregation, see Table A1 of the Appendix. 

### Output

Capital Foreign σ D Value-added All other inputs (including energy inputs) σ = 0

Land Labor σ VA

Domestic
σ M

Region 1 Region r



### ...31



### Figure 16 GTAP–E Production Structure

F 

### igure 17 GTAP–E Capital-Energy Composite Structure Output

Capital- Energy Composite Foreign σ D Value-added-Energy ( Including energy inputs) All other inputs ( Excluding energy inputs but including energy feedstock) σ = 0

Lan Labor σ VAE

Domestic

Region 1 σ M Natural Resource s

Skille Uskilled ) LAB

Region r

...

σ D Capital- Energy Composite

Electric

Foreign σ D Capital Energy Composite

σ KE

σ ENER = 1. 0

Domesti c σ M Non

- Electric

Coal σ N EL Y =0.5

σ M Foreign σ D
Domestic

Region 1 ...

... Region 1

Region Region r ...

... σ N CO L =1.0

Petroleum products Oil Non-Coal

Foreign Domestic

Region 1 Region Gas

σ M32Table 9 Energy Substitution Elasticities in GTAP-E and Other ModelsSECTORCapital-Energy (K-E)Inter-FuelGTAP-E (e)SECTORGTAP-E (a)GREEN(b)Rutherford(c)Electric vsNon-electricCoal vs othernon-electricbetween non-coal,non-electricGREENCoal0.0 0.0 0.0 0.0 0.0 0.0 0.0Crude Oil 0.0 0.0 0.0 0.0 0.0 0.0 0.0Gas0.0 0.0 0.0 0.0 0.0 0.0 0.0Petroleum, coal products 0.0 0.0 0.0 0.0 0.0 0.0 0.0Electricity 0.5 0.0 - 0.8 -(d)- 1.0 0.5 1.0 0.25 -2.0Ferrous metals 0.5 0.0 - 0.8 0.5 1.0 0.5 1.0 0.25 -2.0Chemical, rubber, plastic products 0.5 0.0 - 0.8 0.5 1.0 0.5 1.0 0.25 - 2.0Other manufacturing; trade, transport 0.5 0.0 - 0.80.5 1.0 0.5 1.0 0.25 - 2.0Agriculture, forestry, and fishery 0.0 0.0 - 0.8 0.5 1.0 0.5 1.0 0.25 - 2.0Commercial/public services, dwellings 0.5 0.0 - 0.80.5 1.0 0.5 1.0 0.25 - 2.0For details on the sector aggregation, see Table A1of the Appendix.aTo ensure capital and energy are complements in theshort run, while substitutes in the long run, theelasticity of substitution betweenKandEaggregate must be set lower than theelasticity betweenKand other primary factors ( )VAE).bIn the GREEN model, if the long-term elasticity betweenKandEis equal to 2.0 and the short run value set equalto 0.25, then the ‘intermediate’ term (approximately 5 years)elasticity - which depends on the vintage structureof the capital - will be about 0.8 (see Figure 5,Burniauxet al.(1992), p. 66).cIn Rutherford models, a value of 0.5 is set for substitution between energy composite and land-labor-capital composite in the non energy-producing industries (Babikeret al.(1997)),or between energy composite and labor-capital composite (Rutherfordet al(1997), and Bohringer and Pahlke (1997)).dK-Esubstitution for the electricity industry is determined, not by an econometric parameter, but by thespecification of alternative electricity-generationtechnologies in the ‘processmodel’.eThis is based on the values of 1.0 chosen for the substitution between electric and non-electric, andbetween non-coal fossil fuels in (Babikeret al.(1997)), Rutherfordet al.(1997).Bohringer and Pahlke (1997) however, chose a value of 2.0 for the substitution between non-coal fossilfuels. For substitution between coal and non-coal fossil fuels, Babikeret al.(1997) chose a value of 0.5, whereas Bohringer andPahlke (1997) chose a value of 0.25 if the non-coalaggregate includes electricity.33 

### In GTAP-E, the (inner) value of ı KE 

### is assumed to be 0.5 for most industries 21 

### (including electricity), and is set equal to 0.0 for coal, oil , gas, petroleum and coal products, and agriculture/forestry/fishery. This is based on the (l ow-to-middle) range of the values adopted by other models, such as the GREEN model, and the mode ls used by Babiker et al. (1997), Rutherford et al. (1997), Bohringer and Pahlke (1997) (see Table 5). The value of ı VAE 

### ranges from 0.2 to 1.45 and this seems to be slightly larg er than the values adopted by other models (see Table 6), but these are the values currently used in the stand ard GTAP model. Based on the values of S KE 

### for some typical regions in the GTAP- 4E data base 22 

### , the ‘outer’ values of ı KE 

### are derived using the above formula and are shown in Table 7. From this Table, it can be seen that most industries (with the exception of ‘electricity’ in the USA, and ‘electricity’, ‘ferrous metals’, and ‘chemical, rub ber, plastic products’ in Japan) are characterized as having an overall complementarity relationship be tween energy and capital despite the fact that ı KE 

### is still specified as non-negative within the energy-capit al nest.



### Table 10 Elasticities of Substitution Between Different Fa ctors of Production GREEN Sector GTAP-E a (  VAE ) L

- KEF

b E

- KF c Rutherford d

Coal 0.2 0.0 0.0 1.0 Crude Oil 0.2 0.0 0.0 1.0 Gas 0.84 0.0 0.0 1.0 Petroleum, coal products 1.26 0.0 0.0 1.0 Electricity 1.26 0.0 0.0 - 0.8 1.0 Ferrous metals 1.26 0.12 - 1.0 0.0 - 0.8 1.0 Chemical, rubber, plastic products 1.26 0.12 - 1.0 0.0 - 0.8 1.0 Other manufacturing; trade, transport 1.45 0.12 - 1 .0 0.0 - 0.8 1.0 Agriculture, forestry, and fishery 0.23 0.12 - 1.0 0.0 - 0.8 1.0 Commercial/public services, dwellings 1.28 0.12 - 1 .0 0.0 - 0.8 1.0

a In GTAP-E: between land, natural resources, aggre gate labor, and capital-energy composite. b Between labor ( L ), and energy-capital-fixed factor composite ( EKF ). c Between energy ( E ) and capital-fixed factor composite ( KF ). d Between land, labor, and capital (see Babiker et al. (1997)), or between labor and capital (Rutherford et al (1997), and Bohringer and Pahlke (1997)).

22 See Malcolm and Truong (1999).34



### Table 11 The Relationship Between Inner ( ) KE-inner 

### ) and Outer ) KE-outer 

### ) Elasticities of Substitution for the Cases of Japan and the US Japan USA Sector  KE-inner  VAE S VAE S KE  KE-outer S VAE S KE  KE-outer Coal 0.0 0.2 0.49 0.11 -1.50 0.67 0.16 -0.97 Crude Oil 0.0 0.2 0.64 0.24 -0.52 0.69 0.34 -0.30 Gas 0.0 0.84 0.97 0.95 -0.02 0.81 0.55 -0.49 Petroleum, coal products 0.0 1.26 0.68 0.59 -0.28 0 .91 0.88 -0.04 Electricity 0.5 1.26 0.83 0.71 0.45 0.84 0.71 0.43 Ferrous metals 0.5 1.26 0.51 0.34 0.27 0.43 0.18 -1 .35 Chemical, rubber, plastic products 0.5 1.26 0.42 0.26 0.05 0.50 0.30 -0.05 Other manufacturing; trade, transport 0.5 1.45 0.46 0.16 -2.65 0.51 0.18 -2.45 Agriculture, forestry, and fishery 0.0 0.23 0.58 0.20 -0.77 0.46 0.26 -0.38 Commercial/public services, dwellings 0.5 1.28 0.62 0.30 -0.58 0.63 0.23 -1.41 Note: VAE VAE KE VAE inner KE outer KE S S / /] [ σ

σ σ σ +

− =

− − , where S KE , σ KE-inner are the cost share and substitution elasticity respectively for the capital-energy composite and S VAE , σ VAE are the cost share and substitution elasticity res pectively for the value-added-energy composite. 

### Finally, Tables 8 and 9 show the Armington elastic ities for the substitution between domestic and imported good ( ı D 

### ), and between imported goods from different region s ( ı M 

### ). The values of ı

D 

### and ı

M 

### for GTAP-E are taken from the ‘standard’ GTAP mode l, and are seen to be lower than some of the values used in other models, such as those in Babiker et al. (1997). In studies which seek to simulate the trade effect of a ‘homogeneous energy commodity market’ (such as that for coal) in response to an energy-en vironmental shock (such as the imposition of a carbon tax), these Armington elasticities may play a crucial role. However, this issue is not considered in this paper.



### Table 12 Elasticities of Substitution Between Domestic and F oreign Sources ( σ D 

### ) Sector GTAP-E GREEN b Rutherford c Low-High Coal 2.80 4.0 2.0 Crude Oil 10.0 a ∞ ∞ Gas 2.80 4.0 2.0 Petroleum, coal products 1.90 4.0 2.0 Electricity 2.80 0.3 2.0 Ferrous metals 2.80 2.0 4.0 – 8.0 Chemical, rubber, plastic products 1.90 2.0 4.0 – 8 .0 Other manufacturing; trade, transport 2.59 2.0 4.0 – 8.0 Agriculture, forestry, and fishery 2.47 3.0 4.0 – 8 .0 Commercial/public services, dwellings 1.91 2.0 4.0 – 8.0

a This is higher than the standard value of 2.8 used in most GTAP applications. b Burniaux et al. (1992), p. 76.

c Babiker et al. (1997),3536



### Table 13 Elasticities of Substitution Between Different Reg ions ( ) M 

### ) Sector GTAP-E GREEN b Rutherford c Low-High Coal 5.60 5.0 4.0 Crude Oil 20.0 a ∞ ∞ Gas 5.60 5.0 4.0 Petroleum, coal products 3.80 3.0 4.0 Electricity 5.60 0.5 4.0 Ferrous metals 5.60 3.0 8.0 - 16.0 Chemical, rubber, plastic products 3.80 3.0 8.0 - 1 6.0 Other manufacturing; trade, transport 6.04 3.0 8.0 - 16.0 Agriculture, forestry, and fishery 4.62 4.0 8.0 - 1 6.0 Commercial/public services, dwellings 3.80 3.0 8.0 - 16.0
a This is higher than the standard value of 5.6 used in most GTAP applications. b Burniaux et al. (1992), p. 76.
c Babiker et al. (1997).



### 3.3.2 Consumption Structure On the consumption side, the existing structure of GTAP assumes a separation of ‘private’ consumption from ‘government’ consumption (consumpt ion by households of publicly provided goods) and private savings. Government consumption expenditure is then assumed to be CobbDouglas with respect to all commodities ( 

### ) G 

### = 1). In the GTAP-E model, energy commodities are separated from the non-energy commodities with a ne sted-CES structure as shown in Figure 18. If, however, the substitution elasticity 

### ) GEN 

### given to the inner energy nest and 

### ) GENNE 

### given to the outer nest are both equal to 1 (substitution elasti city 

### ) GNE 

### in the non-energy nest is assumed to be equal to 

### )

G 

### and is therefore also equal to 1), then the GTAP-E structure is equivalent to the original GTAP structure. In general, however, if 

### ) GEN 

### ≠ 

### )

GENNE 

### ≠ 1, then the GTAP-E structure allows for different substitution elasticities with in the energy and non-energy sub-groups, as well as between the two groups. For the current version of GTAP-E, the following values are adopted: 

### ) GEN 

### = 1, and 

### )

GENNE 

### = 0.5. This structure is very similar to the struct ure of household demand given in Rutherford et al. (1997) (see Figure 3), and Bohringer and Pahlke (199 7), except that in the model of Bohringer and Pahlke, a smaller value of 0.3 is used for substitution between energy and non-energy aggregates, and a higher value of 2 i s used for substitution between fossil fuels (excluding coal).37



### Figure 18 GTAP-E Government Purchases

Household ‘private’ consumption (i.e. consumption o f private goods) is assumed to be structured according to the constant-difference of e lasticities (CDE) functional form in the existing GTAP model. If the energy commodities with in the CDE structure have the same income and substitution parameters, then according to the theory of the CDE structure, these commodities can be aggregated into a single composi te with the same parameters as that of the individual components. Currently, in fact, within t he GTAP model, four of the five energy commodities (coal, oil, gas, and electricity) have similar parameters, which differ only from that of the ‘petroleum and coal products’. This implies w e can aggregate the energy commodities into a composite which remains in the CDE structure and h as the same (or the average of the) CDE parameter values characterizing the individual ener gy commodities. To allow for flexible substitution between the individual energy commodit ies, the energy composite is now specified as a CES sub-structure, with a substitution elastic ity of 

### ) PEN 

### = 1 (see Figure 19) which is similar to the value given to 

### ) GEN 

### (see Figure 18). This is the same as the value adopt ed in Rutherford et al. (1997) (see Figure 3) and consistent with the mediu m term value adopted in the GREEN model (see section 2.3.2). 

### Demand for composite goods

Domestic Foreign σ GNE = 1 Energy composite Non-energy composite σ GENNE = 0.5

Region r σ D



## .... σ GEN = 1

Coal

σ M 

## .... ....

.... .... ....

Region r38



### Figure 19 GTAP-E Household Private Purchases

To better characterize the behavior of GTAP-E in c omparison with GTAP, it is worth calculating the overall general equilibrium elastic ities in both models (see Annex 1). GE elasticities depend on the structure of the model, the value of the substitution parameters and the particular closure assumed. They also depend on the benchmark database. The elasticities in Annex 1 have been calculated by using the version 4 of the GTAP data base. Thus the elasticities reported in Annex 1 are primarily to illustrate the behavioral implications of introducing interfuel substitution. Since these elasticities are als o dependent on the base data, they are different in the current version of the model that is based on the version 5 of the GTAP data base. 

# 4 .Illustrative Scenario
GTAP-E has been specifically designed to simulate pol icies in the context of Greenhouse Gas (GHG) mitigation. This is best illustrated by using GT AP-E (based on GTAP Version 5 Data Base) to simulate the Kyoto Protocol. By signing this Protocol in 1997, a number of industrialized countries – referred to hereafter as Annex 1 countries – committed themselves to reduce their GHGs emissions relative to their 1990 levels. Initially, the Protocol aimed at ambitious reductions: the total emissions of Annex 1 countries were planned to be brought down in 2012 by 5 per cent below their 1990 levels. The P rotocol made provision for country specific targets. A number of so-called “flexibility mechani sms” were also provided in order to allow emission reductions to be reallocated among Annex 1 countries. The “Emission Trading” (ET) Household demand for Private Goods

Domestic Foreign

Energy composite Å Non-energy commodities Æ CDE

Region r σ D



## .... σ PEN = 1

Coal

σ M 

## .... ....

Region r Domestic Foreign

Region r 

## .... σ

M
Region r σ

D39



### mechanism and the “Joint Implementation” (JI) mecha nism aimed at reallocating the burden of the emission reductions among Annex 1 countries. In contrast, the “Clean Development mechanism” (CDM) would allow Annex 1 countries to f und emission reductions in non-Annex 1 countries
. However, the initial impetus of the Protocol rapidly faded away. While subsequent COP (Conferences of Parties) meetings struggled with in tricate methodological and implementation issues, emissions in most Annex 1 countries were gro wing well beyond the Kyoto targets. As time passed, the Kyoto objectives increasingly appeare d out of range to some Annex 1 members - - particularly the USA. In March 2001, the USA deci ded to withdraw from the Protocol. Though the remaining Annex 1 countries reiterated their co mmitment to implement the Protocol in Bonn, it is most likely that the US withdrawal will make t he Protocol aggregate constraint nearly nonbinding at the level of the remaining Annex 1 countries 23



### . The scenarios discussed in this section are primari ly illustrative. Specific limitations include, firstly that they refer to the initial version of t he Protocol, including the US. Secondly, they only consider emissions of carbon dioxide while the Protoc ol covers a basket of GHGs and includes net emissions from land use changes. Thirdly, the u se of the flexibility mechanisms is approximated by assuming unrestricted emission trad ing leading to complete equalization of the marginal of costs of abatement among participating countries, an outcome that is most unlikely given the real-world limitations associated with fl exibility mechanisms. Finally, the Protocol is simulated in a static framework that leaves aside all aspects related to the timing of its implementation. 

## 4.1 Alternative Implementations of the Kyoto Protoc ol Three scenarios are considered. The first one is the “no trade” case. Here, Annex 1 countries meet their commitments individually without relying on the use of the flexibility mechanisms. The applied emission constraints correspond to the reduc tions that Annex 1 countries are forecast to achieve in 2012 – i.e. the first commitment peri od of the Protocol – relative to their corresponding emission levels in an unconstrained b aseline scenario. Since this information requires using a dynamic model with an explicit tim e dimension, it is not readily available in GTAP-E. The emission constraints used here are taken from the OECD GREEN model (OECD, 1999, p. 29). In the second scenario, unrestricted emission trading among Annex 1 countries approximates the use of ET and JI mechanisms (“Anne x 1 trade” case). The total emission constraint applied to Annex 1 countries in the seco nd scenario is the same as in the first one, augmented by the amount of “hot air” 24 

### from the Former Soviet Union. The third scenario assumes that carbon emissions are traded worldwide without any restriction (“world trade” case). The constraint applied to world emissions is the sum of the Annex 1 commitments and of the benchmark emission levels for the non-Annex1 countries.

23 This is because the emission surplus originating fr om the economic recession in the Former Soviet Unio n – often referred to as “hot air” – suffices to compensate t he reductions to be achieved in the remaining Annex 1 countries.
24 If Emission Trading is used, the emission surplus i n the Former Soviet Union can be, in principle, tra nsferred to other Annex 1 Parties at no cost. In this scenario, the amount of “hot air” in the Former Soviet Union is assumed equal to 100 million tons of carbon or 13 percent of the 1997 emission levels of the EEFSU region.40 

### Table 10 reports the emission changes relative to t he benchmark levels and the corresponding marginal abatement costs of meeting the em ission limitations. In the no-trade case, emission reductions range from 20 to almost 40 perce nt. These relatively sharp reductions reflect the fast growth rates of emissions, as observed in many Annex 1 countries since 1990, the reference year of the Protocol. The GREEN model ma kes the assumptions that these rates will remain almost unchanged during the first decade of t he 21 st 

### century. The marginal abatement costs corresponding to these reductions range from $126 in the US to $233 in Japan (where these are 1997 US dollars). These costs are in the range o f estimates from other studies (see Weyant and Hill, 1999; OECD, 1999). Marginal costs are lo wer in the US than in other Annex 1 countries – despite the higher reduction rate – beca use the US uses relatively more coal and taxes energy less heavily. In more carbon-efficient countr ies, such as Japan, the marginal abatement costs rise faster, other things being equal. The first column of Table 10 shows that while emissio ns are reduced in Annex 1 countries that are subject to binding constraints, they incre ase in the other countries, a phenomenon that used to be referred to as “carbon leakage”. The cau ses of carbon leakage are multiple and involve competitiveness effects as well as the reacti ons of the world energy markets 25 

### . In this scenario, the leakage rate – defined as the additio nal emissions in countries with no binding constraint relative to the emission reductions in c ountries with binding constraints – amounts to 7 per cent including the EEFSU region and 4 percent, excluding EEFSU 26 

### Allowing unrestricted trade among Annex 1 countries shif ts the burden of the reduction away from oil products in the relatively carbon-efficien t economies (USA, EU, JPN, and RoA1) towards coal in the Former Soviet Union. This induce s a substantial reduction of the marginal abatement costs: from around $150 in the no-trade c ase to $78 in the “Annex 1 trade” case). These cost savings imply that the EEFSU region sells about 300 million tons of carbon per year to other Annex 1 Parties, the largest single share of which is purchased by the USA (see Figure 20). This represents a transaction worth $24 billion per year. The right-hand section of Table 10 shows the results from a h ypothetical worldwide emission trading system. In this case, the largest reduction takes place in the CHIND region (China and India) while the Annex 1 countries account for less than half of the world reduction. The world marginal abatement cost does not exceed $30 per ton of carbon. At this price, around 650 million tons of carbon are traded each year, with China and India accounting for the largest sale share and the USA buying more than half of these emissions (see Figur e 21).

25 See Burniaux and Oliveira-Martins (2002) for an an alytical assessment of these effects.

26 Emission trading among Annex 1 counties implies th at constraint of the EEFSU region becomes effective as part of the Annex 1 total constraint while this constraint is not binding in the “no trade” scenari o. As a result, Annex 1 emissions increase “ex post” relative to their levels in the “no trade” scen ario by an amount equal to the “hot air” less the leakage that would occur in the EEFSU in the “not trade” case. In the same way, world emissions in the “world trade” case are higher than in the “no trade” case by an amount equ al to the “hot air: less the total leakage generated in t he EEFSU and in the non-Annex 2 regions in the “no trade” case. As for the non-Annex 1 regions, this might no t be realistic as most analysts recognize that the Clean Development Mechanisms is not going to prevent carb on leakages.41 

## Figure 20 : Emission trading among Annex 1

countries

-350 -250 -150 -50 50 150 250 350Million tons of carbon

USA

EU JPN RoA1

positive figures are sales; negative figures are pu rchases EEFSU hot air



## Figure 21 : Worldwide emission trading

-700 -500 -300 -100 100 300 500 700Million tons of carbon

positive figures are sales; negative figures are pu rchases USA

EU JPN RoA1 EEFSU hot air EEX CHIND RoW42Table 14 Marginal Costs of Achieving the Kyoto Targets with and Without Using the Flexibility MechanismsKyoto with No Use of the MarginalCostsKyoto with Emission Trading Kyoto with Worldwide Marginal Costs% Reduction ofEmissions(1997 USD perTon of Carbon)% Reduction ofEmissions(1997 USD perTon of Carbon)% Reduction ofEmissions(1997 USD perTon of Carbon)USA -36 126 -27 78 -13 30EU -22 147 -14 78 -6 30EEFSU 4 0 -27 76 -13 30JPN -32 233 -15 78 -6 30RoA1 -36 178 -21 78 -9 30EEx 3 0 2 0 -7 30CHIND -1 0 -1 0 -32 29RoW 4 0 4 0 -9 30Annex 1 -24 -22 -10Non-Annex 1 2 1 -19Leakage rate(incl. EEFSU)7.1% na naLeakage rate(incl. EEFSU)4.0% 3.7% naNote that the marginal costs are expressed in realterms (i.e. deflated by the GDP deflator of each country/region). Therefore, slightly different marginal costs in case ofemission trading are consistent with a common trading price of nominal terms43



## 4.2 Macroeconomic Results Table 11 reports the macroeconomic costs of implem enting the Kyoto Protocol in terms of the percentage change in per capita utility of t he representative household and the associated terms-of-trade changes. If the flexibility mechanis ms are not used, the costs for the Annex 1 Parties (measured in terms of utility of the repres entative regional household) ranges from 0.25 per cent in the USA to 1.3 per cent in the RoA1 reg ion. The higher cost in the RoA1 region is partly explained by the degradation of the terms-of -trade related to the fact that many countries belonging to this region are net energy exporters. In contrast, in the net energy-importing, Annex 1 economies, the costs of imposing carbon restricti ons are partly mitigated by terms-of-trade improvements associated with the reduction in inter national energy prices – particularly for oil. The EEFSU region loses 0.4 % of its welfare despite the fact that it has no carbon constraint to comply with; this loss is entirely explained by the fall of the energy exports value. Interestingly, some non-Annex 1 countries/regions might even lose m ore than the Annex 1 countries following the implementation of the Kyoto Protocol. This is clearly th e case for the energy exporters (EEx). Emission trading among Annex 1 countries (see the middle columns of Table 11) reduces the losses in all Annex 1 countries while g enerating substantial gains (+ 2.8 percent) in the EEFSU region. It also contributes to a reductio n in the losses incurred by the non-Annex 1 energy exporters as it shifts the burden of the red uction from oil towards coal and therefore implies a lower fall of the international oil price . A worldwide emission trading system would contribute to a reduction in the economic costs for the Annex 1 countries and energy exporters, while generating net gains in China, India and the EEFSU regi on. Figures 22 to 23 summarize the real income changes (in terms of equivalent variation) implied by the three alternative implementations of the Kyoto Protocol and provide a decomposition of the real income variations into te rms-of-trade and allocative 27 

### effects. The most noticeable outcome is that substantial cost saving can be achieved by allowing emissions to be traded. Annex 1 trading would cut the aggregate worl d real income loss by a half ($110 billion (1997 USD) to $50 billion) and a worldwide trading s ystem would further reduce the cost by another half (from $50 billion to less than $25 bil lion). It must also be noted that almost every party has a vested interest in some form of emissio n trading (with the noticeable exception of the RoW region) though the Former Soviet Union has an u nambiguous interest in restricting trading to Annex 1 countries only.

27 In Figures 22 to 24, allocative effects include pur e losses from less efficient allocations of product ion and consumption as well as the real income benefits and losses from the sales and purchases of carbon emis sions.44 Figure 22 : Welfare decomposition of implementing t he Kyoto Protocol with no use of the flexibility mechanisms

-120 -100 -80 -60 -40 -20 0 20 40

USA EU EEFSU JPN RoA1 EEx CHIND RoW A1 N-A1 World Terms of trade eff.

Allocative eff.

Figure 23 : W elfare decom position of im plementing t he Kyoto Protocol with trading am ong Annex 1 countries

-60 -50 -40 -30 -20 -10 0 10 20 30

USA EU EEFSU JPN RoA1 EEx CHIND RoW A1 N-A1 World Terms of trade eff.

Allocative eff. 

### Table 15 Macroeconomic Impacts of Implementing the K yoto Protocol: Percent change in welfare (in ) and terms of trade (tot) Kyoto With No Use of the

Flexibility Mechanisms Kyoto with Emission Trading Among Annex 1 Countries

only Kyoto with Worldwide Emission Trading
USA -0.25 0.96 -0.26 0.54 -0.16 0.18 EU -0.48 0.33 -0.27 0.20 -0.06 0.12 EEFSU -0.41 -0.87 2.75 0.92 0.66 0.05 JPN -0.61 1.34 -0.27 0.66 -0.07 0.43 RoA1 -1.30 -0.65 -0.86 -0.56 -0.42 -0.40 EEx -1.00 -3.02 -0.73 -2.19 -0.53 -1.47 CHIND 0.08 0.03 0.05 -0.01 0.44 -080 RoW 0.16 0.26 0.13 0.22 0.10 0.3245 Figure 24 : Welfare decomposition of implementing t he Kyoto Protocol with worldwide emission trading

-25 -20 -15 -10 -5 0 5 10

USA EU EEFSU JPN RoA1 EEx CHIND RoW A1 N-A1 World Terms of trade eff.

Allocative eff.



# 5. Conclusion This technical paper has surveyed some existing CGE models which deal with the issue of energy substitution. Important features of these models are highlighted, and where possible, some of these important features have bee n adapted into the existing standard GTAP model. The result in the model, nick-named GTA P-E is then used to conduct some alternative scenarios involving implementation of t he Kyoto Protocol. The main purpose of these experiments is to highlight the suitability of the GTAP-E model in analyzing the implications of alternative strategies to reduce GH G emissions. The introduction of the energy-environmental dimension in GTAP is only one step towards the elaboration of a GTAP framework that is suitable to analyze GHG issu es. It is hoped that the current version of GTAP-E could be further extended in orde r to incorporate some other aspects, such as the complex relationship between land uses and GHG em issions.46 ! closure with exogenous trade balances exogenous pop psaveslack pfactwld profitslack incomeslack endwslack tradslack ams atm atf ats atd aosec aoreg avasec avareg afcom afsec afreg afecom afesec afereg aoall afall afeall au dppriv dpgov dpsave to tp tm tms tx txs qo(ENDW_COMM,REG) RCTAX MARKCTAX dcwfd(NEGYCOM3,PROD_COMM,REG) dcwfd(COALS,COALS,REG) dcwfd(OILS,OILEXS,REG) dcwfd(GASS,GASEXS,REG) dcwfd(OIL_PCS,OIL_PCEXS,REG) dcwfi(NEGYCOM3,PROD_COMM,REG) dcwfi(COALS,COALS,REG) dcwfi(OILS,OILEXS,REG) dcwfi(GASS,GASEXS,REG) dcwfi(OIL_PCS,OIL_PCEXS,REG) dcwpd(NEGYCOM3,REG) dcwpi(NEGYCOM3,REG) dcwgd(NEGYCOM3,REG) dcwgi(NEGYCOM3,REG) c_CTAXBAS(REG,NEGYCOM3B) ! DTBAL exogenous for all regions except one, ! and cgdslack exogenous for that one region (wh ich can be any one). dtbal("USA") dtbal("EU") dtbal("EEFSU") dtbal("JPN") dtbal("RoA1") dtbal("EEx") dtbal("CHIND") cgdslack("RoW") ; Rest Endogenous ; swap gco2t("USA")=RCTAX("USA"); swap gco2t("EU")=RCTAX("EU"); swap gco2t("JPN")=RCTAX("JPN"); swap gco2t("RoA1")=RCTAX("RoA1"); Shock gco2t("USA") = -35.6; Shock gco2t("EU") = -22.4; Shock gco2t("JPN") = -31.8; Shock gco2t("RoA1") = -35.7;47



# Annex 1 General Equilibrium Elasticities in GTAP-E and GTAP To compare GTAP-E with GTAP, the simplest and most effe ctive way is to compare the overall general-equilibrium (GE) elasticities of the GTAP-E model with those of the GTAP model. The GE elasticities are a function of the structure of the model, the values of the substitution parameters assumed, the benchmark database and the particular closure assumed 28 

### . For a standard GE closure where all the prices and quantit ies of non-endowment commodities are allowed to be endogenously determined, the GE elast icities calculated for this closure will truly reflect the general equilibrium character of the demand ela sticities 29



### . First we look at the GE own-price elasticities. These elasticities measure the perce ntage change in the output of commodity i in region r (i.e. qo ( i , r )) following a 1% change in its ownprice ( pm ( i , r )) induced by an appropriate perturbation in the ou tput tax to ( i , r ). The change in the output level can come from two different causes: (i ) changes in the general level of activity (we can refer to this as the “output (expansion or cont raction) effect”), and (ii) changes due to the substitution of one input or commodity for another (the “su bstitution effect” 30 

### ). For the energy commodities, because of the addition al (energy) input-substitution structure introduced into the GTAP-E model, we expe ct the negative “substitution effect” in this model to add to the negative “output effect” when t he price of an energy commodity increases. This means the magnitude of the GE own-price elastic ities for energy commodities in the GTAPE model is likely to be greater than those in the G TAP model. This is in fact confirmed in Table 10: the changes in the GE elasticities for the ener gy commodities are all negative when we go from GTAP to GTAP-E, indicating that the magnitudes of the (negative) elasticities are all increasing. For the non-energy commodities, on the other hand, since both the GTAP and GTAP-E models have similar structures for these commoditie s, we will expect that there are insignificant changes in the GE own-price elasticities as we move from GTAP to GTAP-E. From Table 10, this is again confirmed: the small variations in th e magnitudes of these elasticities for the nonenergy commodities arise only from the output (expa nsion/contraction) effects and which are seen to be small. Also, the variation can be in either directi on. Tables 11 and 12 give the GE cross-price elasticities for the US and China for illustrative purposes. For both of these countries, we notice that all energy commodities are substitutes (cross-price elasticities being positiv e), with the exception of the pairs: COL and ELY, and OIL and P_C. These pairs of energy commodity are complements because COL is a significant input into ELY, and similarly OIL is a significan t input into P_C. As we move from GTAP to GTAP-E, the magnitudes of the cross-price GE elasticities for the energy commodities become greater, as expec ted. This is in contrast to the case of the GE

28 As the GE elasticities are a function of the partic ular closure assumed, in this section, we present t he GE elasticities which are associated with the experiment considered in the next section. Changing this experiment and its closure will affect the GE elasticities. 29 See Chapter 5 of Hertel (ed.) (1997).
30 Here substitution can occur between different outputs (i.e. in final demand) as well as between differen t inputs (intermediate demand).48



### cross-price elasticities for the non-energy commodi ties. In the latter case, since both GTAP and GTAP-E assume similar structures for these non-ener gy commodities, their corresponding GE cross-price elasticities as thus also similar 31



### . Finally, between the energy and non-energy commodit ies, we notice a significant degree of complementarity (negative cross-price elasticitie s) between P_C and ELY on the one hand, and the non-energy commodities on the other hand. T his reflects the importance of P_C and ELY as major energy inputs into the production of these non-energ y commodities.

31 The non-energy commodities are also observed to be all ‘substitutable’ for each other despite the fac t that in the intermediate input sub-structure, zero substitution was assumed between these non-energy intermediate inputs. The ‘substitution’ as reflected in the GE cross-price e lasticities, however, reflects mainly the output (c ontraction/expansion) effects, which come from a re-allocation of resourc es resulting from a change of the relative prices a mong these commodities.49



### Table A1-1 General-Equilibrium Own-Price Elasticities GE Elasticities WITH Energy Substitution from GTAP- E Model (A): Sectors/ Commodities JPN CHN IND USA E_U FSU NEX NEM COL -3.75 -0.43 -0.07 -0.85 -1.19 -1.59 -1.22 -1.38 OIL -9.88 -3.02 -9.39 -3.33 -7.09 -5.27 -0.88 -7.39 GAS -1.69 -1.03 -0.72 -0.94 -1.46 -1.68 -1.27 -1.18 P_C -0.91 -0.83 -1.13 -0.97 -0.91 -1.28 -1.28 -1.05 ELY -0.84 -1.00 -0.79 -0.82 -1.15 -1.07 -1.21 -1.15 I_S -0.47 -0.86 -1.09 -0.78 -1.00 -2.83 -1.66 -1.78 CRP -0.50 -1.02 -1.15 -0.95 -0.96 -1.27 -1.40 -1.26 OMN -0.75 -1.66 -1.43 -0.89 -0.87 -1.34 -1.40 -1.46 AGR -0.40 -0.32 -0.24 -0.67 -0.59 -0.99 -0.55 -0.56 SER -0.25 -0.27 -0.30 -0.32 -0.31 -0.30 -0.37 -0.35 GE Elasticities WITHOUT Energy Substitution from GT AP Model (B): Sectors/ Commodities JPN CHN IND USA E_U FSU NEX NEM COL -3.71 -0.40 -0.02 -0.26 -0.69 -1.14 -0.81 -1.03 OIL -9.82 -2.16 -9.13 -1.92 -4.70 -3.58 -0.24 -6.05 GAS -1.20 -0.03 0.00 -0.27 -0.92 -1.13 -0.65 -0.47 P_C -0.41 -0.32 -0.79 -0.40 -0.50 -0.85 -0.90 -0.54 ELY -0.22 -0.08 -0.03 -0.16 -0.34 -0.33 -0.48 -0.27 I_S -0.47 -0.85 -1.09 -0.78 -1.00 -2.82 -1.66 -1.78 CRP -0.50 -1.03 -1.16 -0.95 -0.96 -1.27 -1.40 -1.26 OMN -0.80 -1.59 -1.62 -0.93 -0.84 -1.41 -1.38 -1.48 AGR -0.40 -0.31 -0.24 -0.67 -0.59 -0.99 -0.54 -0.56 SER -0.25 -0.25 -0.29 -0.32 -0.29 -0.31 -0.37 -0.34 Change in Own-Price Elasticity from (B) to (A) Sectors/ Commodities JPN CHN IND USA E_U FSU NEX NEM COL -0.04 -0.03 -0.05 -0.59 -0.50 -0.45 -0.41 -0.35 OIL -0.06 -0.86 -0.26 -1.41 -2.39 -1.69 -0.64 -1.34 GAS -0.49 -1.00 -0.72 -0.67 -0.54 -0.55 -0.62 -0.71 P_C -0.50 -0.51 -0.34 -0.57 -0.41 -0.43 -0.38 -0.51 ELY -0.62 -0.92 -0.76 -0.66 -0.81 -0.74 -0.73 -0.88 I_S 0.00 -0.01 0.00 0.00 0.00 -0.01 0.00 0.00 CRP 0.00 0.01 0.01 0.00 0.00 0.00 0.00 0.00 OMN 0.05 -0.07 0.19 0.04 -0.03 0.07 -0.02 0.02 AGR 0.00 -0.01 0.00 0.00 0.00 0.00 -0.01 0.00 SER 0.00 -0.02 -0.01 0.00 -0.02 0.01 0.00 -0.0150



### Table A1.2 General-Equilibrium Cross-Price Elasticities for the USA

GE Cross-price Elasticities WITH Energy Substitution from GTAP-E Model (A): Sectors/ Commodities COL OIL GAS P_C ELY I_S CRP OMN AGR SER COL 0.06 0.00 0.03 -0.15 -0.01 -0.01 0.14 -0.03 0. 03 OIL 0.01 0.01 -0.21 0.05 0.01 0.01 0.52 0.02 0.06 GAS 0.00 0.14 0.16 0.09 -0.01 0.04 0.47 -0.13 0.11 P_C 0.02 -0.51 0.03 0.13 -0.02 -0.13 -0.94 -0.03 0 .14 ELY -0.07 0.10 0.01 0.10 -0.01 -0.02 0.20 -0.12 0. 09 I_S -0.01 0.04 0.00 -0.04 -0.03 0.06 0.21 0.04 0.32 CRP 0.00 0.02 0.00 -0.06 -0.01 0.01 0.90 0.03 0.36 OMN 0.00 0.02 0.00 -0.02 0.00 0.00 0.04 0.01 0.35 AGR -0.01 0.03 -0.01 -0.02 -0.08 0.01 0.04 0.28 0.18 SER 0.00 0.00 0.00 0.01 0.00 0.01 0.03 0.56 0.01

GE Cross-price Elasticities WITHOUT Energy Substitu tion from GTAP Model (B): Sectors/ Commodities COL OIL GAS P_C ELY I_S CRP OMN AGR SER COL 0.02 0.00 0.00 -0.09 -0.01 -0.01 0.40 -0.05 0. 13 OIL 0.00 0.00 -0.11 -0.01 0.01 0.01 0.81 0.02 0.15 GAS 0.00 0.02 0.00 -0.06 -0.01 0.02 0.50 -0.11 0.19 P_C 0.00 0.02 0.00 0.00 -0.01 -0.12 -0.29 -0.01 0. 36 ELY 0.00 0.01 0.00 0.00 -0.01 -0.03 0.19 -0.10 0.1 4 I_S 0.00 0.03 0.00 -0.02 -0.03 0.06 0.21 0.04 0.33 CRP 0.00 0.02 0.00 -0.01 -0.01 0.02 1.00 0.03 0.36 OMN 0.00 0.02 0.00 -0.01 -0.01 0.00 0.04 0.01 0.37 AGR 0.00 0.02 0.00 -0.01 -0.02 0.01 0.03 0.26 0.19 SER 0.00 0.00 0.00 0.01 0.01 0.01 0.03 0.62 0.01 Absolute difference:(A) - (B) Sectors/ Commodities COL OIL GAS P_C ELY I_S CRP OMN AGR SER COL 0.04 0.00 0.03 -0.06 0.00 0.00 -0.26 0.02 -0.1 0 OIL 0.01 0.01 -0.10 0.06 0.00 0.00 -0.29 0.00 -0.0 9 GAS 0.00 0.12 0.16 0.15 0.00 0.02 -0.03 -0.02 -0.0 8 P_C 0.02 -0.53 0.03 0.13 -0.01 -0.01 -0.65 -0.02 - 0.22 ELY -0.07 0.09 0.01 0.10 0.00 0.01 0.01 -0.02 -0.0 5 I_S -0.01 0.01 0.00 -0.02 0.00 0.00 0.00 0.00 -0.01 CRP 0.00 0.00 0.00 -0.05 0.00 -0.01 -0.10 0.00 0.0 0 OMN 0.00 0.00 0.00 -0.01 0.01 0.00 0.00 0.00 -0.02 AGR -0.01 0.01 -0.01 -0.01 -0.06 0.00 0.01 0.02 -0.01 SER 0.00 0.00 0.00 0.00 -0.01 0.00 0.00 -0.06 0.0051



### Table A1-3 General-Equilibrium Cross-Price Elasticities for China

GE Cross-price Elasticities WITH Energy Substitution from GTAP-E Model (A): Sectors/ Commodities COL OIL GAS P_C ELY I_S CRP OMN AGR SER COL 0.05 0.01 0.04 -0.01 0.02 0.04 1.19 0.06 0.01 OIL 0.01 0.00 -0.11 0.04 0.03 0.03 1.97 0.06 0.05 GAS 0.16 0.19 0.22 0.07 0.03 -0.30 0.60 0.01 0.02 P_C 0.03 -0.50 0.01 0.14 -0.04 -0.20 -2.01 -0.11 0 .00 ELY -0.01 0.16 0.00 0.14 -0.06 -0.13 -0.30 -0.03 0 .01 I_S 0.01 0.06 0.00 -0.02 -0.03 0.14 2.12 0.21 -0.0 3 CRP 0.01 0.03 0.00 -0.06 -0.04 0.09 2.61 0.05 0.06 OMN 0.01 0.07 0.00 -0.02 -0.01 0.03 0.07 0.09 0.05 AGR 0.00 0.01 0.00 -0.01 -0.01 0.03 0.01 0.76 0.12 SER 0.00 0.01 0.00 0.00 0.00 0.00 0.02 0.73 0.08 GE cross-price elasticities WITHOUT energy substitu tion from GTAP model (B): Sectors/ Commodities COL OIL GAS P_C ELY I_S CRP OMN AGR SER COL 0.01 0.00 0.00 0.00 0.03 0.05 1.10 0.04 0.00 OIL 0.00 0.00 -0.05 0.00 0.04 0.04 2.68 0.09 0.06 GAS 0.01 0.04 0.00 -0.01 0.02 -0.40 0.85 0.03 0.08 P_C 0.00 0.04 0.00 -0.01 -0.01 -0.18 0.40 0.03 0.0 8 ELY 0.00 0.04 0.00 0.00 -0.07 -0.13 0.39 0.05 0.06 I_S 0.00 0.05 0.00 -0.01 -0.01 0.15 2.26 0.21 -0.0 4 CRP 0.01 0.05 0.00 0.00 0.00 0.08 2.71 0.05 0.05 OMN 0.01 0.05 0.00 -0.01 -0.01 0.05 0.09 0.10 0.05 AGR 0.00 0.03 0.00 0.01 0.01 0.02 0.00 0.98 0.13 SER 0.00 0.01 0.00 0.01 0.01 -0.02 0.00 0.89 0.09 Absolute difference:(A) - (B) Sectors/ Commodities COL OIL GAS P_C ELY I_S CRP OMN AGR SER COL 0.04 0.01 0.04 -0.01 -0.01 -0.01 0.09 0.02 0.0 1 OIL 0.01 0.00 -0.06 0.04 -0.01 -0.01 -0.71 -0.03 - 0.01 GAS 0.15 0.15 0.22 0.08 0.01 0.10 -0.25 -0.02 -0.0 6 P_C 0.03 -0.54 0.01 0.15 -0.03 -0.02 -2.41 -0.14 - 0.08 ELY -0.01 0.12 0.00 0.14 0.01 0.00 -0.69 -0.08 -0. 05 I_S 0.01 0.01 0.00 -0.01 -0.02 -0.01 -0.14 0.00 0. 01 CRP 0.00 -0.02 0.00 -0.06 -0.04 0.01 -0.10 0.00 0. 01 OMN 0.00 0.02 0.00 -0.01 0.00 -0.02 -0.02 -0.01 0. 00 AGR 0.00 -0.02 0.00 -0.02 -0.02 0.01 0.01 -0.22 -0 .01 SER 0.00 0.00 0.00 -0.01 -0.01 0.02 0.02 -0.16 -0.0 152



# Annex 2 Specifying Country-specific Carbon Reducti ons with no Emission Trading in GTAP-E.
The following box shows the closure and shocks used to simulate the “no-trade” case. This scenario assumes no change of the trade account : thus the variable DTBAL (a linear variable expressed in changes) is exogenous and equal to zero in all countries/regions except one. Accordingly, the slack variable cgdslack is made end ogenous (while it is exogenous in the standard closure). Thus investment is calculated as a residue in order to guarantee no change of the trade account. The quantitative restrictions app lied to carbon emissions are introduced by making the real carbon tax RCTAX (i.e. the nominal carbon tax deflated by the GDP deflator) endogenous and the emission growth rates gco2t exog enous and equal to the Kyoto commitments (expressed as a percentage reduction relative to th e corresponding emission levels in 2010 in a scenario with no constraints). Alternatively, one mi ght impose an exogenous real or nominal carbon tax (RCTAX or NCTAX) and leave the emission growth rates to be determined endogenously. An accompanying program calculates the Social Accoun t Matrices (SAMs). The Table A2-1 below shows the SAM of the US after the emission constraint has been applied. The best way to interpret the income flows associated to the restriction is to assume that the restriction is imposed through a domestic market of emission rights . The row CAG shows the revenues that are perceived by some kind of centralized Carbon Ag ency from selling emission permits. The total proceeds of these sales amounts to 124 billio n 1997 USD, two thirds of which originate from sales to the electricity sector (42 billion 19 97 USD) and to the other industries and services (40 billion 1997 USD). Thus, in the electricity sec tor, purchases of emission permits would amount up to 15 per cent of all electricity sales. The total proceeds from domestic permit sales are then refunded to the Regional Household (see th e entry of 124 billion 1997 USD paid by of the RHH).53



### Box A.2.1 Closure and Shocks for No Trading Scenario ! closure with exogenous trade balances exogenous pop psaveslack pfactwld profitslack incomeslack endwslack tradslack ams atm atf ats atd aosec aoreg avasec avareg afcom afsec afreg afecom afesec afereg aoall afall afeall au dppriv dpgov dpsave to tp tm tms tx txs qo(ENDW_COMM,REG) RCTAX MARKCTAX dcwfd(NEGYCOM3,PROD_COMM,REG) dcwfd(COALS,COALS,REG) dcwfd(OILS,OILEXS,REG) dcwfd(GASS,GASEXS,REG) dcwfd(OIL_PCS,OIL_PCEXS,REG) dcwfi(NEGYCOM3,PROD_COMM,REG) dcwfi(COALS,COALS,REG) dcwfi(OILS,OILEXS,REG) dcwfi(GASS,GASEXS,REG) dcwfi(OIL_PCS,OIL_PCEXS,REG) ! dcwpd(NEGYCOM3,REG) dcwpi(NEGYCOM3,REG) dcwgd(NEGYCOM3,REG) dcwgi(NEGYCOM3,REG) c_CTAXBAS(REG,NEGYCOM3B) DTBAL exogenous for all regions except one, ! and cgdslack exogenous for that one region (which can be any one). dtbal("USA") dtbal("EU") dtbal("EEFSU") dtbal("JPN") dtbal("RoA1") dtbal("EEx") dtbal("CHIND") cgdslack("RoW") ; Rest Endogenous ; swap gco2t("USA")=RCTAX("USA"); swap gco2t("EU")=RCTAX("EU"); swap gco2t("JPN")=RCTAX("JPN"); swap gco2t("RoA1")=RCTAX("RoA1"); Shock gco2t("USA") = -35.6; Shock gco2t("EU") = -22.4; Shock gco2t("JPN") = -31.8; Shock gco2t("RoA1") = -35.7;5455



# Annex 3 Specifying Emission Trading in GTAP-E.
Setting up an emission trading system requires to identify a global emission constraint for the group of countries/regions involved in tradi ng and to allocate emission quotas among these countries/regions, the sum of which is equal to the global constraint. The global constraint in GTAP-E is imposed by making exogenous the variab le gmarkco2t (see the box below), while the corresponding marginal abatement cost for the t rading area (i.e. corresponding to the common price at which permits are traded) is specif ied as an endogenous variable (see MARKCTAX in the box below). The quotas allocated to each trading partners are specified by making the corresponding variables gco2q exogenous (note that these variables are endogenous and automatically equal to gco2t in the “no trade” scenario) and by “shocking” these variables along with a given quota allocation. It is to the u ser to verify that the sum of the quotas in terms of emission levels corresponds to the total constra int imposed to the exogenous variable gmarkco2t (in the example below, the weighted sum o f the quotas growth rates specified for the Annex 1 countries/regions must be equal to the exogenous re duction of the Annex 1 emissions by 22.13 % imposed to the variable gmarkco2t). Failure to specify a consistent quota allocation will result into trading flows imbalances. The closure below implies that the sum of the trade account an d the net carbon flows (i.e. the proceeds of emission sales and the expenditures of emissions purchases) is set exogenous and equal to zero. In other words, if a country buys emi ssion rights, it has to compensate for it by exporting more goods and services such as to satisfy to the assumption of a constant net capital flow with the rest of the world (i.e. the net invest ment-saving balance remains unchanged as will be illustrated later on). Alternative closure rules might, o f course, be used. The Table A3-1 shows the SAM for the US in the “Annex 1 trade” case. The total revenue perceived by the Carbon Agency (CAG) is low er than in the “no trade” case (76 billion of 1997 USD compared with 124 billion of 1997 USD). The explanation is twofold. First, extending emission trading to Annex 1 countries low ers the price of permits (from 126 1997 USD to 78 1997 USD per ton of carbon). Second, assu ming that the Carbon Agency plays a centralized role in articulating the domestic and the inte rnational permit market, it has now to pay for buying permits to the Former Soviet Union (see t he negative entry of 11 billion 1997 USD of the CAG row to the ROW column). The Table A3-2 reports the international flows including those related to permit trading. It shows that the t otal amount of permit sales by the EEFSU region amounts to 24 billion of 1997 USD, 11 billion s of it are sales to the USA (see the row CTRAD). Given the closure rule, the net capital flo ws in each country/region (ISBAL) remains constant and equal to their benchmark values so tha t any flow associated to permit trading has to be balanced by a compensatory change of the trade ac count (BALPW). For instance, permit sales in the EEFSU region make possible a deficit of the trade accou nt by 41 billion of 1997 USD.56 

### To summarize, specifying a permit-trading scheme in volving a sub- group of countries/reg requires the following steps:

o The countries/regions that are involved in trading ar e specified in the base data (basedata.har) by setting the corresponding values of the D_MARK coefficients (d um variable for participation to permit trading, header EMTR) e qual to unity.

o The corresponding RCTAX variables are set endogenous in the c losure.

o The country/region specific quotas have to be specif ied. This is done by making corresponding gco2q variables exogenous in the clos ure and by specifying the growth of t quotas in the SHOCK file.

o The aggregate emission growth for the trading area ( gmarkco2t) is set exogenous “shocked” accordingly whi le the equilibrium permit price for the area (i.e. the price at w permits are exchanged: MARKCTAX) becomes endogenous (see the corresponding SW statement below). Note that all values of the D_MARK coefficients sho uld be equal to zero unless a permit- tra scheme is specified.57



### Box A.3.1 Closure and Shocks for Emissions Trading Among Anne x 1 Countries

! basic closure exogenous pop psaveslack pfactwld profitslack incomeslack endwslack tradslack ams atm atf ats atd aosec aoreg avasec avareg afcom afsec afreg afecom afesec afereg aoall afall afeall au dppriv dpgov dpsave to tp tm tms tx txs qo(ENDW_COMM,REG) RCTAX("EEx") RCTAX("CHIND") RCTAX("RoW") MARKCTAX dcwfd(NEGYCOM3,PROD_COMM,REG) dcwfd(COALS,COALS,REG) dcwfd(OILS,OILEXS,REG) dcwfd(GASS,GASEXS,REG) dcwfd(OIL_PCS,OIL_PCEXS,REG) dcwfi(NEGYCOM3,PROD_COMM,REG) dcwfi(COALS,COALS,REG) dcwfi(OILS,OILEXS,REG) dcwfi(GASS,GASEXS,REG) dcwfi(OIL_PCS,OIL_PCEXS,REG) dcwpd(NEGYCOM3,REG) dcwpi(NEGYCOM3,REG) dcwgd(NEGYCOM3,REG) dcwgi(NEGYCOM3,REG) c_CTAXBAS(REG,NEGYCOM3B) ! DTBALCTRA (incl. permit trading) exogenous fo r all regions except one, ! and SAVESLACK exogenous for that one region (w hich can be any one). dtbalctra("USA") dtbalctra("EU") dtbalctra("EEFSU") dtbalctra("JPN") dtbalctra("RoA1") dtbalctra("EEx") dtbalctra("CHIND") cgdslack("RoW") gco2q("USA") gco2q("EU") gco2q("EEFSU") gco2q ("JPN") gco2q("RoA1") ; Rest Endogenous ; swap gmarkco2t=MARKCTAX; Shock gco2q("USA") = -35.6; Shock gco2q("EU") = -22.4; Shock gco2q("JPN") = -31.8; Shock gco2q("RoA1") = -35.7; Shock gco2q("EEFSU") = 12.869; Shock gmarkco2t = -22.132;58Table A3-2 International Flows in the “Annex 1 Trade” Scenario (billion of 1976 USD)1 USA 2 EU 3 EEFSU 4 JPN 5 RoA1 6 EEx 7 CHIND 8 RoW Total1 BALPW -140 99 -41 92 52 -4 25 -83 02 CTRAD -11 -6 124 -4 -3 0 0 0 03 ISBAL 150 -93 17 -88 -49 4 -25 83 0Total 0 0 0 0 0 0 0 0 059



# Annex 4 Specifying Emission Trading in GTAP-E

Table A4-2 Sectoral Disaggregation No. New Code Region Description Comprising GTAP V5 Countries/Regions 1 Agriculture Primary Agric.,

Forestry and Fishing paddy rice; wheat cereal grai ns n.e.c; vegetables, fruit, nuts; oil seeds; sugar

cane, sugar beet; plant- based fibers; crops n.e.c.; bovine cattle, sheep an d goats; animal products n.e.c.; rat milk; wool, silk

- worm cocoons; forestry; fishing 2 Coal Coal Mining coal 3 Oil Crude Oil oil 4 Gas Natural Gas

Extraction gas; gas manufacture, distribution 5 Oil_Pcts Refined Oil

Products petroleum, coal products 6 Electricity Electricity electricity 7 En_Int_Ind Energy Intensive

Industries minerals n.e.c.; chemical, rubber, plastic prod; mineral products n.e.c.;

ferrous metals; metals n.e.c. 8 Oth_Ind_Se r Other Industry &

Services bovine cattle, sheep and goad; meat products; veget able oils and fats; dairy

products; processed rice; sugar; food products n.e. c.; beverages and tobacco produc ts; textiles; wearing apparel; leather products; wo od products; paper products, publishing; metal products; motor vehicle s and parts; transport equipment n.e.c.; electronic equipment; machinery a nd equipment n.e.c.; manufactures n.e.c.; water; construction ; trade; transport n.e.c.; water transport; air transport; communication; financial services n.e.c.; insurance; business services n.e.c.; recreational and other se rvices; public admin. And defense, edu; ownership of dwellings 

### Table A4-1 Regional Disaggregation No. New Code Region Description Comprising GTAP V5 Countries/Regions 1 USA United States United States 2 EU European Union Austria; Belgium; Denmark; Finl and; France; Germany; United Kingdom; Greece; Ireland; Italy; Luxembourg; Netherlands; Po rtugal; Spain; Sweden
3 EEFSU Eastern Europe and

FSU Hungary; Poland; Rest of Central European Assoc: Fo rmer Soviet Union 4 JPN Japan Japan 5 RoA1 Oth. Annex 1 Countries Australia; New Zealand; Canada; Switzerland; Rest o f EFTA 6 EEx Net Energy Exporters Indonesia; Malaysia; Vie t Nam; Mexico; Colombia; Venezuela; Rest of Andean Pact; Argentina; Rest of Middle East; Rest o f North Africa; Rest of Southern Africa; Rest of Sub-Saharan Africa; Rest o f World 7 CHIND China and India China; India 8 RoW Rest of the World Hong Kong; Korea, Republic of; Taiwan; Philippine; Singapore; Thailand; Bangladesh; Sri Lanka; Rest of South Asia; Central America and Caribbean; Peru; Brazil; Chile; Uruguay; Rest of South America ; Turkey; Morocco; Botswana; Rest of SACU; Malawi; Mozambique; Tanzani a, United Republic of; Zambia; Zimbabwe; Uganda60



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