Appendix 1 presents sectoral (Tables A.1 and A.2), regional (Table A.3), and primary factors (Table A.4) identifiers in the GTAPinGAMS and GTAP-EG datasets. They both have 45 regions and 5 primary factors. The GTAPinGAMS dataset has 50 sectors, while the GTAP-EG dataset has 23 sectors (5 of which are energy sectors).
Table A.1. Sectoral identifiers in the Full GTAPinGAMS Dataset
PDR Paddy rice, B_T Beverages and tobacco,
WHT Wheat, TEX Textiles,
GRO Grains (except rice-wheat), WAP Wearing apparel,
V_F Vegetable fruit nuts, LEA Leather goods,
OSD Oil seeds, LUM Lumber and wood,
C_B Sugar cane and beet, PPP Pulp and paper,
PFB Plant-based fibers, P_C Petroleum and coal products,
OCR Crops n.e.c., CRP Chemicals rubber and plastics,
CTL Bovine cattle, NMM Non-metallic mineral products,
OAP Animal products n.e.c., I_S Primary ferrous metals,
RMK Raw milk, NFM Non-ferrous metals,
WOL Wool, FMP Fabricated metal products,
FRS Forestry, MVH Motor vehicles,
FSH Fishing, OTN Other transport equipment,
COL Coal, ELE Electronic equipment,
OIL Oil, OME Machinery and equipment,
GAS Natural Gas, OMF Other manufacturing products,
OMN Other Minerals, ELY Electricity,
CMT Bovine cattle meat products, GDT Gas manuf. and distribution,
OMT Meat products n.e.c., WTR Water,
VOL Vegetable oils, CNS Construction,
MIL Dairy products, T_T Trade and transport,
PCR Processed rice, OSP Other services (private),
SGR Sugar, OSG Other services (public),
OFD Other food products, DWE Dwellings,
CGD Investment composite
Table A.2. Sectoral identifiers in the Full GTAP-EG Dataset GAS Natural gas works FPR Food products ELE Electricity and heat PPP Paper-pulp-print OIL Refined oil products LUM Wood and wood-products COL Coal CNS Construction CRU Crude oil TWL Textiles-wearing apparel-leather I_S Iron and steel industry OMF Other manufacturing CRP Chemical industry AGR Agricultural products NFM Non-ferrous metals T_T Trade and transport NMM Non-metallic minerals SER Commercial and public services TRN Transport equipment DWE Dwellings, OME Other machinery CGD Investment composite OMN Mining
Table A.3. Regional identifiers in the Full GTAPinGAMS and GTAP-EG Datasets AUS Australia (*), ARG Argentina, NZL New Zealand (*), BRA Brazil, JPN Japan (*), CHL Chile, KOR Republic of Korea, URY Uruguay, IDN Indonesia, RSM Rest of South America, MYS Malaysia, GBR United Kingdom (*), PHL Philippines, DEU Germany (*), SGP Singapore, DNK Denmark (*), THA Thailand, SWE Sweden (*), VNM Vietnam, FIN Finland (*), CHN China, REU Rest of EU (*), HKG Hong Kong, EFT European Free Trade Area(*), TWN Taiwan, CEA Central European Associates (*), IND India, FSU Former Soviet Union (*), LKA Sri Lanka, TUR Turkey, RAS Rest of South Asia, RME Rest of Middle East, CAN Canada (*), MAR Morocco, USA United States of America (*), RNF Rest of North Africa, MEX Mexico, SAF South Africa, CAM Central America and Caribbean, RSA Rest of South Africa, VEN Venezuela, RSS Rest of Sub-Saharan Africa, COL Columbia, ROW Rest of World RAP Rest of Andean Pact, The Annex B regions are denoted by (*). CEA includes Bulgaria, Czech Republic, Hungary, Poland, Romania, Slovakia, and Slovenia. REU includes Austria, Belgium, Spain, France, Giblartar, Greece, Ireland, Italy, Luxembourg, Netherlands, and Portugal. EFT includes Switzerland, Iceland, and Norway. FSU includes Armenia, Azerbaijan, Belarus, Estonia, Georgia, Kazakhstan, Kyrgyzstan, Lithuania, Latvia, Moldova, Russia, Tajikistan, Turkmenistan, Ukraine, and Uzbekistan.
Table A.4. Primary Factor Identifiers in the Full GTAPinGAMS Dataset
LND Land,
SKL Skilled labor,
LAB Unskilled labor,
CAP Capital,
RES Natural resources
Appendix 3 describes the mapping of IEA and GTAP 4 production sectors into
GTAP-EG format. For more details, see Rutherford and Paltsev [2000]
where the process of incorporating of IEA statistics into GTAP-EG is
described. The original IEA statistics has 35 sectors. The following
table presents a concordance between IEA and GTAP-EG production sectors.
An aggregation of GTAP 4 into GTAP-EG is done with the aggregation
routine gtapaggr, described in Section 4. The following
table shows the mapping.
Appendix 5 presents the function declarations for GTAP-EG model
implemented in MPSGE.
1 Development of these tools has been supported by the Electric
Power Research Institute and the United States Department of Energy.
The authors are indebted to Randy Wigle and Jared Carbone for their
discussions and comments. The authors can be reached at:
rutherford@colorado.edu, sergey.paltsev@colorado.edu
2 University of Colorado, Department
of Economics, Boulder, CO 80309-0256, USA.
3 A current version of GTAP
database is GTAP 4. The fifth version is announced to be released in
2000.
4 These tools have been implemented with the assistance
of Ken Pearson using modified versions of his SEEHAR.EXE and
MODHAR.EXE programs.
5 GTAPinGAMS has 51 goods/production sectors: 50 goods +
Investment composite (CGD)
6 Users can define their own aggregations of the GTAP data
and use any labels to describe regions. For technical reasons, if a
GTAP dataset is to be used with MPSGE, then regional identifiers can
have at most four characters.
7 GTAP-E-FIT has the same identifiers as the
GTAP4 dataset.
8 Energy is defined as the
capacity to do work. One joule (J) is a unit of energy equal to the
work done when a force of 1 newton acts through a distance of 1
meter. One joule is approximately equivalent to the potential energy
of one apple one meter above the floor. 1 exajoule (EJ) = 1018J.
For conversion: 1 EJ = 23.88 million tonnes of oil equivalent (MTOE).
For electricity: 1kwh = 3.61 ·106 J, or 1EJ = 0.2778 trillion
kwh.
9 A summary of economic
activities from GTAP-E-FIT dataset can be found at
http://debreu.colorado.edu/download/gtap-eg.html
10 In
extensions of the core static model, the GTAPinGAMS framework can be
readily employed to study adjustment paths, but a description of
these techniques lies beyond the scope of the present paper. See
Rutherford, Lau and Pahlke [1998] for a pedagogic introduction to
dynamic general equilibrium analysis within the GAMS framework.
11 The
distribution files provide representations of the core model as a
constrained nonlinear system (CNS) and a square system of nonlinear
constraints within a conventional nonlinear program (NLP).
12 Under a maintained assumption of perfect
competition, Mathiesen may characterize technology as CRTS without
loss of generality. Decreasing returns are accommodated through
introduction of a specific factor, while increasing returns are
inconsistent with the assumption of perfect competition. In this
environment zero excess profit is consistent with free entry for
atomistic firms producing an identical product.
13 Model files in the GTAPinGAMS distribution
accommodate an infinite elasticity of transformation between domestic
and export markets as they are treated in the GTAP implementation in
GEMPACK. For simplicity, my algebraic exposition in this paper
focuses on the case in which the elasticity of transformation is
finite.
14 For the sake of brevity, I
present functional forms explicitly but represent unit demand and
supply functions in reduced form, e.g. airD(pirD,pirX). The next section of the paper presents detailed specific
functions in the GAMS/MCP implementation.
15 There is no
reason that this functional form should be employed in every study.
For example, when we use the GTAP dataset to study energy and
environmental issues, it is important to account for the nature of
substitution possibilities among energy carriers as well as between
energy and non-energy inputs to production; so in those applications
a nested CES function is employed in which energy trades off against
value-added with a non-zero elasticity of substitution.
16 There are some simplifications here. For example,
the regional composition of transportation services is identical
across all bilateral trade flows. Furthermore, while the dataset
incorporates explicit trade and transport margins on international
trade flows, wholesale and retail margins on domestic sales are
ignored in the dataset, so there is some asymmetry in the database's
price level.
17 The model formulation assumes that the export
tax applies on the fob price (net of transport margins), while the
import tariff applies on the cif price, gross of export tax and
transport margin.
18 Within the dataset investment inputs flow to the
cgd sector, and demand for cgd sectoral output
appears as the sole non-zero in the Iir vector for each region
r.
19 When the elasticity of
transformation between goods produced for the domestic and export
markets is infinite, the market clearance conditions for Dir and
Xir are merged, i.e.
20 I have omitted exception operators
from the variable and function declarations to make the code easier
to read. In most aggregations of the dataset, the model shown here
is operational. In highly disaggregate models, however, not all
goods are produced in all regions, and it is necessary to specify,
for example, Y(i,r)$(vdm(i,r)+vxm(i,r)).
21 The output tax is defined on a gross basis.
For example, the value of sales in the domestic market gross of tax
equals vdm(i,r) of which (1-ty(i,r))*vdm(i,r) is
returned to producers and ty(i,r)*vdm(i,r) is paid to the
government.
22 ``va:''
is a nesting identifier. These names are arbitrary and may have from one to
four characters. Two reserved names are ``s:'' which
represents the elasticity of substitution at the root of the inputs
tree and ``T:'' which represents the elasticity of
transformation at the root of the output tree.
23 Readers unfamiliar with the MPSGE model
representation may wish to refer back to the algebraic equilibrium
conditions. The specification of the $PROD:Y(I,R) block
automatically generates a zero profit condition for Yir. It
also generates terms in the market clearance equations for all
associated inputs and outputs. In this function the affected markets
include the domestic output market, the market for export of good i
from region r, markets for Armington composites entering
intermediate demand, and primary factors markets. For this reason
the tabular format is very compact - in essence, the user only needs
to specify the dual (zero-profit) conditions and the modeling
language automatically generates the primal (market clearance)
equations.
24 Note that export taxes on
sales from region s in region r are accrue to the
representative agent in region s (A:RA(s)) while
import tariffs are paid to the representative agent in region
r (A:RA(r)).
25 A .tl suffix alerts MPSGE that a
set of nests are being declared. When an input is to be associated
with one of these nests, the set label flag must be specified on the
input line.
26 In terms of
computational complexity, the cost of solving a system of equations
increases somewhere between the square and the cube of the number of
dimensions, although in large-scale implementations such as the
GAMS/MCP solver PATH or MILES, computational complexity depends on
both the number of equations and their density.
27 Of course it is mathematically equivalent to use the
cost function or an expression for cost based on the unit demand
functions, i.e. if:
28 There is a subtle
but important point with regard to the complex system of taxes in
GTAP. Users should not assume that because the dataset has a tax
instrument the associated tax rates have a strong
empirical basis. The research work in putting together GTAP has
tended to focus on trade taxes (import tariffs and export taxes), and
all other tax rates come directly from the national input-output
tables. If you undertake an analysis in which the structure of the
domestic tax system plays an important role, it is highly recommended
to collect and update the benchmark tax rates. For an example of how
a domestic tax system may be introduced in a GTAP model, see
Harrison, Rutherford and Tarr [1997].
29 In the MPSGE
model a single entry in the import activity introduces both the
import and export taxes, and given a description of taxes applying to
the producer, the modelling language automatically generates the
appropriate income entries, greatly reducing the likelihood of an
accounting error.
30 MPSGE
syntax can be found at http://debreu.colorado.edu/mainpage/mpsge.htm
31 Under a maintained assumption of perfect
competition, Mathiesen may characterize technology as CRTS without
loss of generality. Decreasing returns are accommodated through
introduction of a specific factor, while increasing returns are
inconsistent with the assumption of perfect competition. In this
environment zero excess profit is consistent with free entry for
atomistic firms producing an identical product.
32 GAMS has a special
operator used for exception handling. It is denoted as a dollar sign.
The exception operator is very useful, for example, in the cases when
we want to represent some sectors of an economy which may not be
active in a benchmark. For more information, see GAMS User's Guide.
33 The instructions for obtaining the GTAP
data can be found at http://www.agecon.purdue.edu/gtap/
34 Short directions are also
given in the file README.TXT of the GTAPinGAMS
archive.
35 The GTAP-EG build routine and the model
use the LIBINCLUDE tools located in the INCLIB directory of the GTAP-EG
distribution package. In order to be able to use the tools in your
own applications, you need to install them into GAMS directory. The
latest version of the LIBINCLUDE tools is distributed as a file
inclib.pck. To install it on your computer download the file from
http://nash.colorado.edu/tomruth/inclib/inclib.pck into your GAMS
system directory, and run GAMSINST. A description of
inclib.pck can be found at
http://nash.colorado.edu/tomruth/inclib/gams2txt.htm
36 The files from the ZIP archive can be extracted by
using WinZip.exe or unzip.exe. WinZip can be downloaded
from http://www.winzip.com
37 Make sure that you are
connected to a proper directory.
38 Make sure
that GAMS is included in the PATH variable of your computer's MS-DOS.
To check it, in MS-DOS prompt type path and press
Enter.
39 To
uncomment a pause command, delete a :(column) sign, i.e.
change a line from :pause to pause.
40 To run the GTAP-EG model ``as is'', a region
``USA'' should be present in every aggregation. Otherwise, a user
needs to change a numeraire region in the line e:pc(``usa'')
in the demand block of the GTAP-EG model.
41 SET and MAP files
are provided with the GTAPinGAMS package. An aggregation to
aspen_small.zip is done automatically if you run
aspen.bat
42 The mapping file is copied if one can be
found. This is done to assure that it is always possible to trace
the aggregation definitions for any dataset.
43 The first calculation which is
performed is a benchmark replication check in which a solver
may report ``INFEASIBLE''. This simply means that there is some
imprecision in the data, as is subsequently reported in the listing
as ``Benchmark tolerance''. Any number on the order of 1.e-4
or smaller indicates a reasonably precise dataset.
Appendix 2. Aggregation of IEA regions into GTAP format
Country IEA code Region GTAP-EG code
Australia AUS Australia AUS
New Zealand NZL New Zealand NZL
Japan JPN Japan JPN
Korea KOR Korea KOR
Indonesia IDN Indonesia IDN
Malaysia MYS Malaysia MYS
Philippines PHL Phillipines PHL
Singapore SGP Singapore SGP
Thailand THA Thailand THA
Vietnam VNM Vietnam VNM
China CHN China CHN
Hong Kong HKG Hong Kong HKG
Taiwan TWN Taiwan TWN
India IND India IND
Sri Lanka LKA Sri Lanka LKA
Bangladesh RAS_BGD Rest of South Asia RAS
Nepal RAS_NPL Rest of South Asia RAS
Pakistan RAS_PAK Rest of South Asia RAS
Canada CAN Canada CAN
USA USA USA USA
Mexico MEX Mexico MEX
Antilles CAM_ANT Central America and Carribean CAM
Costa Rica CAM_CRI Central America and Carribean CAM
Cuba CAM_CUB Central America and Carribean CAM
Dominican Republic CAM_DOM Central America and Carribean CAM
Guatemala CAM_GTM Central America and Carribean CAM
Honduras CAM_HND Central America and Carribean CAM
Haiti CAM_HTI Central America and Carribean CAM
Jamaica CAM_JAM Central America and Carribean CAM
Nicaragua CAM_NIC Central America and Carribean CAM
Panama CAM_PAN Central America and Carribean CAM
El Salavador CAM_SLV Central America and Carribean CAM
Trinidad & Tobago CAM_TTO Central America and Carribean CAM
Venezuela VEN Venezuela VEN
Columbia COL Columbia COL
Bolivia RAP_BOL Rest of Andean Pact RAP
Ecuador RAP_ECU Rest of Andean Pact RAP
Peru RAP_PER Rest of Andean Pact RAP
Argentina ARG Argentina ARG
Brazil BRA Brazil BRA
Chile CHL Chile CHL
Uruguay URY Uruguay URY
Paraguay RSM_PRY Rest of South America RSM
Great Britain GBR Great Britain GBR
Germany DEU Germany DEU
Denmark DNK Denmark DNK
Sweden SWE Sweden SWE
Finland FIN Finland FIN
Austria REU_AUT Rest of European Union REU
Belgium REU_BEL Rest of European Union REU
Spain REU_ESP Rest of European Union REU
France REU_FRA Rest of European Union REU
Giblartar REU_GIB Rest of European Union REU
Greece REU_GRC Rest of European Union REU
Ireland REU_IRL Rest of European Union REU
Italy REU_ITA Rest of European Union REU
Luxembourg REU_LUX Rest of European Union REU
Netherlands REU_NLD Rest of European Union REU
Portugal REU_PRT Rest of European Union REU
Switzerland EFT_CHE European Free Trade Area EFT
Iceland EFT_ISL European Free Trade Area EFT
Norway EFT_NOR European Free Trade Area EFT
Bulgaria CEA_BGR Central European Associates CEA
Czech Republic CEA_CZE Central European Associates CEA
Hungary CEA_HUN Central European Associates CEA
Poland CEA_POL Central European Associates CEA
Romania CEA_ROM Central European Associates CEA
Slovakia CEA_SVK Central European Associates CEA
Slovenia CEA_SVN Central European Associates CEA
Armenia FSU_ARM Former Soviet Union FSU
Azerbaijan FSU_AZE Former Soviet Union FSU
Belarus FSU_BLR Former Soviet Union FSU
Estonia FSU_EST Former Soviet Union FSU
Georgia FSU_GEO Former Soviet Union FSU
Kazakhstan FSU_KAZ Former Soviet Union FSU
Kyrgyzstan FSU_KGZ Former Soviet Union FSU
Lithuania FSU_LTU Former Soviet Union FSU
Latvia FSU_LVA Former Soviet Union FSU
Moldova FSU_MDA Former Soviet Union FSU
Russia FSU_RUS Former Soviet Union FSU
Tajikistan FSU_TJK Former Soviet Union FSU
Turkmenistan FSU_TKM Former Soviet Union FSU
Ukraine FSU_UKR Former Soviet Union FSU
Uzbekistan FSU_UZB Former Soviet Union FSU
Turkey TUR Turkey TUR
United Arab Emirates RME_ARE Rest of Middle East RME
Bahrain RME_BHR Rest of Middle East RME
Iran RME_IRN Rest of Middle East RME
Iraq RME_IRQ Rest of Middle East RME
Israel RME_ISR Rest of Middle East RME
Jordan RME_JOR Rest of Middle East RME
Kuwait RME_KWT Rest of Middle East RME
Lebanon RME_LBN Rest of Middle East RME
Oman RME_OMN Rest of Middle East RME
Qatar RME_QAT Rest of Middle East RME
Saudi Arabia RME_SAU Rest of Middle East RME
Syria RME_SYR Rest of Middle East RME
Yemen RME_YEM Rest of Middle East RME
Morocco MAR Morocco MAR
Algeria RNF_DZA Rest of North Africa RNF
Egypt RNF_EGY Rest of North Africa RNF
Libya RNF_LBY Rest of North Africa RNF
Tunisia RNF_TUN Rest of North Africa RNF
South Africa CU SAF South Africa SAF
Angola RSA_AGO Rest of South Africa RSA
Mozambique RSA_MOZ Rest of South Africa RSA
Tanzania RSA_TZA Rest of South Africa RSA
Zambia RSA_ZMB Rest of South Africa RSA
Zimbabwe RSA_ZWE Rest of South Africa RSA
Benin RSS_BEN Rest of South-Saharan Africa RSS
Cote d'Ivoire RSS_CIV Rest of South-Saharan Africa RSS
Cameroon RSS_CMR Rest of South-Saharan Africa RSS
Congo RSS_COG Rest of South-Saharan Africa RSS
Ethiopia RSS_ETH Rest of South-Saharan Africa RSS
Gabon RSS_GAB Rest of South-Saharan Africa RSS
Ghana RSS_GHA Rest of South-Saharan Africa RSS
Kenya RSS_KEN Rest of South-Saharan Africa RSS
Nigeria RSS_NGA Rest of South-Saharan Africa RSS
Sudan RSS_SDN Rest of South-Saharan Africa RSS
Senegal RSS_SEN Rest of South-Saharan Africa RSS
Zaire RSS_ZAR Rest of South-Saharan Africa RSS
Albania ROW_ALB Rest of World ROW
Bosnia ROW_BIH Rest of World ROW
Brunei ROW_BRN Rest of World ROW
Cyprus ROW_CYP Rest of World ROW
Croatia ROW_HRV Rest of World ROW
Macedonia ROW_MKD Rest of World ROW
Malta ROW_MLT Rest of World ROW
Myanmar ROW_MMR Rest of World ROW
Papua New Guinea ROW_PNG Rest of World ROW
North Korea ROW_PRK Rest of World ROW
Serbia ROW_SER Rest of World ROW
Other Africa OTHERAFRIC Rest of World ROW
Other Asia OTHERASIA Rest of World ROW
Other Latin America OTHERLATIN Rest of World ROW Appendix 3. An aggregation of production sectors into
GTAP-EG format
IEA code Sector GTAP-EG sector
COL Coal COL
AGR agriculture AGR
CNS Construction CNS
CRP Chemical and Petrochemical CRP
DWE Dwellings DWE and final consumption
ELY Electricity ELE
EXPORTS Exports goes to export data
FPR Food and Tobacco FRP
GAS Gas GAS
HEAT Heat Not used
I_S Iron and steel I_S
IMPORTS Imports goes to import data
INDPROD Indigenous production Not used
LUM Wood products LUM
NEINTREN Non energy use in industry CRP
NEOTHER Non-energy use in other sectors AGR
NETRANS Non-energy use in transport T_T
NFM Non ferrous metals NFM
NMM Non metallic minerals NMM
NONROAD Other (non road) transport T_T
OIL Oil CRU
OME Machinery OME
OMF Other manufacturing OMF
OMN Mining OMN
OWNUSE Ownuse Not used
P_C Petroleum OIL
PPP Paper, Pulp, and Print PPP
RENEW Renewable Not used
ROAD Road Part to T_T and part to final consumption
SER Services SER
TRN Transport equipment TRN
TWL Textile and leather TWL
GTAP 4 GTAP-EG Sector
GDT, GAS GAS Natural gas works
ELY ELE Electricity and heat
P_C OIL Refined oil products
COL COL Coal transformation
OIL CRU Crude oil
I_S I_S Iron and steel industry
CRP CRP Chemical industry
NFM NFM Non-ferrous metals
NMM NMM Non-metallic minerals
MVH, OTN TRN Transport equipment
ELE, OME, FMP OME Other machinery
OMN OMN Mining
OMT, VOL, MIL, PCR, SGR, OFD, B_T, CMT FPR Food products
PPP PPP Paper-pulp-print
LUM LUM Wood and wood-products
CNS CNS Construction
TEX, WAP, LEA TWL Textiles-wearing apparel-leather
OMF, WTR OMF Other manufacturing
PDR, WHT, GRO, V_F, OSD, C_B, PFB,
OCR, CTL, OAP, RMK, WOL, FRS, FSH AGR Agricultural products
T_T T_T Trade and transport
OSP, OSG SER Commercial and public services
DWE DWE Dwellings
CGD CGD Investment composite Appendix 4. GTAP-EG: Basic statistics
Table A.4.1. Economic activity by sector
---------------------------------------------------
gdp gdp% trade trade%
---------------------------------------------------
DWE 104.0 4.1
ELE 93.8 3.7
CNS 159.9 6.3 2.2 0.4
COL 12.0 0.5 2.3 0.4
GAS 14.6 0.6 3.2 0.5
NMM 21.0 0.8 7.3 1.2
OIL 18.4 0.7 8.5 1.4
OMN 5.8 0.2 9.1 1.5
LUM 19.1 0.7 11.0 1.8
NFM 5.5 0.2 11.3 1.8
OMF 25.5 1.0 15.3 2.5
PPP 41.6 1.6 16.1 2.6
I_S 20.6 0.8 18.5 3.0
CRU 37.1 1.5 21.3 3.4
AGR 120.3 4.7 25.9 4.2
FPR 76.0 3.0 35.1 5.6
TWL 44.2 1.7 46.4 7.5
SER 892.3 35.0 46.4 7.5
T_T 505.5 19.8 53.3 8.6
TRN 55.0 2.2 58.0 9.3
CRP 84.4 3.3 64.1 10.3
OME 190.9 7.5 165.8 26.7
---------------------------------------------------
Table A.4.2. Economic activity by region
---------------------------------------------------
gdp gdp% trade trade%
---------------------------------------------------
RSM 0.4 0.0 0.4 0.1
URY 1.4 0.1 0.4 0.1
LKA 1.2 0.0 0.5 0.1
VNM 1.2 0.0 0.7 0.1
MAR 2.6 0.1 1.0 0.2
COL 6.9 0.3 1.5 0.2
RSA 1.6 0.1 1.5 0.2
RAP 7.4 0.3 1.6 0.3
RAS 6.9 0.3 1.7 0.3
CHL 5.5 0.2 2.0 0.3
VEN 6.8 0.3 2.0 0.3
NZL 5.1 0.2 2.2 0.3
PHL 5.9 0.2 2.8 0.4
ARG 24.9 1.0 2.9 0.5
ROW 22.0 0.9 3.3 0.5
SAF 12.7 0.5 3.5 0.6
TUR 15.6 0.6 3.8 0.6
RNF 10.7 0.4 3.9 0.6
RSS 13.6 0.5 4.3 0.7
CAM 7.2 0.3 4.4 0.7
IND 27.7 1.1 4.4 0.7
FIN 11.6 0.5 4.9 0.8
IDN 19.6 0.8 5.7 0.9
BRA 62.9 2.5 6.2 1.0
DNK 15.5 0.6 6.4 1.0
AUS 31.8 1.2 7.2 1.2
THA 14.9 0.6 7.5 1.2
HKG 9.9 0.4 8.2 1.3
MEX 25.2 1.0 8.9 1.4
SWE 19.3 0.8 9.2 1.5
MYS 7.1 0.3 9.3 1.5
FSU 44.8 1.8 11.4 1.8
CEA 27.8 1.1 11.7 1.9
SGP 6.0 0.2 13.3 2.1
TWN 24.6 1.0 15.1 2.4
RME 39.8 1.6 15.8 2.5
KOR 39.7 1.6 16.0 2.6
EFT 40.8 1.6 16.6 2.7
CAN 49.7 2.0 21.1 3.4
CHN 55.5 2.2 23.7 3.8
GBR 101.3 4.0 29.6 4.8
JPN 463.1 18.2 54.3 8.7
DEU 222.1 8.7 58.6 9.4
USA 655.8 25.7 79.5 12.8
REU 372.0 14.6 132.2 21.3
---------------------------------------------------
Table A.4.3. Carbon inventories -- mton
------------------------------------------------------------------------
total ind_nele fd_nele electric ind_total fd_total kg/$
AUS 78.0 33.2 9.8 35.0 60.8 17.1 0.2
NZL 8.8 6.8 1.2 0.8 7.4 1.4 0.2
JPN 342.8 198.3 54.8 89.7 269.7 73.0 0.1
KOR 122.4 83.5 18.0 20.9 101.4 21.0 0.3
IDN 64.0 40.3 12.3 11.5 48.8 15.2 0.3
MYS 23.1 12.8 3.7 6.6 18.4 4.6 0.3
PHL 12.2 7.2 1.9 3.1 9.7 2.5 0.2
SGP 23.2 16.8 0.8 5.6 21.6 1.6 0.4
THA 38.4 18.2 8.2 12.0 28.1 10.3 0.3
VNM 5.4 4.0 0.6 0.8 4.6 0.8 0.5
CHN 848.8 534.0 78.5 236.4 745.1 103.7 1.6
HKG 13.8 7.5 0.4 5.8 12.2 1.6 0.1
TWN 49.8 28.9 4.8 16.1 42.1 7.7 0.2
IND 210.9 88.1 26.4 96.4 172.4 38.5 0.8
LKA 2.1 1.7 0.3 0 1.7 0.3 0.2
RAS 27.4 14.8 5.5 7.1 20.3 7.1 0.4
CAN 138.1 83.9 28.6 25.6 104.1 34.0 0.3
USA 1489.2 613.2 337.1 539.0 1014.5 474.8 0.2
MEX 89.6 54.5 16.3 18.8 70.1 19.5 0.4
CAM 27.2 17.5 2.7 7.0 23.5 3.8 0.4
VEN 33.1 22.2 5.8 5.1 26.4 6.7 0.5
COL 17.8 10.8 4.1 2.9 12.9 4.8 0.3
RAP 13.8 9.8 2.5 1.5 11.0 2.7 0.2
ARG 33.4 15.6 12.2 5.6 20.0 13.4 0.1
BRA 78.9 61.5 14.1 3.3 64.2 14.7 0.1
CHL 11.3 6.9 2.6 1.9 8.5 2.8 0.2
URY 1.6 1.2 0.3 0 1.3 0.3 0.1
RSM 0.9 0.4 0.5 0 0.4 0.5 0.2
GBR 165.6 84.9 37.4 43.3 117.9 47.7 0.2
DEU 265.4 118.4 64.4 82.6 184.2 81.2 0.1
DNK 18.6 7.7 2.7 8.2 13.9 4.7 0.1
SWE 17.5 11.1 4.4 2.1 12.6 4.9 0.1
FIN 16.2 8.4 2.4 5.4 12.7 3.5 0.1
REU 473.1 267.7 106.9 98.5 346.6 126.4 0.1
EFT 25.3 17.5 7.4 0.3 17.8 7.5 0.1
CEA 208.1 91.3 25.0 91.8 167.2 40.9 0.8
FSU 695.1 324.6 72.3 298.2 576.6 118.5 1.7
TUR 45.9 27.5 7.1 11.3 37.0 8.9 0.3
RME 225.6 133.4 39.4 52.8 175.2 50.4 0.6
MAR 7.3 3.7 1.0 2.7 5.7 1.6 0.3
RNF 56.5 32.3 9.2 15.1 44.5 12.1 0.5
SAF 96.0 44.1 10.9 41.0 79.8 16.2 0.8
RSA 7.2 4.5 0.6 2.1 6.3 0.9 0.5
RSS 22.7 16.0 4.4 2.3 17.9 4.8 0.2
ROW 56.8 32.0 5.6 19.2 47.2 9.6 0.3
total 6208.5 3218.4 1054.9 1935.1 4784.3 1424.1
------------------------------------------------------------------------
Table A.4.4. Carbon emissions as a percentage of global carbon emissions
-------------------------------------
ANNEX B
as % of as % of
annex total
AUS 1.978 1.256
NZL 0.222 0.141
JPN 8.696 5.521
CAN 3.503 2.224
USA 37.782 23.987
GBR 4.202 2.668
DEU 6.732 4.274
DNK 0.471 0.299
SWE 0.445 0.282
FIN 0.411 0.261
REU 12.002 7.620
EFT 0.642 0.407
CEA 5.279 3.352
FSU 17.636 11.197
annex b 100.000 63.488
NON-ANNEX B
as % as % of
of non-annex total
KOR 5.398 1.971
IDN 2.824 1.031
MYS 1.018 0.372
PHL 0.539 0.197
SGP 1.023 0.374
THA 1.694 0.618
VNM 0.237 0.086
CHN 37.446 13.672
HKG 0.607 0.222
TWN 2.195 0.801
IND 9.303 3.397
LKA 0.091 0.033
RAS 1.207 0.441
MEX 3.951 1.442
CAM 1.202 0.439
VEN 1.460 0.533
COL 0.784 0.286
RAP 0.608 0.222
ARG 1.471 0.537
BRA 3.479 1.270
CHL 0.501 0.183
URY 0.070 0.025
RSM 0.039 0.014
TUR 2.024 0.739
RME 9.954 3.634
MAR 0.322 0.118
RNF 2.495 0.911
SAF 4.235 1.546
RSA 0.316 0.115
RSS 1.003 0.366
ROW 2.504 0.914
non-annex b 100.000 36.512
-------------------------------------
Table A.4.5. Carbon dioxide emissions - billion of tonnes
IEA book IEA stat GTAP-E-FIT EG with GTAP-EG
no fix
AUS 0.286 0.286 0.283 0.286 0.286
NZL 0.029 0.032 0.033 0.032 0.032
JPN 1.151 1.208 1.145 1.257 1.257
KOR 0.353 0.449 0.396 0.449 0.449
IDN 0.227 0.235 0.212 0.235 0.235
MYS 0.092 0.085 0.084 0.085 0.085
PHL 0.050 0.045 0.044 0.045 0.045
SGP 0.059 0.085 0.085 0.085 0.085
THA 0.156 0.140 0.140 0.141 0.141
VNM 0.022 0.020 0.021 0.020 0.020
CHN 3.007 3.098 2.902 3.112 3.112
HKG 0.044 0.052 0.052 0.050 0.050
TWN 0.167 0.182 0.179 0.182 0.182
IND 0.803 0.771 0.765 0.773 0.773
LKA 0.006 0.008 0.007 0.008 0.008
RAS 0.211 0.100 0.097 0.100 0.100
CAN 0.471 0.505 0.472 0.506 0.506
USA 5.228 5.339 5.175 5.340 5.460
MEX 0.328 0.328 0.309 0.328 0.328
CAM 0.111 0.097 0.100 0.100 0.100
VEN 0.113 0.114 0.112 0.121 0.121
COL 0.065 0.063 0.062 0.065 0.065
RAP 0.052 0.050 0.047 0.051 0.051
ARG 0.128 0.121 0.115 0.122 0.122
BRA 0.287 0.269 0.256 0.289 0.289
CHL 0.042 0.042 0.039 0.042 0.042
URY 0.005 0.006 0.006 0.006 0.006
RSM 0.003 0.003 0.004 0.003 0.003
GBR 0.565 0.605 0.540 0.607 0.607
DEU 0.884 0.973 0.865 0.973 0.973
DNK 0.060 0.067 0.063 0.068 0.068
SWE 0.056 0.064 0.061 0.064 0.064
FIN 0.054 0.059 0.057 0.059 0.059
REU 1.560 1.734 1.628 1.735 1.735
EFT 0.078 0.093 0.082 0.093 0.093
CEA 0.749 0.762 0.707 0.763 0.763
FSU 2.483 2.542 2.341 2.549 2.549
TUR 0.160 0.168 0.156 0.168 0.168
RME 0.817 0.788 0.755 0.827 0.827
MAR 0.026 0.027 0.026 0.027 0.027
RNF 0.213 0.204 0.201 0.207 0.207
SAF 0.321 0.347 0.337 0.352 0.352
RSA 0.025 0.026 0.026 0.026 0.026
RSS 0.081 0.083 0.103 0.083 0.083
ROW 0.518 0.208 0.183 0.208 0.208
total 22.150 22.482 21.272 22.644 22.764
Appendix 5. MPSGE formulation of the GTAP-EG model
* Final demand
$prod:c(r) s:0.5 c:1 e:1 oil(e):0 col(e):0 gas(e):0
o:pc(r) q:ct0(r)
i:pa(i,r) q:c0(i,r) p:pc0(i,r) i.tl:$fe(i) c:$(not e(i)) e:$ele(i) a:ra(r) t:tc(i,r)
i:pcarb(r)#(fe) q:carbcoef(fe,"final",r) p:1e-6 fe.tl:
* Non-fossil fuel production (includes electricity and refining):
$prod:y(i,r)$nr(i,r) s:0 vae(s):0.5 va(vae):1
+ e(vae):0.1 nel(e):0.5 lqd(nel):2
+ oil(lqd):0 col(nel):0 gas(lqd):0
o:py(i,r) q:vom(i,r) a:ra(r) t:ty(i,r)
i:pa(j,r)$(not fe(j)) q:vafm(j,i,r) p:pai0(j,i,r) e:$ele(j) a:ra(r) t:ti(j,i,r)
i:pl(r) q:ld0(i,r) va:
i:rkr(r)$rsk q:kd0(i,r) va:
i:rkg$gk q:kd0(i,r) va:
i:pcarb(r)#(fe) q:carbcoef(fe,i,r) p:1e-6 fe.tl:
i:pa(fe,r) q:vafm(fe,i,r) p:pai0(fe,i,r) fe.tl: a:ra(r) t:ti(fe,i,r)
* Fossil fuel production activity (crude, gas and coal):
$prod:y(xe,r)$vom(xe,r) s:(esub_es(xe,r)) id:0
o:py(xe,r) q:vom(xe,r) a:ra(r) t:ty(xe,r)
i:pa(j,r) q:vafm(j,xe,r) p:pai0(j,xe,r) a:ra(r) t:ti(j,xe,r) id:
i:pl(r) q:ld0(xe,r) id:
i:pr(xe,r) q:rd0(xe,r)
* Armington aggregation over domestic versus imports:
$prod:a(i,r)$a0(i,r) s:4 m:8 s.tl(m):0
o:pa(i,r) q:a0(i,r)
i:py(i,r) q:d0(i,r)
i:py(i,s) q:vxmd(i,s,r) p:pmx0(i,s,r) s.tl:
+ a:ra(s) t:tx(i,s,r) a:ra(r) t:(tm(i,s,r)*(1+tx(i,s,r)))
i:pt#(s) q:vtwr(i,s,r) p:pmt0(i,s,r) s.tl: a:ra(r) t:tm(i,s,r)
* International transport services (Cobb-Douglas):
$prod:yt s:1
o:pt q:(sum((i,r), vst(i,r)))
i:py(i,r) q:vst(i,r)
* Final demand:
$demand:ra(r)
d:pc(r) q:ct0(r)
e:py("cgd",r) q:-vom("cgd",r)
e:rkr(r)$rsk q:(sum(i, kd0(i,r)))
e:rkg$gk q:(sum(i, kd0(i,r)))
e:pl(r) q:evoa("lab",r)
e:pr(xe,r) q:rd0(xe,r)
e:pc("usa") q:vb(r)
e:pcarb(r) q:carblim(r)
Appendix 6. ASPEN_SMALL.SET
$TITLE Set Definitions for 13 regions and 8 goods
SET I Sectors/
Y Other manufactures and services
EIS Energy-intensive sectors
COL Coal
OIL Petroleum and coal products (refined)
CRU Crude oil
GAS Natural gas
ELE Electricity
CGD Savings good/;
SET R Aggregated Regions /
USA United States
CAN Canada
EUR Europe
JPN Japan
OOE Other OECD
FSU Former Soviet Union
CEA Central European Associates
CHN China (including Hong Kong + Taiwan)
IND India
BRA Brazil
ASI Other Asia
MPC Mexico + OPEC
ROW Rest of world /
Set F Aggregated factors /
LAB Labor,
CAP Capital /;
Appendix 7. ASPEN_SMALL.MAP
$title Map file
* Aggregating ASPEN dataset (45x23) into ASPEN_SMALL dataset (13x8)
* --------------------------------------------------------------
* The target dataset has fewer sectors, so we need to specify how
* each sector in the source dataset is mapped to a sector in the
* target dataset:
$SETGLOBAL source aspen
Set mapi Sectors and goods /
GAS.GAS Natural gas works
ELE.ELE Electricity and heat
OIL.OIL Refined oil products
COL.COL Coal transformation
CRU.CRU Crude oil
I_S.EIS Iron and steel industry (IRONSTL)
CRP.EIS Chemical industry (CHEMICAL)
NFM.EIS Non-ferrous metals (NONFERR)
NMM.EIS Non-metallic minerals (NONMET)
TRN.EIS Transport equipment (TRANSEQ)
PPP.EIS Paper-pulp-print (PAPERPRO)
T_T.Y Trade margins
AGR.Y Agricultural products
OME.Y Other machinery (MACHINE)
OMN.Y Mining (MINING)
FPR.Y Food products (FOODPRO)
LUM.Y Wood and wood-products (WOODPRO)
CNS.Y Construction (CONSTRUC)
TWL.Y Textiles-wearing apparel-leather (TEXTILES)
OMF.Y Other manufacturing (INONSPEC)
SER.Y Commercial and public services
DWE.Y Dwellings,
CGD.CGD Investment composite /;
SET MAPR mapping GTAP regions /
AUS.OOE Australia
NZL.OOE New Zealand
JPN.JPN Japan
KOR.ASI Republic of Korea
IDN.MPC Indonesia
MYS.ASI Malaysia
PHL.ASI Philippines
SGP.ASI Singapore
THA.ASI Thailand
VNM.ASI Vietnam
CHN.CHN China
HKG.CHN Hong Kong
TWN.CHN Taiwan
IND.IND India
LKA.ASI Sri Lanka
RAS.ASI Rest of South Asia
CAN.CAN Canada
USA.USA United States of America
MEX.MPC Mexico
CAM.ROW Central America and Caribbean
VEN.ROW Venezuela
COL.ROW Columbia
RAP.ROW Rest of Andean Pact
ARG.ROW Argentina
BRA.BRA Brazil
CHL.ROW Chile
URY.ROW Uruguay
RSM.ROW Rest of South America
GBR.EUR United Kingdom
DEU.EUR Germany
DNK.EUR Denmark
SWE.EUR Sweden
FIN.EUR Finland
REU.EUR Rest of EU,
EFT.EUR European Free Trade Area
CEA.CEA Central European Associates
FSU.FSU Former Soviet Union
TUR.ROW Turkey
RME.MPC Rest of Middle East
MAR.ROW Morocco
RNF.MPC Rest of North Africa
SAF.ROW South Africa
RSA.ROW Rest of South Africa
RSS.ROW Rest of South-Saharan Africa
ROW.ROW Rest of World /;
* The following statements illustrate how to aggregate
* factors of production in the model. Unlike the aggregation
* of sectors or regions, you need to declare the set of
* primary in the source as set FF, then you can specify the
* mapping from the source to the target sets.
set ff /LND,SKL,LAB,CAP,RES/;
SET MAPF mapping of primary factors /LND.CAP,SKL.LAB,LAB.LAB,CAP.CAP,RES.CAP/;
References
Footnotes:
Yir = DIir + DGir + DCir + Iir +
å
s
Mirs + TDir.
c(p) º minx
å
pi xi s.t. f(x) = 1
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Last Updated 01/20/01
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