GTAPinGAMS and GTAP-EG: Global Datasets for Economic Research and Illustrative Models - Appendix, References, and Footnotes


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Appendix 1. GTAP Identifiers

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 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

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.

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

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.

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

Appendix 5 presents the function declarations for GTAP-EG model implemented in MPSGE.

*       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

[]
Armington, P. (1969). A Theory of Demand for Products Distinguished by Place of Production. IMF Staff Papers, 16, 159-178.

[]
Babiker M.H. and T.F. Rutherford, ``Input-output and general equilibrium estimates of embodied carbon: A data set and static framework for assessment'', Working Paper 97-2, University of Colorado, Boulder. 1997.

[]
Brooke A., D. Kendrick, and A. Meeraus, GAMS: A User's Guide, Release 2.25, Scientific Press, 1992.

[]
Bruce J.P., H. Lee, and E.F.Haites (eds.), Climate Change 1995: Economic and Social Dimensions of Climate Change, Cambridge University Press, 1996.

[]
Complainville C. and D. van der Mensbrugghe, ``Construction of an Energy Database for GTAP V4: Concordance with IEA Energy Statistics'', OECD Development Centre, 1998.

[]
Ferris, M.C. and T.F. Rutherford, ``Matrix Balancing: A Practical Approach'', University of Colorado Working Paper, 1998.

[]
Harrison, W.J. and K.R. Pearson, ``Computing solutions for large general equilibrium models using GEMPACK'', Computational Economics, 9:83-127, 1996.

[]
Harrison, G.W., T.F. Rutherford, and D. Tarr, ``Opciones de política comercial para Chile: una evaluación cuantitiva'' Cuadernos de Economía, August 1997. A related working paper in English is available on the web, (see http://theweb.badm.sc.edu/glenn/addreg.pdf).

[]
Hertel, T.W. (ed.) Global Trade Analysis: Modeling and Applications, Cambridge University Press, Cambridge and New York, 1997.

[]
International Energy Agency. CO2 Emissions from Fuel Combustion. A New Basis for Comparing Emissions of a Major Greenhouse Gas. OECD/IEA, Paris, 1997.

[]
Malcolm G. and T.P.Truong, ``The Process of Incorporating Energy Data into GTAP'', GTAP Technical Paper, 1999.

[]
Mathiesen, L. ``Computation of Economic Equilibrium by a Sequence of Linear Complementarity Problems'', Mathematical Programming Study 23, North-Holland, 1985, pp. 144-162.

[]
McDougall, R. ``The GTAP Database'', Draft documentation. See the GTAP 4 release (http://www.agecon.purdue.edu/gtap/database).

[]
Rutherford, T.F. ``Applied General Equilibrium Modeling with MPSGE as a GAMS Subsystem: An overview of the Modeling Framework and Syntax'', Computational Economics, V.14, Nos. 1-2, 1999.

[]
Rutherford, T.F. and S.V.Paltsev, ``GTAP-EG: Incorporating energy statistics into GTAP format'', University of Colorado Department of Economics, 2000.

[]
Rutherford, T.F., M. Lau and A. Pahlke, ``A Primer in Dynamic General Equilibrium Analysis'', Working Paper, University of Colorado Department of Economics, 1998.

[]
Rutherford, T.F., and D. Tarr, ``Regional Trading Arrangements for Chile: Do the Results Differ with a Dynamic Model?'', University of Colorado Department of Economics, 1998.

[]
United Nations, Input-Output Tables and Analysis, Studies in Methods, Series F, No. 4, Reve.1, New York, 1973.


Footnotes:

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 ../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.

Yir = DIir + DGir + DCir + Iir +
å
s 
Mirs + TDir.
and prices pirD and pirX are replaced throughout the model by a single price index, pirY.

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:

c(p) º minx å
pi xi    s.t. f(x) = 1
then c(p) = åi pi xi*(p) where xi*(p) is the unit demand function.

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 ../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.


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