Govinda Timilsina The World Bank, Washington, DC Skopje, Macedonia March 01, 2011 Global and Country Specific CGE Models at the World Bank for Climate.
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Govinda Timilsina
The World Bank, Washington, DC
Skopje, Macedonia
March 01, 2011
Global and Country Specific CGE Models at the World Bank for Climate Change Analysis
Presentation Outline
Introduction Global CGE Model Data for Global CGE Model Single Country CGE Model Data for Single Country CGE Model
Introduction
• Costs of climate change (impacts and mitigation) are carried out at activity or sector level and such analysis does not account the inter-sectoral linkages. Due to inter-linkages between productive sectors; between economic agents and international trade, an activity, if implemented at a large scale, could have economy wide effects;
• The impacts or costs measured at the activity or project level could be significantly different from those measured at the economy-wide levels
• A GHG mitigation technology, for example, attractive from activity or sectoral approach may not necessarily be attractive if its impacts to the overall economy are accounted for (or the rankings of GHG mitigation options could change)
Global CGE Model – Key Characteristics
• Multi-sector, multi-region, global recursive dynamic CGE model
• The model is flexible enough to accommodate new regions/countries or sectors and is calibrated with GTAP database
• Nested CES and CET functional forms to represent production behavior and land supply, respectively
• Nonhomothetic Constant Difference of Elasticities (CDE) function form for households
• Detailed representation of land-use biofuel sectors
• Representation of bilateral and international trade
Global CGE Model Structure – Production Sector
Nested CES structure of the model for production sectors
CGE Model – Production Sector (Cont.)
𝐦𝐢𝐧𝑽𝑨𝑬𝒊,𝒓,𝒕,𝒗, 𝑵𝑫𝒊,𝒓,𝒕 𝑷𝑽𝑨𝒊,𝒓,𝒕,𝒗𝑽𝑨𝑬𝒊,𝒓,𝒕,𝒗+𝑷𝑵𝑫𝒊,𝒓,𝒕𝑵𝑫𝒊,𝒓,𝒕
𝒔.𝒕. ൫𝜶𝒊,𝒓,𝒗𝑽𝑨𝑬൯ 𝟏𝝈𝒊,𝒓,𝒗𝑽𝑨𝑬𝑵𝑫൫𝝀𝒊,𝒓,𝒕,𝒗𝑽𝑨𝑬 𝑽𝑨𝑬𝒊,𝒓,𝒕,𝒗൯
𝝈𝒊,𝒓,𝒗𝑽𝑨𝑬𝑵𝑫−𝟏𝝈𝒊,𝒓,𝒗𝑽𝑨𝑬𝑵𝑫
+ ൫𝜶𝒊,𝒓,𝒗𝑵𝑫൯𝟏𝝈𝒊,𝒓,𝒗𝑽𝑨𝑬𝑵𝑫
൫𝝀𝒊,𝒓,𝒕,𝒗𝑵𝑫 𝑵𝑫𝒊,𝒓,𝒕൯𝝈𝒊,𝒓,𝒗𝑽𝑨𝑬𝑵𝑫−𝟏𝝈𝒊,𝒓,𝒗𝑽𝑨𝑬𝑵𝑫
𝒗 𝝈𝒊,𝒓,𝒗𝑽𝑨𝑬𝑵𝑫𝝈𝒊,𝒓,𝒗𝑽𝑨𝑬𝑵𝑫−𝟏 ≥ 𝑿𝒗𝒊,𝒓,𝒕,𝒗
where X is gross output. VAE and ND correspond to the share parameters for VAE and ND, respectively, and VAEND is the elasticity of substitution between VAE and ND. VAE and ND are the productivity parameters that represent the state of the technology. The indices i, r, v and t correspond to sector, country/region, capital vintage and time, respectively.
where VAE is the value added and energy bundle, ND is the non energy bundle. PVA and PND are the prices of VAE and ND, respectively.
Cost minimization formulation
CGE Model – Production Sector (Cont.)
• Derivation of demand and price variables
𝑽𝑨𝑬𝒊,𝒓,𝒕,𝒗 = 𝜶𝒊,𝒓,𝒗𝑽𝑨𝑬𝝀𝒊,𝒓,𝒕,𝒗𝑽𝑨𝑬 𝝈𝒊,𝒓,𝒗𝑽𝑨𝑬𝑵𝑫−𝟏ቆ
𝑷𝑿𝒗𝐢,𝐫,𝐭,𝐯𝑷𝑽𝑨𝒊,𝒓,𝒕,𝒗ቇ𝝈𝒊,𝒓,𝒗𝑽𝑨𝑬𝑵𝑫𝑿𝒗𝒊,𝒓,𝒕,𝒗
𝑵𝑫𝒊,𝒓,𝒕 = 𝜶𝒊,𝒓,𝒗𝑵𝑫 𝝀𝒊,𝒓,𝒕,𝒗𝑵𝑫 𝝈𝒊,𝒓,𝒗𝑽𝑨𝑬𝑵𝑫−𝟏ቆ
𝑷𝑿𝒗𝐢,𝐫,𝐭,𝐯𝑷𝑵𝑫𝒊,𝒓,𝒕,𝒗ቇ𝝈𝒊,𝒓,𝒗𝑽𝑨𝑬𝑵𝑫𝑿𝒗𝒊,𝒓,𝒕,𝒗𝒗
𝑷𝑿𝒗𝐢,𝐫,𝐭,𝐯= 𝜶𝒊,𝒓,𝒗𝑽𝑨𝑬ቆ𝑷𝑽𝑨𝒊,𝒓,𝒕,𝒗𝝀𝒊,𝒓,𝒕,𝒗𝑽𝑨𝑬 ቇ
𝟏−𝝈𝒊,𝒓,𝒗𝑽𝑨𝑬𝑵𝑫 + 𝜶𝒊,𝒓,𝒗𝑵𝑫 ቆ𝑷𝑵𝑫𝒊,𝒓,𝒕,𝒗𝝀𝒊,𝒓,𝒕,𝒗𝑵𝑫 ቇ
𝟏−𝝈𝒊,𝒓,𝒗𝑽𝑨𝑬𝑵𝑫𝟏𝟏− 𝝈𝒊,𝒓,𝒗𝑽𝑨𝑬𝑵𝑫
• In the similar manner, all demand and price variables were derived
Global CGE Model – Land Use (Cont.)
AEZ Climate type Humidity levelAEZ1 Arid
AEZ2 Dry semi-aridAEZ3 Moist semi-aridAEZ4 Sub-HumidAEZ5 HumidAEZ6 Humid > 300-days LGP*AEZ7 Arid
AEZ8 Dry semi-aridAEZ9 Moist semi-aridAEZ10 Sub-HumidAEZ11 HumidAEZ12 Humid > 300-days LGPAEZ13 Arid
AEZ14 Dry semi-aridAEZ15 Moist semi-aridAEZ16 Sub-HumidAEZ17 HumidAEZ18 Humid > 300-days LGP
Tropical
Temperate
Boreal
*LGP stands for Length of growing period
• Land is split into 18 Agro-Ecological Zones (AEZs)
Model Dynamics and Closure
• Medium variant of UN population forecasts
• Per capita GDP growth is exogenous (World Bank projections)
• Resource prices (e.g., oil price forecasts) are exogenous
• Annual sector specific productivity growth (2.1% for agriculture, 1% for service and 2% for manufacturing)
• Autonomous energy efficiency improvement (1% )
• Long-term sustainability Government deficit and capital account are fixed
Data & Parameters
• Data are coming from the GTAP (Global Trade Analysis Project) database (Purdue University, Indiana)
• The database provides SAMs and international trade (bilateral flows, trade barriers)
• Database version 7.1– Year 2004– 112 countries/regions– 57 sectors
GTAP 7.1 – Geographic disaggregation
RUS
CHN
CAN
USA
BRA
AUS
XWF
XNF
KAZ
XEC
XWS
XAC
IND
ARG
XCF
XEA
MEX
IRN
IDN
ZAF
XSU
PER
ETH
COL
BOL
EGYPAK
XSA
TUR
TZA
NGAVEN
UKR
XSC
MOZ
FRA
ZMB
MAR
SWE
MMR
XNA
CHL
ESP
MDG
BWA
DEU
FIN
THA
POL
JPN
NOR
XSM
XOC
PRY
ZWE
ITA
GBR
NZL
NZL
BLR
VNM
ROU
MYSMYS
PHL
PHL
KGZ
ECU
XER
LAO
UGA
XEF
XCB
XCB
URY
SEN
TUN
KHMXCA
BGR
BGD
GRC
HUN
NIC
AUT
MWI
CZE
LVA
KOR
PRT
IRLLTU
GTM
AZEGEO
EST
PAN
SVK
HRV
DNK
LKA
CHE
NLD
CRI
XEE
BEL
TWN
ARMALB
SVN
XSE
CYP
LUX
MUS
HKG
SGP
MLT
Orange – individual countries; red- combined a regions
GTAP 7.1 – Sectoral Disaggregation
Paddy rice Coal Wood. Prod. ElectricityWheat Oil Paper etc. Gas Dist.Oth. Cereals Gas Ref. oil etc. WaterVeg. & Fruits Oth. Minerals Chemicals etc. ConstructionOil seeds Red meat Oth. Min. Prod. TradeSug. Cane & Beet White meat Ferr. Met. Land trns.Plant-based fibers Veg. Oils Oth. Met. Sea trns.Oth. Crops Dairy Prod. Met. Prod. Air trns.Beef etc. Proc. Rice Mot. Veh. & parts CommunicationPoultry, Pork, etc. Ref. sugar Oth. Trp. Eqpt. Fin. Serv.Raw milk Oth. Food Electronic Eqpt. InsuranceWool etc. Bev. & Tob. Oth. Mach. & Eqpt. Oth. Bus. Serv.Forestry Textiles Oth. Manu. Recr. & Oth. Serv.
Fishing Clothing Publ. Serv.Leath. Prod. Dwellings
Regional and sector decomposition
• Computational limitations require aggregation of countries/regions and sectors
(GTAP: 112 regions & 57 sectors
or 112* 57 = 6,384 equations for 1
variable only defined on 2 dimensions)
• Focus on main countries/regions producer of biofuels
• Keep as much detail as possible for agriculture (especially biofuel feedstocks) and for energy sectors
1 Paddy rice2 Sugar (cane & beet)3 Vegetables, fruit4 Wheat5 Corn6 Other cereal grains7 Oilseeds8 Livestock9 Sugar Ethanol
10 Corn Ethanol11 Grains Ethanol12 Biodiesel13 Processed food14 Forestry15 Coal16 Crude oil17 Natural gas18 Other mining19 Gasoline20 Diesel21 Refined oil22 Chemicals23 Other manufacturing24 Electricity25 Gas distribution26 Construction27 Transport services28 Other services
1 Australia and New Zealand2 Japan3 Canada4 United States5 France6 Germany7 Italy8 Spain9 UK
10 Rest of EU & EFTA11 China12 Indonesia13 Malaysia14 Thailand15 Rest of East Asia & Pacific16 India17 Rest of South Asia18 Argentina19 Brazil20 Rest of LAC21 Russia22 Rest of ECA23 MENA24 South Africa25 Rest of Sub-Saharan Africa
Key Elasticity Parameters
• Elasticity parameters other than related to biofuels and land-use are
from Burniaux and Chateau (2010); van der Werf (2008); Timilsina
and Shrestha (2006); Ma et al (2010); Jarrett and Torres (1987) and
Narayanan and Walmsley (2008).
• Elasticity of substitution between biofuels and fossil fuels
• Existing studies (e.g., Birur et al. 2007) - 2.0 based on historical data
• Increase the value from 1.2 (2004) to 3.0 (2020) to reflect expansion of flex-
fuel vehicles
• Elasticity parameters for land-use module:• A high value (18) between AEZ (based on literature)
• CET elasticity values -- -0.2, -0.5 and -1.0, respectively for top, middle and bottom nests
(Choi, 2004; Hertel et al. 2008)
Single Country Model: Key Features
• Multisector, SAM based general equilibrium model for a country (Thailand, Brazil, Nigeria and Morocco);
• It has two regions: the country and the rest of the world (but assumption small open economy)
• Number of sectors are flexible based on policy questions to be analyzed (for example, in Thailand 187 sectors are aggregated to 21 sectors)
• Deep nested structures for representing the behavior of production and household sectors;
Single Country Model: Difference from other models
• Has detailed representation of the energy sectors and commodities (e.g., coal, crude oil, natural gas, fuel wood, petroleum refinery, gas processing and electricity generation);
• The electricity sector is further divided into seven sub-sectors: hydro; coal-, oil- and gas- fired steam turbine; oil- and gas- fired combined cycle; and diesel fired internal combustion engine;
• Refined petroleum products are divided into three category: gasoline, diesel and others
• Land use and biofuels are explicitly represented to allows modeling of GHG mitigation options in the land use change and forestry sector
Single Country Model: Production Structure(Excluding transport, agriculture and forestry sectors)
Output
Materials Value Added & Energy
Labor Capital & Energy
Capital
Energy
Electricity
Non-electricity
Coal
Liquid fuel (Petroleum)
Gasoline
Diesel
Others
Gas
Single Country Model: Production Structure(transport sector)
Liquid fuels
Other petroleum products Biofuels-gasoline-diesel
Ethanol & gasoline
Ethanol
Gasoline
Biodiesel & diesel
Biodiesel
Diesel
Single Country Model: Production Structure(Agriculture and forestry sectors)
Output
Materials Value Added & Energy
Land Labor
Capital &
EnergyC
apital
Energy
Electricity
Non-electricity
Coal
Liquid fuel (Petroleum)
Gasoline
Diesel
Others
Gas
Single Country Model: Land Supply
Total land supply
Protected forests Other lands
UnprotectedForest lands
Crop lands
Land for sugar crop
Land for soybean
Land for other crops
Pasture land
Single Country Model: Electricity Supply
Electricity
Hydro & Renewable Thermal
Steam Turbine
Coal
Oil
Gas
Combined cycle/gas
turbine
Gas Oil
Modeling Challenges
– CGE models, normally do not have technology level details of production sectors, especially when a production sector is an aggregate of several sub-sectors, which in turn are aggregate of several technologies (e.g., food & beverage sector, chemical sector)
– Since CGE models are based on database of a base year (SAM), modeling a technology which does not exist in the base year is difficult (although there might be some tricks)
– Since MAC curve is a product of a separate models/modules outside the CGE model, there exits always a danger of inconsistencies on assumptions on the common economic variables (e.g., GDP growth, price assumptions, etc.)
– Precise estimation cost of climate change impacts is difficult if not impossible.
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