A 3-regional CGE-model
for China
Development Research Centre, PRC
Development Research Center, The State Council, PRC
Presentation Outline
ƒ Introduction
ƒ Economic part of the three-regional model
ƒ Environmental part of the three-regional model
ƒ Further Analysis
Development Research Center, The State Council, PRC
Introduction
ƒ The main goal:
To analyze environmental implications of
China’s WTO accession
ƒ Geographic differentiation:
A single China-wide model
A two regional model
(GD-Guangdong and ROC-Rest of China)
A multi-regional model
Development Research Center, The State Council, PRC
ƒ The three-regional model
• The three -region Chinese CGE model we employ in this
study is an extension of the following models that had
been used in China’s WTO accession study
ƒ the single region Chinese CGE model (Development Research Center,
1998)
ƒ The two regional Chinese CGE model (Li and Zhai, 2000,2002)
• Three regions
ƒ GD (Guangdong), SX (Shanxi) and Rest of China (ROC)
ƒ Why?
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ƒ Introduce of the geographic differentiation into
the model
- to reflect the different impact on different regions
of WTO accession in China according to different
regional comparative advantage, etc.
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Shanxi Province
Guangdong
Province
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The differentiation between
GD and SX
ƒ
ƒ
ƒ
ƒ
Location, Area
Population
Nature Resource
Economic development
• GDP, Per capita GDP, Market openness,
Industrial structure, International trade, etc.
ƒ Infrastructure, FDI, Human resource,
Institutions, etc.
ƒ ……
Development Research Center, The State Council, PRC
Some indicators for GD&SX in 2003
SX
GD
Ratio to
National
level
Ratio to
National
level
China
Population (10000 persons)
3314
2.6
7954
6.2
129227
GDP（100 million RMB）
2457
2.1
13626
11.6
117252
Per capita GDP (RMB)
7435
81.7
17213
189.1
9101
Import & Export (USD 10 000)
52
0.6
2892
34
8510
Export
37
0.8
1537
35.1
4382
Import
14
0.3
1355
32.8
4128
21361
0.4
782294
14.6
5350467
FDI(USD 10 000)
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Low
High
Average
GDP growth
High
Beijing、Tianjing、Zhejiang、
Jilin、Xinjiang、Heilongjiang
Shanghai、Guangdong、
、Liaoning
Fujiang、Hebei、Shandong、
Hebei、Jiangsu
Average
Per capita GDP
Low
Shaanxi、Jiangxi、Hunan、
Ninxia、Guizhou、Yunnan、
Shanxi、Chongqing、Anhui、
Hainan、Guangxi、Sichuan
Xizang、Gansu、Inner
Mongolia、Qinghai、Henan
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Guangdong Province
ƒ Guangdong province locates in southern
China, neighboring Hong Kong and Macao.
As one of the largest economies in China,
ƒ It accounts for 35 percent of national foreign
trade in 2003.
ƒ The development of Guangdong since 1978
and its economic structure could be a
representation of China’s coastal area.
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Shanxi Province
ƒ Shanxi, locates on the middle part of North
China.
ƒ As the "Coal Warehouse of China", the output
of coal in Shanxi ranks the first in China and
accounts for nearly one-fourth of the country's
total.
ƒ According to the UNIDO technique
classification, resource-based manufactured
export account for 61.94% of the total
manufactured export in 2000.
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ƒ Difference between 2 regional model and 3
regional model
- “bilateral” to “triangular” , etc.
ƒ Some important issues in the model
- data
- environmental issues
- “new” energy
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Economic part of the threeregional model
ƒ Data-three regional SAM
ƒ CGE-Model
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Data-three regional SAM
ƒ Inter-regional trade
ƒ Two separate trade regimes
ƒ Different household groups
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Inter-regional trade
ƒ In most countries, interregional trade is not
covered by official statistics, which results in
it having to be estimated by whoever has an
interest in it (Pedro Ramos et al., 2003). In
China, that is the case, too.
ƒ With regional IO tables and Customs
Statistics, we can get international trade,
inflow (not incl. import) and outflow (not incl.
export)
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Table 1: The main methods for estimating the interregional trade
TECHNIQUE USED FOR
THEESTIMATION
SOME MODELS
INDIRECT ESTIMATION
A POSTERIORI
Use of Gravitational model
TIM, (Funck et al. 1975)
Use of Entropy Maximising Paradigm
Batten (1983)
Pool-Approach of Leontief
Leontief (1977)
INTERREG (Martellato et al, 1996)
DIRECT ESTIMATION BASE ON REAL DATA
↓
A PRIORI
Use of International trade flows
EU-IRIO (Oosterhaven et al., 1995)
Use of Transport flows
MRIO-HERP (Polenske 1980);
Hewings, 1993; Kazumi H., 2000.
INTERTIO, (Llano, 2000)
Use of surveys designed ad-hoc for
producers and consumers.
JAPAN IRIO TABLES (1960-70)
Source: Carlos Llano Verduras, the estimation of the interregional trade in the context of an interregional input-output model for the
Spanish economy,
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Inter-regional trade
ƒ The main method used to estimate the
inter-regional trade in this study
Indirect Estimation--Use of Gravitational
model
ƒ We only estimation the inter-regional
trade Matrix (3×3) for merchandise trade.
ƒ For services trade we use the net outflow.
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Table 2: The schematic interregional trade matrix
Regions
GD
GD
SX
ROC
T12
T13
T23
SX
T21
ROC
T31
T32
Total
IF1
IF2
Total
OF1
OF2
OF3
IF 3
Notes: GD-Guangdong Province, SX-Shanxi Province, ROC-the Rest of China.
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ƒ Gravity model
D j S i (GDPi ) (GDPj ) (OI i ) (OI j )
γ1
Tij = α
TDS
γ2
(d )
δ1
δ2
β
Where, suffix i refers to the origin, j refers to the destination. Dj
is the total demand for a given commodity in region j; Si is the
total supply in region i; TDS is the total demand (or total supply)
for the three regions. GDP is the regional economic size (share of
GDP). OI is the trade openness index. d is the distance between
the region i and j. α,β ,γ andδ are parameters.
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ƒ first step :To choose the parameters, we structure
the following programming problem
:
2
⎞
⎛
min destination = ∑ ⎜ ∑ Tij − IF j ⎟
⎠
j ⎝ i
s.t.
γ
γ
δ
δ
⎧
D j S i (GDPi ) 1 GDPj 2 (OI i ) 1 OI j 2
⎪ Tij = α
β
TDS
(d )
⎪
⎪ ∑ Tij = OFi
⎪⎪ j
( IFROC + OFROC )
⎨ OI ROC =
OUTPUTROC
⎪
⎪ ∑ IF j = ∑ OFi
i
⎪ j
⎪ Tij ≥ 0
⎪⎩ IFROC ,OFROC ≥ 0
(
)
( )
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ƒ Second step :To balance the trade matrix,
we use Cross Entropy Methods:
⎛ Tij0
⎛ Tij ⎞ ⎞
min entropy = ∑∑ ⎜
LN ⎜ 0 ⎟ ⎟
⎜ T ⎟⎟
⎜ OF j
i
j
⎝ ij ⎠ ⎠
⎝
s.t.
⎧ ∑ Tij = OFi
⎪ j
⎪
⎨ ∑ Tij = IF j
⎪ i
⎪ Tij ≥ 0
⎩
, i = "GD","SX"
, j = "GD","SX"
Development Research Center, The State Council, PRC
The verification of the result
sector
β
sector
β
sector
β
sector
β
Automobile
0.632 Logging
3.813 Textile
11.202 Printing
15.498
corn
0.777 Leather
4.004 CoalMin
11.705 Instrumnt
15.754
Sawmills
0.923 OthManuf
4.217 OthAg
12.206 rice
16.215
Tobacco
1.467 Machinery
5.187 Beverage
12.797 ElecMach
16.792
NFerProd
1.48 Gas
5.611 FoodProc
12.818 Fishing
17.127
FerOreMin
1.952 wheat
5.651 OthCrop
13.478 SocActProd
23.217
Wool
2.239 Apparel
8.318 SpecEquip
13.5 BuildMat
25.566
Chemical
2.746 Quarrying
8.44 RefPet
13.844 GrainForage
37.231
MetalProd
2.763 Water
9.67 IronSteel
13.863 Medicine
61.694
Plastic
3.49 Forestry
9.771 Electron
14.293 Sugar
193.282
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(Continued)
ƒ The statistic transportation yearbook provide
the inter-regional transport data for coal.
ƒ The correlation coefficient between the
published trade matrix and estimated trade
matrix is 0.82.
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Two separate trading regimes
ƒ Two separate trading regimes
Processing trade
Ordinary trade
ƒ In this SAM, both production and trade are
divided between ordinary trade and
processing trade.
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Different household groups
ƒ In the SAM, all households are divided into
14 groups, 7 groups of urban households
and 7 groups of rural households.
¾Lowest
¾Low
¾Lower-middle
¾Middle
¾Upper-middle
¾High
¾Highest
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CGE-Model
ƒ
ƒ
ƒ
ƒ
ƒ
ƒ
ƒ
ƒ
ƒ
Model Dimension
Production and Factor Markets
Interregional and Foreign Trade
Income Distribution and Demands
Central and Regional Governments, and Extrabudget Public Sector
Macro Closure
Recursive Dynamics
Data
Parameters of the model
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Environmental part of the threeregional model
ƒ Emission of Pollutant
CO2,SO2,NMVOC,NOX,PM10, CH4 and N2O
ƒ The impact on human health and other
environmental end-points like crop damage
and material damage
ƒ Additional topic-Biomass
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ƒ In the three regional CGE model, the total
amount of a given polluting emission takes
the following form:
E =
∑∑α
i
i, j
C i, j
+
j
∑
β i XP i
+
i
∑γ
j
XA
j
j
I
II
III
Emission with
Emission with
Emission with
intermediate consumption
sectoral production
final consumption
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where i is the sector index, j the consumed product index,
C intermediate consumption, XP output, XA final
consumption, α ij the emission volume associated with one
unit consumption of product j used by sector i; β i the
emission volume associated with one unit production of
sector i. γ j the emission volume associated with one unit
consumption of product j in final consumption .
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Health benefit
Emission Change
Step 1: introduce of dispersion
model
Estimate impacts on air
pollution exposure
Step 2: introduce of doseresponse function
Estimate impacts on mortality
etc. health risk end-points
Step 3: introduce of VSL (Value
of a statistical life)
Estimate units values of
health risk end-points
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Biomass
ƒ In China the biomass energy plays a very important
role in the total energy consumption, especially in
rural energy consumption and plays an important
role in the discharge of GHG.
ƒ While we consider only commercial energy sources
both renewable and natural resources are explicitly
treated. Traditional biomass fuels are ignored since
national accounts and official input-output data do
not include their value (Rana, 1999).
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ƒ In China, the biomass also does not
introduce into the official account
ƒ How to Import the residential combustion of
biomass to China’s environmental CGE
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Import biomass to CGE
ƒ What determines the choice of biomass
energy
ƒ Key issues of adding the biomass energy in
the model
ƒ The function for the biomass consumption
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What determines the choice of
biomass energy
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Figure 2: The transition of energy consumption
Agricultural Modernization
Development
Township Enterprises
Development
Labor Transfer From Agriculture To
Other Industries etc.
The Increase
income Of
Peasants
Development of
large mines and
power
generation etc.
High Quality
of
Living Standard
Construction and
Opening-up of
energy market
Energy ladder
Substitution
Transition of
Energy Consumption
Decrease of the biomass energy consumption
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The factors
ƒ Cost
Opportunity cost of collecting biomass and price of
other energy
ƒ Income
ƒ Living custom etc.
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Key issues of adding the biomass
energy in the model
ƒ “Exogenous”-simple
ƒ “Semi-exogenous”
ƒ “Endogenous”-complicated
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The function for the biomass
consumption
ƒ Demand
-income
ƒ Supply
-the forest coverage rate and per cap output of
grain
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q it = α t + β t ln y it + γ z + γ z + ε it
1 1
t it
2
t
2
it
Where, z1 z 2 denote the forest coverage rate and per
cap output of grain respectively.
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ƒ For the data of non-commercial biomass consumption
in rural area, there is only ten-years-series data of noncommercial biomass consumption at national level.
Maybe the data is too short for econometric analysis.
ƒ Fortunately we have pooled data, non-commercial
biomass consumption by province (31 provinces) and
by years (1991, 1992, 1993, 1995, 1996, 1998, and
1999). So our empirical analysis is based on the pooled
data.
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Table 2 Partial empirical result for non-commerce biomass consumption in rural
area
1991
1992
1993
1995
1996
1998
1999
I nt er cept
-
-
-
-
-
-
-
Ln( I NC)
- 203. 6522
- 188. 8393
- 136. 5411
- 186. 4200
- 84. 8028
- 83. 1575
- 115. 7410
( Per net i ncome i n r ur al ar ea)
( - 3. 646318)
( - 4. 350736)
( - 3. 318059)
( - 3. 654203)
( - 3. 010487)
( - 3. 356177)
( - 3. 687193)
l n( GRAI NP)
250. 0117
225. 4915
179. 9735
254. 9298
126. 5163
127. 6774
166. 3154
( Per cap out put of gr ai n)
( 4. 358807)
( 4. 996974)
( 4. 094587)
( 4. 331719)
( 3. 827585)
( 4. 408217)
( 4. 412811)
FCR
4. 7703*
6. 2152
4. 8507
4. 3929*
3. 6836
3. 2955
2. 9507*
( For est cover age r at e)
( 2. 776916)
( 4. 448686)
( 3. 041686)
( 2. 161174)
( 3. 130782)
( 3. 267888)
( 2. 150377)
R2
0. 49070
0. 61935
0. 47488
0. 51589
0. 47540
0. 51104
0. 45200
2
Adj ust ed R
0. 44642
0. 58625
0. 43287
0. 46979
0. 43343
0. 47343
0. 41141
D. W.
1. 56799
1. 53491
1. 24243
1. 61724
1. 84065
1. 42560
1. 62037
Notes: 1.All the results are derived from the estimation by Eviews
3.1.
2.* Significant at the level of 5%.
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β
per capi t a annual
I ncome El ast i ci t y of
bi omass consumpt i on*
bi omass consumpt i on
1991
- 203. 7
295. 1
- 0. 69
1992
- 188. 8
250. 8
- 0. 75
1993
- 136. 5
265. 5
- 0. 51
1995
- 186. 4
273. 9
- 0. 68
1996
- 84. 8
220. 7
- 0. 38
1998
- 83. 2
224. 9
- 0. 37
1999
- 115. 7
220. 1
- 0. 53
Aver age
- 0. 56
Notes: * Per capita annual biomass consumption in national level, unit:10-3tce/person.
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“Semi-exogenous”
ƒ Link the biomass consumption function to
the model
-the income is the linkage
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Further Analysis
ƒ To design scenarios of China’s WTO accession
ƒ To analyze environmental implications of
China’s WTO accession