http://www-iam.nies.go.jp/aim/publications/report/2010/cge_4.5W.pdf
28 June 2010

Analysis of 4.5 W/m2 stabilization scenarios with renewable energies and advanced technologies
using AIM/CGE[Global] model
Toshihiko Masui, Shuichi Ashina and Junichi Fujino
AIM Team, National Institute for Environmental Studies
16-2, Onogawa, Tsukuba, 305-8506
Contact: masui@nies.go.jp

1. Introduction
In order to reduce the greenhouse gas (GHG) emissions to avoid the climate change, many
countermeasures are proposed. The energy efficiency improvement can contribute to the
reduction of energy demand. On the energy supply side, introduction of renewable energies isrecognized as an important countermeasure to reduce the fossil fuel demand. Moreover, CCS(carbon capture and storage) is regarded as a key technology for the long-term countermeasure.
In this paper, in order to stabilize 4.5 W/m2 of radiative forcing, the contribution of renewableenergies and the advanced technologies such as CCS is assessed using a global model named
AIM/CGE[Global].

2. ModelThe AIM (Asia-Pacific Integrated Model) has been developed to assess mitigation options to
reduce the GHG emissions. The AIM/Enduse model is a partial equilibrium model of energy
system, and it can assess the GHG emission reduction by introduction of specific technologies
based on the assumption of the future energy service demands. On the other hand, the AIM/CGE
model can show consistent economic activity by introducing the countermeasures to reduce the
GHG emissions. In this paper, introduction of renewable energies and new technologies tostabilize 4.5 W/m2 of radiative forcing is assessed by using AIM/CGE[Global], a variant of
AIM/CGE model.
The AIM/CGE[Global] is a global computable general equilibrium model with recursive dynamics.
The world is divided into 24 regions as shown in Table 2.1. The benchmark economic dataset
follows the GTAP6. That is to say, the benchmark year is 2001. The simulation years are 2005,
2010, and every 10 years up to 2100. The classification of commodities is shown in Table 2.2. The
electricity is produced by many sub-sectors; thermal power plants (based on coal, crude oil, oil
products and natural gas), hydro, nuclear, solar, wind, geothermal, biomass, and others. It is1assumed that the CCS technology can be installed in the thermal and biomass power plants. The
biomass energy can be transformed into bio-fuel or bio-gas.
Table 2.1 Regional definition in AIM/CGE [Global]
JPN

Japan

USA

USA

CHN

China

XE15

EU-15 in Western Europe

KOR

Korea

XE10

EU-10 in Eastern Europe

IDN

India

RUS

RussiaIND

Indonesia

XRE

Rest of Europe

THA

Thailand

ARG

Argentine

XSE

Other South-east Asia

BRA

BrazilXSA

Other South Asia

MEX

MexicoAUS

Australia

XLM

Other Latin America

NZL

New Zealand

XME

Middle East

XRA

Rest of Asia-Pacific

ZAF

South Africa

CAN

CanadaXAF

Other Africa

Table 2.2 Classification of commodities
AGR

Agriculture

(1)

TRT

transport

(3)

LVK

Livestock

(1)

CMN

communication

(3)

FRS

Forestry(1)

OSG

public service

(3)

FSH

Fishing(2)

SERother service

(3)

OMN

mining (except fossil fuels)

(2)

COA

coal

(4)

EIS

energy intensive products

(3)

OIL

crude oil

(4)

M_M

metal and machinery

(3)

P_C

petroleum products

(5)

OMF

other manufactures

(3)

GAS

natural gas

(4)

WTR

water

(3)

GDT

gas manufacture distribution

(6)

CNS

construction

(3)

ELY

electricity(7)(1)-(7) correspond to the classification of production structure in Appendix.
Figure 2.1 shows the overall model structure. Each region has the production sectors as shown in
Table 2.2 and the final demand sector. The final demand sector consists of household andgovernment. The final demand sector has capital, labor, resources, and land as endowments, andreceives income by supplying these endowments to the production sectors. Each production sector
produces the corresponding commodity by taking inputs of production factors, raw materials andenergies. The land is input in agriculture, livestock, and forestry sectors, and can be shared
among these three sectors. In order to produce commodities in the fishing, mining, crude oil, coaland natural gas sectors, rent is taken into account as a production factor. The depletion of thesefossil fuels is represented by increase in the fuel extraction cost. The model assumes a2relationship between the fuel extraction cost and the accumulated fuel extraction. The detailed
production structures are depicted in the Appendix.
The produced commodities are supplied to the international market and/or domestic market. In
the domestic market in each region, the goods produced in that region and the imported goods areadded. A uniform international market for the crude oil, coal and natural is assumed. Bilateral
trades for other goods are considered based on the Armington assumption. In each domestic
market, the supplied commodities are consumed as intermediate goods in the production sectors,
or final consumption goods or investment goods in the final demand sector.
The total investment in each region in a period follows the expected future economic growth rate
defined exogenously. In the production sectors, the existing capital and the new capital are
distinguished. The existing capital cannot move among the sectors, whereas the new capital can.
When the new capital is installed in a specific sector, it cannot move to other sectors later. The
technology changes such as energy efficiency improvement are represented through the newcapital. For the existing capital, the efficiency change is assumed in proportion to the new capitalinstalled in the previous years. This means that the more new capital is installed, the more rapid
efficiency change will be. The sectors in which the new investment is not introduced do not exhibit
efficiency improvement.
The GHG emissions treated in the model are CO2, CH4 and N2O. Other GHGs are treatedexogenously. The air pollutants such as SO2 and NOx are calculated in the model. The GHG andother gas emissions from the fossil fuel combustion and production process are distinguished.

laborcapitalland

resource

production

production sector

intermediate demand

final demand sector

marketfinal demand

tradeother countries

Figure 2.1 Overall structure of AIM/CGE[Global]

33. Renewable energy potential
The model includes five types of renewable energy: (1) Solar, (2) Wind, (3) Biomass, (4) Hydro, and(5) Geothermal. Future potentials for hydro and geothermal have been set based on 2007 surveyof energy resources by World Energy Council, and estimation by International Geothermal
Association, respectively.
3.1 Solar power
The potentials are determined based on SeaWiFS Surface Solar Irradiance data1 and Global
Land Cover Characterization data2.
The SeaWiFS Surface Solar Irradiance data includes monthly-averaged solar intensity (unit:
10,000 W/m2) on the global surface (onshore and offshore) from July 1983 to June 1991. The
global surface is represented by 2.5o x 2.5o mesh.
The Global Land Cover Characteristic data is represented by 0.5ox0.5o mesh and its land use isclassified into 17 types of covers as shown in Table 3.1.
Table 3.1 Legend of land use type in the global land cover characteristic data
Value

Description

1

Evergreen Needleleaf Forest

2

Evergreen Broadleaf Forest

3

Deciduous Needleleaf Forest

4

Deciduous Broadleaf Forest

5

Mixed Forest

6

Closed Shrublands

7Open Shrublands

8

Woody Savannas

9

Savannas

10

Grasslands

11

Permanent Wetlands

12

Croplands

13

Urban and Built-Up

14

Cropland/Natural Vegetation Mosaic

15

Snow and Ice

16

Barren or Sparsely Vegetated

17

Water Bodies

99

Interrupted Areas (Goodes Homolosine Projection)

100

Missing Data

1

The NASA Goddard Institute for Space Studies (GISS), (1991). “Surface solar irradiance:

Datasets produced for SeaWiFS”, http://data.giss.nasa.gov/seawifs/.
22) The U.S. Geological Survey (USGS), (1997). “Global Land Cover Characterization”,

http://edc2.usgs.gov/glcc/glcc.php
43.1.1 Methodology of evaluation
Utilization of solar power can be classified into two categories; generation of electricity and direct
heat use. The model focused on only electricity generation potentials by photovoltaics.
Solar potentials have been estimated by four steps: (1) Grouping of land use, (2) Determinationand grading of solar intensity (3) Estimation of solar energy’s physical and technologicalpotentials by country, and (4) Calculation of solar energy’s economic potentials by country.
(1) Grouping of land use
The type of land use impacts construction of photovoltaic systems. We aggregated land use types
in the Global Land Cover Characteristic data into four types: (1) Forests, (2) Grasslands, (3)Urban, and (4) Others, and assumed the space availability for setting up photovoltaics of 5% in
forests, 20% in grasslands, 20% in urban areas. No photovoltaic is available for ‘other’ types oflands. In table 2.1, Nos. 1, 2, 3, 4, 5, 6, 8, 14 are grouped as Forests, Nos. 7, 9, 10, 15, 16 are
Grasslands, and No. 13 is Urban land.
(2) Determination and grading of solar intensity
We used the data from January 1984 to December 1990 of the SeaWiFS Surface Solar Irradiancedata. By averaging monthly data during that period, representative monthly averaged solar
intensity has been set by each mesh (25o x 25o).
Each mesh is classified into six grades based on its solar intensity, as shown in Table 3.2.
Table 3.2 Definition of solar intensity grades
Grade

Explanation

Grade 1

> 280 W/m2

Grade 2210-280 W/m2

Grade 3

170-210 W/m2

Grade 440-170 W/m2

Grade 5120-140 W/m2

Grade 6< 120 W/m2

(3) Estimation of solar energy’s physical and technological potentials by country
Physical potential of solar energy for a country can be calculated by:

Rk =∫ (rad ×η × A )diiiii∈Country k(3.1)

where, k is country number, i is mesh number, Rk is total solar energy in country k [W], radi istotal solar intensity in mesh i [W/m2], ηi is availability factor by land use type in mesh i (Forests:
0.05, Grasslands: 0.2, Urban: 0.2), Ai is the area in mesh i [m2].
Technological potentials could be obtained from physical potentials by multiplying with efficiencyof photovoltaic system. In our study the efficiency was assumed as 8%.
5(4) Calculation of economic potentials by country
Economic potentials are determined by the model analysis. Here, we calculated cost data as an
input for the model, which is one of the major drivers of economic potential.
Generation cost of the photovoltaic system is:Cst = (crf + ins )× (1 + IND) ×( MOD + BOS + PC × SP × EPV )+ OMC
SYR × EPV × EPC(3.2)

where Cst is generation cost [$/MWh], crf is capital recovery factor (determined by equation (3.3))[-], ins is insurance [-], IND is indirect cost [-], MOD is module cost of photovoltaic system [$/MW],BOS is non-module cost [$/MW], PC is ancillary cost for power output [$/MWh], SP is maximum
solar intensity [kW/m2], EPV is efficiency of photovoltaic system [-], EPC is efficiency of power
control [-], SYR is annual solar radiation [MWh/m2/yr] and OMC is operation and maintenancecost [$/MWh].

crf =

r
1 − (1 + r ) N(3.3)

where r is discount rate and N is depreciation year.
Figure 3.1 represents the summary of solar power energy potential by grade and model region.

2500000

TWh/yr2000000
1500000
1000000
5000000
grade1

grade2

grade3

grade4

grade5～～～～～143$/MWh191$/MWh235$/MWh285$/MWh333$/MWh

XAF

ZAF

XME

XLM

ARG

MEX

BRA

XRE

RUS

XE10

XE15

USA

CAN

XRA

NZL

AUS

XSA

XSE

THA

IND

IDN

KOR

CHN

JPN

Figure 3.1 summarized cost of solar energy by region.
3.2 Wind energy potentials
The potentials of wind energy are determined based on the Climatic Research Unit Global
Climate Dataset3 and the Global Land Cover Characterization data2) (same as section 3.1).
The Climatic Research Unit Global Climate Dataset includes monthly-averaged wind speed(Unit: 10 m/s) on the global surface (onshore) from 1961 to 1991. The global surface is represented

3

IPCC Data Distribution Center (IPCC/DDC), (2009). “The Climatic Research Unit Global

Climate Dataset”, http://www.ipcc-data.org/obs/cru_climatologies.html
6

by 0.5o x 0.5o mesh.
Wind potentials have been calculated by seven steps: (1) Grouping of land use, (2) Assumption for
typical wind generator (3) Estimation of average wind speed over the available altitude for thewind turbine (>30 meter), (4) Estimation of annual average generation from wind turbine, (5)Grading of physical potentials of wind energy, (6) Estimation of physical and technologicalpotentials by country, (7) Calculation of economic potentials by country.
(1) Grouping of land use
Installation of wind turbine is constrained by land use type. We aggregated Land use types in the
Global Land Cover Characteristic data into three types: (1) Forests, (2) Grasslands, and (3)Others. In Table 3.1, Nos. 1, 2, 3, 4, 5, 6, 8, 14 are grouped as Forests, and Nos. 7, 9, 10, 15, 16 are
Grasslands.
(2) Assumption for typical wind generator
In order to evaluate wind energy potentials at the global scale, a virtual wind turbine is assumed.
Capacity of wind turbine is 500 kW, diameter is 40 m, and cut-in (Vin) and cut-out (Vout) windspeed is 5.5 m/s and 24 m/s. Rated wind speed is 12.5 m/s. The characteristic curve for the windturbine can be drawn as shown in Figure 3.2.
For installation of multiple wind turbines, in order to avoid rear turbulence, each wind turbine isassumed to keep 10 times of diameter clearance, thus, one wind turbine occupies 160,000 m2.
Power [kW] P(V)
500

5.5
(Vin)

12.5

24

Wind speed[m/s]V(Vout)

Figure 3.2 Characteristic curve for virtual wind turbine(3) Estimation of average wind speed over the available altitude for the wind turbine (>30 meter)
The data includes varying observation altitude, however, in the research we assume all data at 10
m height.
By averaging all data by mesh, total monthly average wind speed is derived. It is well known thatwind speed follows power law of altitude as shown in equation (3.4). By using this equation, windspeed at 30 meter altitude is calculated in each mesh.
1⎛ z ⎞nV = Vh × ⎜ ⎟
⎝h⎠

7

(3.4)

where V is wind speed at altitude z, Vh is observed wind speed at altitude h, and n is
landform-dependent parameter (4.8 in the research).
(4) Estimation of annual average generation from wind turbine
Probability distribution of wind speed follows the Rayleigh distribution as shown in equation(3.5).

⎧⎪ π ⎛ V ⎞ 2 ⎫⎪V
f (V ;Vz ) = × × exp ⎨− ⋅ ⎜⎜ ⎟⎟ ⎬2 Vz⎪⎩ 4 ⎝ Vz ⎠ ⎪⎭

π

(3.5)

Then, annual generation from wind turbine at wind speed V can be calculated by:

G (V ) = P (V ) × f (V ;Vz ) × 8760

(3.6)

where G(V) is generation at wind speed V [Wh]By integration from cut-in speed to cut-off speed, annual generation from wind power can be
estimated.
Vout

Vout

Vin

Vin

WYPi = ∫ G (V ) dV = ∫ P (V ) × f (V ;Vz ) × 8760 dV(3.7)

where WYPi is yearly wind power generation at mesh i.
Figure 3.3 shows results of annual generation by wind speed.

3000000

Annual generation [Wh/yr]

2500000
2000000
1500000
1000000
500000
0
0

5

10

15
20
Average Wind speed [m/s]25

30

Figure 3.3 Annual generation by wind speed(5) Grading of physical potentials of wind energy
Wind energy has been classified into 5 grades based on annual utilization rate as shown in Table
3.3. Annual utilization rate in each mesh (Ui) is determined by equation (3.8).

Ui =

WYPi
Capacity × 8760

(Capacity is 500 kW)

8

(3.8)

Table 3.3 Grading for wind power
Grade

Utilization rate

Grade 1

> 0.30

Grade 20.25-0.30

Grade 30.20-0.25

Grade 40.15-0.20

Grade 5

< 0.15(6) Estimation of physical and technological potentials by country
Physical potentials from wind power by country can be calculated by multiplying maximum
number of wind turbines with the results of equation (3.7). Maximum number of wind turbines is
set based on the area available in each mesh and the area required by a turbine (160,000 m2)..
(7) Calculation of economic potentials by country
Economic potentials are determined by the model analysis. Here, we calculated cost data as an
input for the model.
Generation cost of the wind turbine is:⎛ crf × Cc + C m ⎞Cst = ⎜⎟ × ( N × 500 )
WYP
⎝
⎠

(3.9)

where Cst is generation cost [$/kWh], crf is capital recovery factor as shown in equation 3.3, Cc is
Capital cost [$/kW], Cm is maintenance cost [$/kW], and N is number of wind turbines in acountry.

TWh/yrFigure 3.4 shows the summary results of wind energy potential by the model regions.

200000180000160000140000120000100000
80000
60000
40000
20000
0
grade1

grade2

grade3

grade4

grade5～～～～～85$/MWh 100$/MWh120$/MWh149$/MWh199$/MWh

Figure 3.4 Summary of wind energy potential

9

XAF

ZAF

XME

XLM

ARG

MEX

BRA

XRE

RUS

XE10

XE15

USA

CAN

XRA

NZL

AUS

XSA

XSE

THA

IND

IDN

KOR

CHN

JPN

3.3 Biomass potential
Biomass energy can be classified into three categories: (1) Traditional fuelwood, (2) Plantation
bioenergy, and (3) Residue bioenergy. In this section, only the residual bio energy is explained.
Plantation bioenergy is calculated in the model taking into account the land.
Source of bioenergy is a wide variety even of biomass production and utilization processes. The
model addresses residue bioenergy through the following steps:(1) Assumption for incidence rate of residue bioenergy from biomass production/utilization
process(2) Determination of production/utilization quantity of biomass(3) Estimation of cost for utilization of residue bioenergy
Table 3.4 shows a part of the data set we prepared for incidence rate of residue bioenergy for themodel. Figure 3.5 shows its potential .
Table 3.4 Incidence rate of residue bioenergy
Process

Incidence rate

Energy production rate[dry-t/dry-t][GJ-HHV/dry-t]

Rice production

1.4

16.3

Straw production

1.3

17.5

Corn production

1.0

17.7

Rootstock production

0.4

6.0

Sugarcane production

0.28

17.33

Bagasse

0.15

17.33

Cow

1.10 [t/yr/animal]

15.0

Grunter

0.22[t/yr/animal]

17.0

Chicken

0.037[t/yr/animal]

13.5

Horse

0.55[t/yr/animal]

14.9

Buffalo, Camel

1.46[t/yr/animal]

14.9

Sheep, Goat

0.18[t/yr/animal]

17.8

Log stock for industrial use

1.17

16.0

Log stock for fuel woods

0.67

16.0

Lumber residue

0.784

16.0

10

mill residues & timber
scrap

industrial roundwood
residues

kitchen refuse&humanfeces

black liquor & paper
scrap

dung

bagassecrop and sugarcane
residues

EJ/year

12
10
8
6
4
2
0

XAF

ZAF

XME

XLM

ARG

MEX

BRA

XRE

RUS

XE10

XE15

USA

CAN

XRA

NZL

AUS

XSA

XSE

THA

IND

IDN

KOR

CHN

JPN

Figure 3.5 Summary of residue bioenergy by model regions

4. Future Scenarios and simulation results
The following three scenarios are considered for analysis.
1) The reference scenario2) Stabilization of 4.5W/m2 with CCS technologies
3) Stabilization of 4.5W/m2 without CCS technologies
These scenarios use common assumptions about population change, expected GDP growth,
efficiency changes, and renewable energy potential. The renewable energy potential in the futureis explained in the previous section.
The future population changes up to 2050 follow the medium variant estimated in “World
Population Prospects, The 2008 Revision”, and after 2050, the growth rates of population follow
the medium scenario estimated by “World Population to 2300”. The economic growth follows theSustainability First scenario of UNEP/GEO4. The energy efficiency improvement is set toreproduce the primary and final energy demands in SRES B2 for the reference scenario. The land
productivity is assumed to follow the past trend with an upper limit.
Figure 4.1 shows the global CO2 emissions in each scenario. In the reference scenario, CO2
emissions will increase throughout 2100. On the other hand, in the 4.5W/m2 stabilization
scenarios, CO2 emissions will peak in 2050, and then decrease. In the reference scenario, theradiative forcing in 2100 will reach about 6.3 W/m2 as shown in Figure 4.2.

11

100
90

CO2 [GtCO2]

80
70
60
50
40
30
20
10
0
2000

2020

Reference

2040

2060

4.5W/m2 w CCS

2080

2100

4.5W/m2 w/o CCS

Figure 4.1 Global CO2 emissions results

Radiative forcing [W/m2]

7.00
6.00
5.00
4.00
3.00
2.00
1.00
0.00
2000

2020

Reference

2040
4.5W/m2 w CCS

2060

2080

2100

4.5W/m2 w/o CCS

Figure 4.2 Total radiative forcing
In order to reduce the GHG emissions, many countermeasures are introduced. Figures 4.3 and 4.4
represent the global primary energy and the global final energy change in each scenario. In thereference scenario, the global primary energy in 2100 will exceed 1000 EJ. On the other hand, theglobal primary energy in the stabilization scenarios in 2100 will be about 700 EJ. In thestabilization scenarios, the primary energy supply in the scenario with CCS will be more thanthat in the scenario without CCS, because the CCS can reduce the CO2 emissions from the large
scale fossil fuels combustion plants as shown in Figure 4.5. The CCS capital will be installed after
2050. In the stabilization scenario without CCS, more renewable energy will have to be
introduced instead of fossil fuels. Figure 4.6 represents the mix of the primary energy supply. In
the stabilization scenario with CCS, the share of renewable energy will be expanded, especially inwind and bioenergy. In the stabilization scenario without CCS, increase of the renewable energy
share will be more intense. As a result, the price of CO2 in the stabilization scenario without CCS

12

will be more expensive than that with CCS, as shown in Figure 4.7. The GDP change in 2100
compared to the reference scenario will be -4.6% and -7.0% in the stabilization scenarios with
CCS and without CCS, respectively.

Total primary energy [EJ]

1200
1000
800
600
400
200
0
2000

2020

Reference

2040

2060

4.5W/m2 w CCS

2080

2100

4.5W/m2 w/o CCS

Figure 4.3 Global primary energy supply

1000

Final energy [EJ]

800
600
400
200
0
2000
Reference

2020

2040
4.5W/m2 w CCS

2060

2080
4.5W/m2 w/o CCS

Figure 4.4 Global final energy demand

13

2100

CO2 storage [GtCO2]

100
80
60
40
20
0
2000

2020

Reference

2040

2060

4.5W/m2 w CCS

2080

2100

4.5W/m2 w/o CCS

Figure 4.5 Captured CO2 in the world

1200
Other

Primary energy [EJ]

1000

Geothermal
800

Hydro

600

Solar

400

Wind

200

Bioenergy
Nuclear0
Reference

4.5W/m2 w
CCS

4.5W/m2
w/o CCS

Coal
Gas
Oil

2000

2100

Figure 4.6 Mix of primary energy supply in the world

CO2 price [2000$/tCO2]

500
400
300
200
100
0
2000

2020

Reference

2040
4.5W/m2 w CCS

2060

2080
4.5W/m2 w/o CCS

Figure 4.7 Price of CO2

14

2100

350

GDP [tri.2000US$]

300
250
200
150
100
50
0
2000

2020

Reference

2040

2060

4.5W/m2 w CCS

2080

2100

4.5W/m2 w/o CCS

Figure 4.8 Global GDP change
The GDP losses in non-Annex I countries in the stabilization cases will be larger than those inAnnex I countries as shown in Figure 4.9, because a global emission market is assumed in thesesimulations. Although allocations of CO2 emissions in Annex I countries will be smaller thanthose in non-Annex I countries, the real CO2 emissions in Annex I countries will be more becauseof purchase of emission rights. If the global emission market is not assumed, the carbon price inAnnex I countries will increase, and consequently, the GDP loss in these countries will be more
severe.

4.5W/m2 w CCS

4.5W/m2 w/o CCS

GDP change [2000 tri.$]0‐5Annex I‐10

non‐Annex I
‐15

‐20

‐25

Figure 4.9 Breakdown of GDP change by regions

5. Conclusion
In this paper, the stabilization scenarios at 4.5 W/m2 of radiative forcing are examined. Comparedto the reference scenario, the stabilization scenarios will require more renewable energies. If the

15

CCS technologies are not introduced, furthermore renewable energies will have to be introduced
to achieve the stabilization target. As a result, the economic impacts will be more severe. In order
to avoid severe economic impacts, all possible countermeasures will have to be prepared. In thiscase, involvement of the developing countries in mitigation will become necessary, as they have astarting disadvantage of technologies and requirement of more rapid economic growth ascompared with the developed countries.

16Appendix: Production structure in AIM/CGE[Global](1) Production structure using land
producedgoods

sectornon-energy
intermediate
goods

value added

capital

labor

land

solid

coal

fuel

electricity

liq

coal

crude oilGHGs

crude oil

GHGsfrom processenergygas
oil products
oilproducts

GHGs

GHGs

natural gas
naturalgas

town gas

town gas

GHGs

GHGs

(2) Production structure using non-fossil resources
producedgoods

sectornon-energy
intermediate
goods

value added

capitallabor

resource

solid

coal

fuel

electricity

liq

coal

crude oilGHGs

crude oil

GHGs

GHGs
from processenergygas
oil products
oilproducts

GHGs

17

natural gas
naturalgas

GHGs

town gas

town gasGHGs

(3) Production structure in manufacturing and service sectors
producedgoods

sectornon-energy
intermediate
goods

value added

capital

labor

fuel

solid

coal

electricity

liq

coal

crude oil

GHGs

GHGs
from processenergycrude oilgas
oil products
oilproducts

GHGs

GHGs

natural gas
naturalgas

town gas

town gas

GHGs

GHGs

(4) Production structure in fossil fuels extraction
producedgoods

sectorvalue added

capitalresourcelaborelectricity

liq

coal

crude oil

GHGs

GHGs
from processenergy

fuel

solid

coal

non-energy
intermediate
goods

crude oil

GHGs

gas
oil products
oilproducts

GHGs

18

natural gas
naturalgas

GHGs

town gas

town gasGHGs

(5) Production structure in petroleum products
producedgoods

sectorvalue added

capitalnon-energy
intermediate
goods

crude oil

labor

coal

GHGs
from processenergy

fuel

solid

liq

coal

oil products

GHGs

electricity

gas

oil
products

GHGs

natural gas
naturalgas

town gas

town gas

GHGs

GHGs

(6) Production structure in gas production and distribution
producedgoods

sectorvalue added

capitalnon-energy
intermediate
goods

natural gas

labor

fuel

solid

coal

electricity

liq

coal

gas

crude oil

GHGs

GHGs
from processenergycrude oilGHGs

oil products
oilproducts

19

GHGs

town gas

town gasGHGs

(7) Production structure in power sectors
producedgoods

sectorvalue added

capital

land

labor

notes:

electricity

liq

coal

crude oil

GHGs

GHGs
from processenergy

fuel

solid

coal

non-energy
intermediategoods

renewable
resource

crude oil

GHGs

gas
oil products
oilproducts

GHGs

natural gas
naturalgas

GHGs

town gas

town gas

Individual power sectors have different structures.
Biomass power sector takes land as an input.
Other renewable sectors take the input of corresponding renewable resources.
A thermal power sector takes corresponding fossil fuel as input.

20

GHGs