Research Institute of Innovative
Technology for the Earth

RITE GHG Mitigation Assessment ModelMay 29, 2009
Systems Analysis Group
Research Institute of Innovative Technology for the Earth (RITE)<Outline>○ The model consists of 3 modules; 1) Key Assessment Model DNE21+, forenergy-related CO2, 2) Non-energy CO2 emission scenario, that assumes specificnon-energy CO2 emissions independent of mitigation levels of energy-related CO2
emissions 3) Non-CO2 GHG Assessment Model, for mitigation of the 5 kinds ofgreenhouse gas emissions of the Kyoto Protocol.
○ The historical total GHG emissions for Annex I and Non-Annex I parties are basedon GHG inventories of UNFCCC (February, 2009) and IEA Statistics (IEA, 2007a),
respectively. As to energy-related CO2 emissions, they are based on IEA Statistics
for all the countries. Whereas the statistical data of Energy-related CO2 emissions
are observed to different between UNFCCC and IEA in some countries, non-CO2
GHG emissions for Annex I parties are defined by subtracting the energy-related
CO2 emissions (IEA statistics) and the non-energy use CO2 emissions (UNFCCC
statistics) from the total GHG emissions (UNFCCC statistics), thus giving priority to
the GHG emissions being consistent with the UNFCCC statistics.

DNE 21+ Model

NonNon-Energy CO2Emissions Scenario

NonNon-CO2 GHG
Assessment Model• Assessment model forenergy-related CO2
emissions• 54 regions in theworld• Bottom-up modeling(200-300 specifictechnologies aremodeled)• Projection module fornon-energy CO2emissions• 54 regions in theworld• Estimations ofsectoral non-energy
CO2 emissions to beconsistent with GDP
and production
activities• Assessment model forthe 5 non-CO2 GHG
emissions (CH4, N2O,
HFCs, PFC, SF6)
• 18 regions in theworld (based on thehistorical data which
are allocated to the 54
regions)• The methodology issimilar to the USEPA
assessment

Estimates of the 6 GHG emissions, emission reduction costs
and potentials, and specific cost-effective measures foremission reductions1

Research Institute of Innovative
Technology for the EarthI
1.

Energy-related CO2 Assessment Model : DNE21+
Features○ The DNE21+ Model represents energy systems (e.g., energy flows, capacities ofenergy related facilities) consistently in which the worldwide costs are minimized,
when the sectoral amounts of production activities (e.g., crude steel and cement),
the amount of service activities (e.g., the traffic amount in the transportation sector),
the final energy demand in other sectors and the performances and the facility costsof various technologies are given.
○ When any emission restrictions (e.g., upper limit of emissions, emissions reduction
targets, specific unit improvement goals, carbon taxes) are applied, the model
specifies the energy systems whose costs are minimized, meeting all the assumed
requirements.
○ Salient features of the model include (1) long term analysis until 2050, (2) analysisof regional differences by the fine regional segregation while maintaining otherglobal issues consistent, and (3) detailed evaluation of global warming measures,
thanks to modeled 200–300 specific technologies against global warming.
○ CO2 from the energy sector is the principal target of evaluation.
○ The model conducts assessments for the cost minimization under the perfectforesight, and does not take into account such uncertain factors as related to energy
security etc. Therefore, implications from the model analysis results should
accordingly be drawn.
○ The lead time is not taken into account while construction of large-scale facilities
such as electric power plants often requires a long lead time. Thus, the implications
from the analysis results should be prudential.
2.

Model Structure○ Total worldwide energy system costs over all the assessment period are minimized(an optimization type linear programming model). The energy supply sectors are
hard-linked with the energy end-use sectors, including energy export/import, life
times of facilities, so that assessments are conducted keeping complete
consistency with energy systems.
○ Eight representative time points are used for optimization: 2005, 2010, 2015, 2020,2

Research Institute of Innovative
Technology for the Earth2025, 2030, 2040, 2050 (2005 represents the period from 2003 to 2007, 2010
represents the period from 2008 to 2012, 2015 represents the period from 2013 to2017 and so on.)○ The world is divided into 54 regions (America, Canada, Australia, China, India,
Russia are divided into further small regions, making a total of 77 regions).

Fig.1-1 Global regional division in DNE21+○ Technological costs and energy efficiency of energy supply technologies (various
power generation technologies, oil refinery, coal gasification technology, etc.) andcarbon dioxide capture, storage and sequestration are explicitly modeled("Bottom-up approach”).
○ Energy Demand Technologies·

Costs and energy efficiencies of technologies used in energy intensive
industries such as steel, cement, paper & pulp, aluminum, some groups of thechemical industry (ethylene, propylene production in the petrochemical industry
and ammonia production), transportation (automobiles) and several groups ofresidential & commercial sector are explicitly modeled ("Bottom-up approach").
The amounts of activities of these sectors (industry: outputs, automobiles:
transportation demands, groups of residential & commercial sector: time
periods of equipment utilization) are estimated exogenously and kept fixed inthis model regardless of emissions constraints, while technological options are3Research Institute of Innovative
Technology for the Earthendogenously determined in the model and so are energy consumption
amounts etc.
·

Other sectors whose technological characteristics and future evolutions varydepending on the region and, therefore, whose bottom-up modeling andincorporation into the model are assumed inappropriate and do not necessarily
lead to fruitful evaluation are modeled in a top-down fashion; final energy
demands are divided into four macro types (solid energy demand, liquid energy
demand (gasoline demand, light oil demand, heavy oil demand), gaseous fueldemand and electrical energy demand) and their amounts of demand areassumed for aggregated three sectors: industry, transportation and householdwithout considering specific technologies ("Top-down approach"). The
assessment is carried out covering all the sectors at the same time.

·

The energy-saving effects are evaluated using long-term price elasticity.

○ With the facility vintage (facility introduction year and capacity) taken into account, it
is explicitly considered that cost-efficiencies of facility replacement vary dependingon the representative time point and then explicitly reflected on cost-efficienttechnology selection. If a new facility is constructed within the life time of an oldone, its cost will be high because of the cost compensation of the old facility but thisoption can also be examined in the model.
○ Interregional transportation of energy (coal, oil natural gas, synthetic oil, ethanol,electrical power and hydrogen) and CO2 are incorporated in the model.
○ Eight types of primary energy are considered (coal, oil(conventional andunconventional), natural gas(conventional and unconventional), hydro power andgeothermal, nuclear, wind power, photovoltaics and biomass).
○ Electricity demand is modeled so that demand-supply balance is ensured; fourkinds of time periods are set based on annual load duration curves, and electricity
supply follows varying loads. This enables appropriate evaluation of electricity
system corresponding to the characteristics of individual power generation
technologies such as the base power source, the peak power source etc.
○ Various energy conversion processes (various types of electricity generation, coalgasification and liquefaction, natural gas reforming and the like) and carbon dioxide
capture, storage and sequestration (CCS) are modeled. CCS technology was
excluded from the study of the regional mitigation potentials up to 2020 andevaluated with the assumption of its availability only after 2020 so that the4Research Institute of Innovative
Technology for the Earthharmonization made among the model teams of “the Mid-term Target Explanatory
Council” of the government is maintained.
○ The total costs of the energy systems are the sum of the followings;a) Costs of bottom-up individual technologies[facilities cost]/ [payback period]+[operation and maintenance costs]1) +[annual fuel cost]1)[operation and maintenance cost] is assumed to be a certain ratio of facility costs

and the annual expense rate is assumed in the individual technology as follows;[annual expense rate] ≡1/ [payback period] + [the ratio of operation andmaintenance cost to facility cost].

b) Costs of top-down fields (loss of consumption utility)
As for other energy consumptions where technological options are notexplicitly modeled but only final energy demands of four kinds are provided
in a top-down fashion, the long-term price elasticity relationships are used
representing the rapport between the final energy price and the amount ofenergy-saving. The integrated value of the two over the evaluation time
periods can be defined as loss of consumption utility and is interpreted as
the mitigation costs of top down fields.
○ Despite the high facility costs, if the energy-saving effect is high enough and theannual fuel cost is more cost-saving than the cost increase due to ([facility costs]/[years of payback period] ) of the new facility, such options of high-cost technologies
are chosen in the case of 0 $/tCO2 marginal mitigation costs in the model. Morespecifically, measures considered having negative net costs are selected by themodel optimization for the reference case without any emissions mitigation efforts.

5Research Institute of Innovative
Technology for the Earth

Industry

Fossil fuels
Coal
Oil (conventional, unconv.)
Gas (conventional, unconv.)

Unit
production
costEnergy conv.
processes(oil refinery, coalgasification, bioethanol, gas
reforming, waterelectrolysis etc.)

Iron & steel
CementPaper & pulp
Chemical (ethylene, propylene,ammonia)

Aluminum
Cumulative production

Solid, liquid and gaseous fuels, andelectricity <Top-down modeling>

Renewable energies
Hydro power & geothermalWind power
Photovoltaics
Biomass

ElectricPower
generation

Unit
supplycost

Transport
vehicleSolid, liquid and gaseous fuels, andelectricity <Top-down modeling>

Residential & commercial
Annual production

CCS

Refrigerator, TV, air conditioner
etc.
Solid, liquid and gaseous fuels, andelectricity <Top-down modeling>

Nuclear power

Fig.1-2 Outline of energy flows in DNE21+

6

Research Institute of Innovative
Technology for the Earth

Table 1-1 Bottom-upped technologies in DNE21+
SectorElectricity

Technologies
Coal power {low efficiency (subcriticality), mid-efficiency (supercriticality), highefficiency (extra supercriticality–IGCC/IGFC), IGCC with pre-combustion CO2
capture}, Oil power {low efficiency (diesel generator, etc.), mid-efficiency(subcriticality), high efficiency (supercriticality), CHP}, Synthetic oil power {midefficiency, high efficiency}, Natural gas power {low efficiency (steam turbine),
mid-efficiency (conventional NGCC), high efficiency (high temperature NGCC), CHP,
oxy-fuel combustion}, Biomass power {low efficiency, high efficiency}, Nuclear power{conventional, next-generation (Generation IV, etc.)}, Hydro/geothermal power, Windpower, Photovoltaics, Power storage system for wind/PV, Hydrogen power, Electricalcable {conventional, superconducting high efficiency}, CCS {post-combustion
capture; applicable for coal, oil, synthetic oil, natural gas, biomass power}

Industry

Iron and steel

CementPulp
and Paper
Aluminum

ChemicalTransportation

Residential& Commercial

BF-BOF {low efficiency (small scale), mid-efficiency (large scale), high efficiency(large scale, equipped with CDQ, TRT, recovery of by-product gases),next-generation (super coke oven, eg. SCOPE 21, utilizing plastic wastes and tire
wastes, as well as highly efficient equipments), iron making by hydrogen reduction},
COG recovery {externally attachable to low/mid-efficient BF-BOF}, LDG recovery,
CDQ/TRT {externally attachable to mid-efficient BF-BOF}, Direct reduction {natural
gas base (mid/high efficiency), hydrogen gasification base}, Scrap-EAF {lowefficiency (small scale), mid-efficiency (tri-phase electric arc furnace), high efficiency(DC water-cooled walls arc furnace equipped with scrap preheating)}, CCS{applicable for BF-BOF}
Small scale facilities: Vertical kiln, Wet rotary kiln, Dry rotary kiln, SP/NSP dry rotarykiln {equipped with suspensionpreheaters (SP), or new SP (NSP) meaning
precalciner}, Advanced fluidized bed shaft furnace {equipped with SP/NSP, efficient
clinker coolers}
Large scale facilities (more efficient than small scale): Wet-process rotary kiln,
Dry-process rotary kiln, SP/NSP dry-process rotary kiln, SP/NSP dry-process rotarykiln (BAT) {equipped with efficient clinker coolers, SP with 5 or 6 levels, efficientwaste heat recovery}
Chemical pulp {low efficiency, mid-efficiency, high efficiency, next-generation}, Paper
recycling {low efficiency, mid-efficiency, high efficiency}, Milling paper {low efficiency,
mid-efficiency, high efficiency, Next-generation}, Black liquid recovery&use {lowefficiency, high efficiency}, Paper sludge boilers, Steam turbine power systems
Söderberg aluminum production, Prebake aluminum production
Ethylene/propylene: Naptha cracking {low efficiency, mid-efficiency, high efficiency,
next-generation}, Other production {ethane cracker etc. low efficiency, mid-efficiency,
high efficiency}
Ammonia: from Coal {low efficiency, mid-efficiency, high efficiency}, from Oil {lowefficiency, mid-efficiency, high efficiency}, from Natural gas {low efficiency,
mid-efficiency, high efficiency}
Types: Small passenger car, Large passenger car, Bus, Small truck, Large truck
Internal combustion engines (gasoline/diesel) {conventional internal combustion cars
(low/high efficiency), hybrid cars, plug-in hybrid cars}, Electric cars, Fuel-cell cars,
Alternative fuels {bioethanol mixed with gasoline, biodiesel mixed with diesel, CNG}
Refrigerator {low efficiency, mid-efficiency, high efficiency}, Lighting {small bulb, small
fluorescent light, small next-generation (LED etc.), medium size mid-efficient
fluorescent light, medium size high-efficient fluorescent light, medium size next
generation (LED, OLED etc.), large size mid-efficient HID, large size high-efficient
HID, large size next-generation (LED etc.)}, Television {small size low efficiency,
small size high efficiency, large size low efficiency, large size high efficiency, large
size next generation (high-efficient liquid crystal television, plasma, rear projection,
OLED)}, Air conditioner {low efficiency, mid-efficiency, high efficiency}, Gas cooking
stove {low efficiency, mid-efficiency, high efficiency}

7

Research Institute of Innovative
Technology for the Earth

3.

Assumed Key Conditions

The model is established under a number of assumed conditions. The followings are
key assumed conditions.
3.1

Assumed Population and GDP○

Population statistics and prospects from United Nations 2006 are used.

(UN,

World Population Prospects: The 2006 Revision. http://esa.un.org/unpp/)

10000

Population (million people)

8000

Latin America
AfricaMiddle East

6000

Other developing Asia
India
China

4000

Transition economies
OECD Pacific
OECD Europe

2000

OECD N. America
01975 1985 1995 2005 2015 2025 2035 2045
Year

Fig.1-3 Population projections○

GDP growth rates of the world and major countries were prepared by Japan
Economics Research Center for Mid-term Target Exploratory Council in December
2008. Based on them, GDP scenarios were made for the 54 regions.

○

From 2030 to 2050, GDP growth rates for the four regions in the IPCC SRES B2
scenario (OECD90, former Soviet Union and Eastern Europe, Asia, Other) werereferred and per-capita GDP growth rates were estimated for the 54 regions.

○Average annual rates of GDP growth from 2005 to 2020 are the followings; 1.3%
for Japan, 1.9% for US, 1.9% for EU27, 5.0% for Russia, 8.2% for China, 7.2% forIndia, and 3.0% for the whole world.

○

Population and GDP are not directly utilized to assume conditions for the energy
model but to assume the amount of production/service activities for individual8Research Institute of Innovative
Technology for the Earthsectors with bottom-upped technologies and also energy demands (IEA, 2007b)for other sectors of top-down approach without specific technology bottom-up.

GDP (trillion US$ at 2000 prices and ex. rates)

120
Latin America

100

AfricaMiddle East

80

Other developing Asia
India

60

China
Transition economies

40OECD Pacific
OECD Europe

20OECD N. America

01975 1985 1995 2005 2015 2025 2035 2045
Year

60

1200

50

1000
GDP per capita (Y2005=100)

GDP per capita (thousand US$ per capita)

Fig.1-4 GDP projections

40
30
20

OECD N. America
OECD Europe
OECD Pacific

800

Transition economies
China

600

India
Other developing Asia

400

Middle East
Africa200

1001975 1985 1995 2005 2015 2025 2035 2045
Year

Latin America

01975 1985 1995 2005 2015 2025 2035 2045
Year

Fig.1-5 per capita GDP projections

9Research Institute of Innovative
Technology for the Earth

3.2
(1)

Assumed Production/ Service Activities
Iron and steel sector○ The correlation between evolution of per-capita GDP and per-capita apparent
consumption of crude steel, trends in industry structure change by region,
government planning reports etc. were taken into account in the scenario
construction.

700

China

Statistics

India

600

Crude steel production(Mt crude steel per year)

US

500

Japan
Korea

400

Russia300

Germany
UK

200

Spain

100
0
1980

BrazilIndonesia

1990

2000

2010
2020
Year

2030

2040

2050

Fig.1-6 Crude-steel production of major regions (statistics and future scenario)(2)

Cement sector○ Cement production scenario is assumed from historical trends based on thefollowing assumptions; 1) when the regional per-capita GDP is low, the cement
production depends on the total GDP. 2) when per-capita GDP is high, theproduction depends on the population size.
○ The clinker/cement ratio is fixed across the timeframe of analysis.

10Research Institute of Innovative
Technology for the Earth

Cement Production (Mt cement per year)

1,600
1,400

China

Statistics

India
1,200
EU (15)

1,000

US

800
600

Japan

400

Brazil200

Indonesia

0
1990

2000

2010

2020
Year

2030

2040

2050

Fig.1-7 Cement production of major regions (statistics and future scenario)(3)

Transportation sector○ Road passenger transport (p-km) scenarios for passenger cars and buses are
separately assumed based on per-capita GDP and the historical trends. Also thetransition of modal share is assumed. (Fig. 1-8)
○ As for road freight transport (t-km) scenarios for cargo trucks, overall cargo service
per-capita is estimated by the GDP size, and then the transition of modal share isassumed. (Fig. 1-9)
○ As for transportation scenarios in Japan, the traffic service is assumed based on the
updated prospect by National Land and Transportation Ministry.

11

Research Institute of Innovative
Technology for the Earth

50,000AfricaTraffic service (billion p-km/yr)

Latin America
40,000

Middle East
Other Asia
India

30,000China
Oceania

20,000Japan
Former Soviet Union

10,000

East PacificWest Europe

02000

US, Canada
2010

2020

2030

2040

2050

Fig.1-8 Traffic service of passenger car by region

20,000AfricaTraffic service (billion t-km/yr)

Latin America
Middle East
15,000

Other Asia
India
China

10,000OceaniaJapan
Former Soviet Union

5,000

East PacificWest Europe
02000

US, Canada
2010

2020

2030

2040

2050

Fig.1-9 Traffic service of cargo truck by region

12Research Institute of Innovative
Technology for the Earth

3.3

Facility Introduction(1)

Iron and steel sector○ The crude steel production scenario is modeled by sorting out the processes into
three routes; basic oxygen furnace (BF-BOF), scrap-based electric arc furnace(EAF) and DRI-based electric arc furnace (DRI-EAF) where the estimation on thehistorical installation of the facilities are conducted and their results are taken into
account; installation year, energy efficiency and capacity. Fig.1-10 shows theestimated energy efficiency of BF-BOF by region at 2000 (specific energy

1
BF-BOF
0.8
0.6
0.4
0.2

Russia

India

China

Korea

Australia

Japan

Germany

FranceUK

Canada0
US

Energy consumption per unit production of crude
steel (toe/ton-cs)

consumption).

Fig.1-10 Estimated energy efficiency by region
Note 1) The less energy consumption is, the higher energy efficiency is
Note 2) Electricity is converted to primary energy using 1MWh=0.086/0.33 toe(2)Cement sector○ The model is also constructed with the estimation of the historical installation of the
facilities; installation years, energy efficiencies and capacities of the facilities.
Fig1-11 shows the energy efficiency calculated for the cement production andclinker production by region at 2000 (specific energy consumption).
○ The more adulterated clinker is, the higher the energy efficiency (per ton of) cement
production is, and the cement qualities vary by region. As discussed above, the13Research Institute of Innovative
Technology for the Earth

Energy consumption per unit production

clinker/cement ratio is fixed by region across the timeframe of analysis.
0.18

toe/ton-clinker
toe/ton-cement

0.16
0.14
0.12
0.1
0.08
0.06
0.04
0.02

Russia

India

China

Korea

Australia

Japan

Germany

FranceUK

CanadaUS0

Fig.1-11 Estimated energy efficiency by region
Note 1) The less energy consumption is, the higher energy efficiency is
Note 2) Electricity is converted to primary energy 1MWh=0.086/0.33 toe(3)Electricity generation sector○ The model is constructed based on the estimation of the historical installation of the
facilities; installation years, energy efficiencies and capacities of the facilities.

60

Coal
Oil and Gas
Average

50
40
30
20
10

Fig.1-12 Efficiency of power generation by country in 2005
Note) The higher efficiency rate is, the higher energy efficiency is

14

Russia

India

China

Korea

Australia

Japan

Germany

FranceUK

Canada0
USEfficiency of power generation (% LHV)

Fig.1-12 shows the regional efficiency of power generation by fossil fuels.

Research Institute of Innovative
Technology for the Earth

3.4

Assumed Payback Period○ The payback period is calculated from numerous kinds of factors observed in thesociety such as interest rate, income and financial leeway, subjective preference forrisk, and prospective profit rate of stockholders. In business behavior, the return on
investment (ROI) is generally 10–20%, and this means that payback period has tobe 5–10 years. Table1-2 shows the assumed payback period, considering suchsituations.
○ In the analysis by International Institute for Applied Systems Analysis using GAINS
model (IIASA, 2008), an international research similar to ours, the discount rate isdetermined 20% across the board in the standard case, corresponding to a 5 year
payback period. In the TIMER model of Netherlands Environmental Assessment
Agency, the payback period is assumed to be 1–3 years (de Vries et al., 2001).
Table 1-2 Assumed payback period
Payback period
Upper limit

Lower limit

Electricity generation sector

10

6.7

Other energy conversion sector7

4.7

Industrial sector (energy-intensive industry)

10

6.7

Transportation sector

5

3.3(Purchase of environment-conscious products)
Residential & commercial

(10)
3

2.0

Note) Payback periods are assumed within the above list for the regions dependingon their per capita GDP
Note) The upper limit is applied for Japan

3.5

Wind Power and Photovoltics○ Wind power and photovoltaics is assumed to have an annual costs decrease rate of
1.0% and 3.4%, respectively. In 2000, the unit costs of wind power is 56–
118$/MWh and photovoltaics 209–720$/MWh, depending on wind velocity andsolar radiation etc. In 2050, the unit costs of wind power and photovoltaics are
assumed to become 34–71$/MWh and 37–128$/MWh, respectively.
○ Since the peak of wind power generation does not always match the instantaneous15

Research Institute of Innovative
Technology for the Earthpeak time period of power demand, the output of wind power generation at
instantaneous peak time is assumed to be 30% of the maximum capacity of wind
power generation. Since the power generation time period of photovoltaics islimited, power supply is assumed to be possible only during the instantaneous peaktime and the peak period of time.
○ From the viewpoint of stability of the power system, the maximum useable amountof power generation by wind power and photovoltaics is each assumed to be 15%
of total power generation. However it is assumed that use of storage batteries may
expand the upper supply limit by 15% to total 30%. Wind power with storage
batteries is assumed to account for up to 60% of maximum output during
instantaneous peaks. When storage batteries are used in association withphotovoltaics, power supply is possible during medium demand periods in addition
to instantaneous peaks and peaks. The water electrolysis for hydrogen production
by photovoltaics has no upper limit, (naturally restrictions on supply of natural
resources should be treated separately).
3.6

Nuclear Power Generation○ Exogenous scenarios are assumed for nuclear power generation up to 2030.
(Table 1-3)○

Some constraints are assumed that the power generation of nuclear would becapped at 50% of the total power generation amount and that an annual expansionof conventional nuclear power generation would be 0.33%, and the expansion rateof advanced nuclear power generation would be 1%. As long as the constraints
are obeyed, costs-efficient options are selected by the model.
Table 1-3 Scenarios for nuclear power generation (TWh/yr)
2005

2020

Japan

305

437

United States

811

873

EU27

987

965

Russia

149

346

China

53

282

India

17

124

Worldwide total

2767

3677

16

Research Institute of Innovative
Technology for the Earth

4.

Model Simulation Results(1)

Regional CO2 emission outlook○ If CO2 emission intensity stayed the same in the future (if global warming mitigation
were not advanced in the future), the CO2 emissions of the entire world would
double the current emissions in 2020.
○ There are large potentials for emission reductions of negative costs, and thesepotentials should be implemented. Yet, barriers need to be removed and large
efforts are required to bring the potential into practice.

CO2 emission from fossil fuel combustion (GtCO2/yr)○ High emission growth in Non-annex I countries are estimated for the future.

80

IEA Statistics

DNE21+ Outlook

Other developing countries

Tech.-frozen
case

70

Negative cost
opportunities

60

Major developing countries

Baseline(CO2 intensity
trend 1971-2005)

50
Annex 1 & OECD

40
30

Other developing countries

20

Major developing countries

10

Annex 1 & OECD

0
1990

Negative costachieved (NCA)
case

1995

2000

2005

2010

2015

Positive costmeasures

2020

2025

2030

Fig.1-13 the world CO2 emission prospect(2)

Estimated regional potentials for emission reduction○ There are large potentials for emission reductions of negative costs and relatively
low costs (<20 $/tCO2).
○ Reduction potentials of United States below 20 $/tCO2 have a large share in thoseof Annex I & OECD.
○ Reduction potentials of China and India below 20 $/tCO2 have large shares in thoseof major developing countries.

17Research Institute of Innovative
Technology for the Earth$50-$100/tCO2
12000$20-$50/tCO2

10000$0-$20/tCO2<$0/tCO2

8000
6000
4000
2000

Other
developing
countries

Major
developing
countries

AnnexI &
OECD

India

China

Russia

Japan

EU-27

0
United States

CO2 emission reduction fromsectoral technology-frozen case [MtCO2/yr]

14000

Fig.1-14 Regional emission reduction potentials by marginal costs in 2020
○ Fig.1-15 shows potentials for sectoral and regional emission reduction in 2020.
(a) ≤ 0$/tCO2
Power sector: Fuel switching among fossil fuels

United States

Power sector: Efficiency improvement

EU-27

Power sector: Wind power

Japan

Power sector: Photovoltaics

RussiaPower sector: Other renewables
Power sector: Nuclear

China

Other enery conversion sectors

India

Iron&Steel sector

AnnexI & OECD

Cement sector

Major developing
countries
Other developing
countries

Other industries
Transportation sector
Residential&Commercial sector

0

2000

4000

6000

CO2 emission reduction [MtCO2/yr]

18

8000

Research Institute of Innovative
Technology for the Earth(b) 0–20$/t CO2
Power sector: Fuel switching among fossil fuels

United States

Power sector: Efficiency improvement

EU-27

Power sector: Wind power

Japan

Power sector: Photovoltaics

RussiaPower sector: Other renewables
Power sector: Nuclear

China

Other enery conversion sectors

India

Iron&Steel sector

AnnexI & OECD

Cement sector

Major developing
countries
Other developing
countries

Other industries
Transportation sector
Residential&Commercial sector

0

2000

4000

6000

8000

CO2 emission reduction [MtCO2/yr](c) 20–50$/t CO2

Power sector: Fuel switching among fossil fuels

United States

Power sector: Efficiency improvement

EU-27

Power sector: Wind power

Japan

Power sector: Photovoltaics

RussiaPower sector: Other renewables
Power sector: Nuclear

China

Other enery conversion sectors

India

Iron&Steel sector

AnnexI & OECD

Cement sector

Major developing
countries
Other developing
countries

Other industries
Transportation sector
Residential&Commercial sector

0

2000

4000

6000

CO2 emission reduction [MtCO2/yr]

19

8000

Research Institute of Innovative
Technology for the Earth(d) 50–100$/t CO2
Power sector: Fuel switching among fossil fuels

United States

Power sector: Efficiency improvement

EU-27

Power sector: Wind power

Japan

Power sector: Photovoltaics

RussiaPower sector: Other renewables
Power sector: Nuclear

China

Other enery conversion sectors

India

Iron&Steel sector

AnnexI & OECD

Cement sector

Major developing
countries
Other developing
countries

Other industries
Transportation sector
Residential&Commercial sector

0

2000

4000

6000

8000

CO2 emission reduction [MtCO2/yr]

Fig. 1-15 Sectoral emission reduction potentials by region in 2020
Note) Emission reduction effects by switching from coal power to gas combined power
are divided and allocated to [Power sector: efficiency improvement] (as gas
combined power is generally more efficient than coal power) and [Power sector:
fuel switching among fossil fuels].

○ Compared to EU27 and the United States, Japan is observed to have smaller
mitigation potentials. However, there are several other countries which areobserved to have small mitigation potentials in terms of the mitigation ratio relative
to 1990 such as some of EU member countries and Canada, as well as Japan.

20Research Institute of Innovative
Technology for the Earth

CO2 emission f rom f ossil f uel combustion relative to in1990 [%]

50
40

Emissions in 2005

30
20
10
0

-10
-20
-30

Annex I: ‐ 25%(CO2: -16%)

-40
-50

Fig.1-16 CO2 emissions in 2020 in case of equalization of marginal abatement costs:
GHG emission reduction in Annex I by 25% relative to 1990 levels (energy
CO2 ;16% reduction)(3)

Estimation of energy supplies in the world regions○ As to the primary energy consumption, the higher the marginal mitigation costs are,the more the energy is saved. Also, the shares of natural gas and renewable energy
are increased.

21

16000
14000
12000
10000
8000
6000
4000
2000

Photovoltaics
Wind power
NuclearHydro&Geothermal
Biomass

Y2020 ($100/tCO2)

Y2020 ($50/tCO2)

Y2020 ($20/tCO2)

Y2020 ($0/tCO2)

Y2005

0

Y1990

Primary energy supply [Mtoe/ yr]

Research Institute of Innovative
Technology for the EarthNatural gas
Crude oil
Coal

Fig.1-17 World primary energy consumption by marginal costs in 202022

Research Institute of Innovative
Technology for the EarthII Non-Energy CO2 Emissions
1.

Overview○ Exogenous scenarios which stay fixed regardless of individual mitigation options
are assumed. The followings are reasons.
<Regarding “fugitive”>·

Measures are already in progress to reduce flares.

·

References relating to cost analysis for flare reductions are not necessarily
satisfactory.

<Regarding industrial processes>
Cement sector·

Expansion of blended cement sale would enable to decrease the clinker tocement ratio. However, the blended cement requires a longer curing time and it
can not necessarily meet all the demanded requirements. It seems difficult todecrease the clinker ratio by policy induction.

·

In principle, using fly-ash as clinker feedstock would enable to reduce
limestone. However, there is a social barrier that this makes difficult to accept(take) other waste materials.

·

Application of CCS to kiln exhaust gas will enable to reduce CO2 emissions
from the production processes. However, a large amount of application of CCS
is unreasonable and impractical, considering the high cost push-up ratio to thecement cost and financial constraints observed generally in cement industries
to say nothing of those in the developing countries.

Iron and steel sector·

Measures of CCS are impractical, since CO2 emissions are dispersed acrossthe various processes in plants<Regarding waste>·

In consideration of social demands of both CO2 emission reductions andappropriate disposals of waste, it seems hard to take measures only for CO2
emission reductions.

23

Research Institute of Innovative
Technology for the Earth2.

Estimation method of CO2 emissions○

Non-energy CO2 emissions are estimated for the future, using sectoral approachand the following methodology.

○

CO2 emissions from 1990–2005 were based on UNFCCC data (2009) for Annex I
parties, and IEA Statistics (IEA, 2007a) for Non-Annex I parties.

<Regarding “fugitive”>“Fugitive” is CO2 emissions generated when hydrocarbon gas is burned as flare in oil
wells or gas fields. The amount of flare depends on the regional characteristics ofgeology or measures of effective use of and backfilling into underground of thehydrocarbon gas and not on oil or natural gas production output.
Accordingly, CO2 – fugitive for the future is estimated simply, assuming that thehistorical trends will continue in the future. This estimation means that the past efforts
including effective utilization of and backfilling of the gas will continue in the future. (Insome regions, the increasing rate of fugitive emissions is extremely high. In cases like
this, the upper limit of the increasing rate is set 2% p.a.)<Regarding industrial processes + waste>
According to the industrial processes data of UNFCCC, emissions from cement andaluminum sectors are very strongly associated with cement and primary aluminumproduction, respectively. As for 0.5 gas emissions from these sectors, emission
scenarios for the future are based on the cement and aluminum production scenarios,
assuming that the existing relationship will be sustained in the future.

24

Research Institute of Innovative
Technology for the Earth

CO2 emission by process (Mt-CO2/ yr)

50

y = 0.4376x
R² = 0.9809

40
30
20
10
0
0

20

40
60
80
100
Cement production (Mt-cement/ yr)120

Fig2-1 Cement production and CO2 emissions by process in 2005

CO2 emission by process (Mt-CO2/ yr)

7

y = 1.7138x
R² = 0.9871

6
5
4
3
2
1
0
0

1

2

3

4

Aluminium production (Mt-Al/ yr)

Fig2-2 Aluminum production and CO2 emissions by process in 2005
As factors of emissions from the industrial processes + waste of other sectors thancement and aluminum sectors are various and are affected significantly by regional andspecific circumstances, the detailed analysis is difficult using the existing statistics.
Consequently, the industrial processes + waste emission scenarios (except cement andaluminum sectors) are generated so that the scenarios may reflect the regional
circumstances, assuming the dependence on the crude steel production which may
represent the “entire physical production in industrial sectors”, and also assuming theregional circumstances (residuals which can not be represented by crude steel
production) will continue to stay.

25

Research Institute of Innovative
Technology for the Earth

3.

Emission scenario
Figs. 2-3 and 2-4 show the overview of non-energy CO2 emission scenarios.

Basically, in Annex I countries, the increase of raw material production, such as cement
and aluminum productions is assumed to be moderate in the scenario. Accordingly, theincrease in the non-energy CO2 emission scenario is gradual (or plateauing, reduced).
Canada has the highest increase rate in the developed countries, significantly affectedby the increase in fugitive emission caused by oil (including unconventional oil) andnatural gas production. Developing countries such as China and India are assumed toincreaserawmaterial

productions

andgenerally

have

sharp

increasesin

non-energy-related CO2 emissions in the scenario.
Non-energy CO2 emission (MtCO2)

4,000
3,500
3,000

Non-annex I
Annex I

2,500
2,000
1,500
1,000
500
0

1990

2000

2010

2020

2030

2040

2050

Non-energy CO2 emission (MtCO2)

Fig.2-3 Non-energy CO2 emissions up to 2050
1,000

1990
800

2005
2020

600

2050
400
200
0

Japan

US

EU-27

Russia Canada China

India

Fig.2-4 Comparison of non-energy CO2 emissions of 1990, 2005 historical data and the2020, 2050 scenarios

26

Research Institute of Innovative
Technology for the Earth

III Non-CO2GHG Assessment Model
1.

Overview
In RITE non-CO2 GHG Assessment Model, the baseline emissions are estimatedand the emission mitigation costs and mitigation potentials can also be assessed. The
model considers five kinds of emissions: CH4 (7 sectors), N2O (6 sectors), HFCs (1sector), PFC (1 sector), SF6 (1 sector) in 18 regions. To be consistent with the 54
regions in DNE21+ Model for analyses of energy CO2, the historical data of emissions in
the base year are used and allocated to the regions. Basically, the model is based on
USEPA analysis and assessment model (EPA(2002), EPA(2006)), although thehistorical data are updated.
Meanwhile, the total of GHG emissions are based on the data of UNFCCC (February,2009) for Annex I parties, and IEA statistics for Non-annex I parties. (For more details,
please refer to the top page of this document.)
100%

All GHG emissions

80%

60%
SF6
PFC

40%

HFCs
N2O

20%

CH4
Other CO2
Energy CO2

1990

World total

Non Annex I

Annex I

India

China

Russia

EU27

USA

Japan

World total

Non Annex I

Annex I

India

China

Russia

EU27

USA

Japan

0%

2005

Fig. 3-1 Shares of all GHG emissions by regionNote) Annex I countries are from UNFCCC data, Non-annex I countries from IEA data27

Research Institute of Innovative
Technology for the Earth2.

Estimation of baseline emissions
CH4 emissions are considered in 7 sectors: agriculture, oil, coal, natural gas,

residential and transportation, energy intensive industries, and other industrial sectors.
N2O emissions are considered in 6 sectors: agriculture, oil, natural gas, residential andtransportation, energy intensive industries, and other industrial sectors. HFCS, PFC andSF6 are considered in one-macro sector each. The sectoral baseline emissions are
estimated in the following way.
<CH4 from the agriculture sector>
The agriculture sector is divided into 6 groups: rice cultivation, grassland burning,
agricultural residue burning, enteric fermentation, livestock manure, land use change
and each group is estimated by region. Methane baseline emission from rice cultivation
is estimated according to the population of the relevant regions, based on EPA
estimation methods. Emissions from grassland burning are estimated based on Masui(2001) and assumed that they vary depending on the meadow areas in IPCC SRES-B2
scenario. In this case, the FAOSTAT data (FAO, 2002) are used for the regional
meadow areas for the base year. Emissions from agriculture residue burning are
estimated similarly to those from grassland burning, assuming that they vary dependingon the cultivation areas in SRES-B2 scenario. Emissions from enteric fermentation are
estimated based on EPA estimation methods and also on the grazing areas, numbers oflivestock and the livestock production ratios. Emissions from enteric fermentation are
estimated, keeping fixed emission intensity in accordance with the IPCC guideline(IPCC, 1996). But due to large uncertainties of land use, the estimation is adjusted to benearly consistent with the EPA estimation up to 2020. As for emissions from livestock
manure, the baseline is estimated similarly to those from enteric fermentation andbased on the grazing areas, numbers of livestock and the livestock production ratios.
Emissions from land use change, which may naturally be assumed to come from burnt
or rotten biomass waste, are estimated to be in inverse proportion to the conversion ofgrazing and forest areas in the SRES-B2 scenario.
<N2O from the agriculture sector>
Emissions from 7 groups are estimated by region; emissions from soil plus the same
6 groups as CH4 emissions from the agriculture sector. N2O emissions from theaforementioned 6 groups of the agriculture sector are estimated, using the same28

Research Institute of Innovative
Technology for the Earthmethods as for CH4 from the agriculture sector. As for N2O emissions from soil, the totalof 4 kinds of emissions are estimated; those from chemical fertilizer, nitrogen-fixing
plants, crop residues and others. The future emissions from chemical fertilizer are
estimated using the exponential regression formula of nitrogenous fertilizer production,using time, population and GDP per capita as explanatory variables, and also
considering the estimation methods of Tilman (2002), Masui (2001) and USDA (2003).
As for nitrogen-fixing plants, future production of soybeans and edible seeds are
estimated, using the future projection data of FAOSTAT (FAO, 2005) and IFPRI (IFPRI,
1999) up to 2015. After 2020 the baselines are estimated using the data of areas undercultivation in the SRES-B2 scenario, assuming that the crop yields will vary according to
the areas under cultivation. The emissions from crop residues are estimated from riceand oats plus the nitrogen-fixing plants mentioned above. Emissions from others are
estimated by multiplying the numbers of aforementioned livestock by the N2O emission
specific unit of nitrogen contained in livestock manure. However, due to large
uncertainties, the estimation is adjusted to be nearly consistent with the EPA estimation
up to 2020.
< CH4, N2O from the oil, coal and natural gas sectors >The estimated baselines of oil, coal, and natural gas sectors are based on GDP per
capita and the EIA estimations of production and estimation scenarios, respectively, tobe coherent with the EPA methods.
< CH4, N2O from the residential & transport sector>
Emissions from 3 groups of transportation, landfills and sewerage are considered.
As for transportation, the baseline is estimated based on GDP per capita and the EIA
estimation of production and demand scenarios for transport, being coherent with the
EPA methods. As for landfills and sewerage, being coherent with the EPA methods, thebaselines are estimated based on GDP per capita scenario.
<HFCs, PFC, SF6>
Regional baseline emissions are estimated based on the SRES-B2 4 regionalemission scenario, and regional allocation is made according to regional GDP shares.

29

Research Institute of Innovative
Technology for the Earth

14000

14000

F-gases
N2O
CH4Annex I

10000
8000
6000
4000

12000
10000
8000
6000

EPA(2006)

RITE

EPA(2006)

Y2020

Y2005

Y2000

Y1990

Y2020

Y1990

Y2020

Y2005

Y2000

Y1990

Y2020

0

Y2005

0

Y2000

2000

Y1990

2000

Y2005

4000

Y2000

12000

Non-CO2 GHG (MtCO2eq.)

Non-CO2 GHG (MtCO2eq.)

Non Annex I

RITE

Fig. 3-2 Non-CO2 GHG emission baselines of 2020
(left; Annex I and Non-annex I, right; by gas)

Non-CO2 GHG (MtCO2eq.)

2500

2000

1990
2005
RITE
EPA(2006)

1500

1000

500

0
Japan

USA

EU15

Russia

China

India

Fig.3-3 Non-CO2 GHG emission baselines of 2020 by major region (Annex I countries
are from UNFCCC data, Non-annex I countries from IEA data for historical dataof 1990 and 2005, RITE estimation)

30

Research Institute of Innovative
Technology for the Earth3.

Estimation methods of emission reduction potentials
The assessment model for non-CO2 GHG mitigation potentials is based on theassessments by EPA (2002), EPA (2006), and Hyman et al. (2002). Equation (3-1)
indicates the relationships between the individual non-CO2 GHG mitigation ratio andmarginal abatement costs by the elasticity of substitution. This model estimates
non-CO2 GHG mitigation in 18 regions when the non-CO2 GHG abatement costs areequalized to the CO2 marginal abatement costs. The elasticity is determined so that themarginal abatement costs curves correspond to EPA studies by sector and gas
calculated using technology database concerning non-CO2 GHG measures. It is not a
direct bottom-up model but basically its marginal costs and potentials are derived fromtechnological bottom-up analysis by EPA.
The elasticity of Hyman et al. (2002) is basically applied to RITE model, but theelasticity is adjusted to be consistent with the analysis result for 20%/yr discount rate,
in consideration of the sensitivity analysis results of the discount rate (payback period)
carried out by EPA (2002). The elasticity is also adjusted in consideration of the
mitigation effect report by EPA (2006) in part. Hereby, the elasticity of gases and themitigation potentials are estimated to be smaller than those of Hyman et al. (2002).

⎛
⎞
1
⎟⎟
Red_r(g, h, n, t) = 1 − ⎜⎜⎝ P(g, h, n, t) ⎠σ(g, h, n)(3-1)

g: gas, h: sector, n: region, t: year
Red_r(g,h,n,t): reduction rate in total emission
P(g,h,n,t): marginal abatement cost
σ: elasticity based on EPA(2002), EPA(2006), and Hyman et al.(2002)

31

Research Institute of Innovative
Technology for the EarthFig3-4 Comparison of EPA parameterization (squares) with methane marginal
abatement curves (diamonds) for China (top panel), and the USA (bottom
panel)
Source: Bottom-up mitigation curves were derived by combining data from IEA (1998,
1999) and U.S.EPA (1999); for details see Hyman (2002)

32

Research Institute of Innovative
Technology for the Earth

Table 3-1 Elasticity, ‘σ’ of equation (3-1) in case of discount rate of 20%/yr
Sector

Japan

US

EU

Agriculture

0.04

0.05

0.04

Oil

0.00

0.04

0.02

Natural gas

0.14

0.12

0.10

Coal

0.59

0.29

0.08

Final demand

0.20

0.19

0.15

Energy intensive industries

0.11

0.11

0.11

Others

0.11

0.11

0.11

Agriculture

0.02

0.05

0.03

Oil

0.00

0.00

0.00

Natural gas

0.00

0.00

0.00

Final demand

0.00

0.00

0.00

Energy intensive industries

0.37

0.37

0.37

Others

0.00

0.00

0.00

HFCs

0.03

0.03

0.03

PFC

0.15

0.15

0.15

SF6

0.29

0.29

0.29

Non-CO2

CH4

N2O

As for CH4 emissions from the agriculture sector, they contain emissions from ricecultivation and domestic livestock (ruminants). For rice cultivation, the mitigation options
include full midseason drainage, shallow flooding, off-season straw, use of ammonium
sulfite and upland rice. Measures against CH4 emissions from enteric fermentation of
livestock include feed improvement, antibiotics and bovine somatotropin control. As forN2O emissions from the agriculture sector, emissions from cultivated soil accounts for alarge share. The mitigation options are available such as separated application of
nitrogen fertilizer, reduction of fertilizer application and no-tillage. In agriculture, theassumed regional parameters are required to reflect regional situations since soil,
aqueous environment and climate conditions vary depending on the regions. However,substantial mitigation which inevitably accompanies cost increase is practically very
hard, as it has a large impact on food supply as well as cost increase. Thus, a relatively
low elasticity is assumed than other sectors. As developed countries have little
potentials for the future increase of agricultural products, the baseline emissions are
assumed to be nearly constant for them since the basic year, 2005.
CH4 emissions from the energy sector include 1) methane emissions in coal mining

33

Research Institute of Innovative
Technology for the Earth2) methane emissions in mining, storage and transportation of natural gas 3) methane
emissions associated with oil refinery. As for 1), the reduction options include CH4
capture from coal mines, catalytic oxidation of VAN (ventilation air methane) from coalmines, flaring methane emissions, electric generation by captured methane and so on.
As for 2), enhanced management of aging facilities in natural gas systems is included
as an option. As for 3), flaring and direct reduction can be assumed in oil refinery.
Emissions from this sector are very little in Japan. Russia emits most of CH4 fromnatural gas systems among developed countries. Consequently, this sector has large
mitigation potentials and, to be precise, large mitigation potentials of CH4 from energy
sector for a whole Annex I parties.
CH4 emissions from the residential & transportation sector include emissions fromlandfills, wastewater and sewage. Since the measures for them are relatively facile andcorresponding CDM project activities have been implemented in developing countries, alarge elasticity is assumed also in developed countries. In this model, mitigation optionsof N2O emissions from wastewater and sewage are not considered.
N2O emissions from the energy-intensive sectors arise mostly from the production of
nitric acid and adipic acid. Mitigation potentials from the baselines are considered aslarge as those in Hyman et al. (2002). The major mitigation options include emission
control equipments, process improvement and pyrolysis.
HFC emissions increased rapidly from 1990 to 2005 in developed countries except
Japan and foam insulation utilization grow in the future in many countries including
Japan. Correspondingly, emission growth in baselines is assumed to be high. As HFC
mitigation options include gas replacement, gradual decrease of emissions caused byleakage in the process of production/disposal/collection/reuse/destruction, aerosol gas
replacement (only dust blowers in Japan), recovery/leakage prevention(improvement of
capture ratio and leakage ratio) of commercial air conditioning and refrigerationequipments, gas replacement of detergent and solvent. Japan has little room of
mitigation as 100% recovery has already been accomplished for car air-conditioners
and vending machines, and so assumed for Japan.
As for mitigation of PFC and SF6, reduction effects by installation of decomposition
devices are projected in the future and so assumed. The United States and Japan
already reduced substantial SF6 of the electricity sector by the time point of 2005 andtheir baseline emissions are assumed not to grow substantially in the future.

34

Research Institute of Innovative
Technology for the Earth4.

Estimated Emission Reduction Potentials
Fig.3-5 shows RITE and EPA (2006) analyses of non-CO2 GHG emissions reduction

relative to base years by marginal reduction costs for major regions in 2020 relative to1990 and 2005. Fig.3-5 a)(relative to 1990) shows considerable differences among theregions, but Fig.3-5 b) (relative to 2005) shows only little differences. Furthermore,
Fig.3-5 a) shows considerable differences between RITE and EPA results as for Japan.
This disagreement is due to the fact that EPA uses much smaller values of emissions for1990 than the historical data reported by UNFCCC while they use larger values for 2005
than the corresponding UNFCCC data whereas RITE makes a fairly good adjustment tomeet the UNFCCC historical data.
Fig.3-6 shows emissions by marginal abatement cost and by region. Mitigation
potentials are observed fairly large up to 50 $/tCO2 for all the regions while they aresmall beyond this value.
Emissions relative to 1990
60
EPA_Annex I
50

Marginal Abatment Cost [$/tCO2]a)

EPA_EU-15
EPA_Japan

40

EPA_Russia
30

EPA_USARITE_AnnexI

20

RITE_EU-15
10

RITE_Japan

0140% 120% 100% 80% 60% 40% 20%

RITE_Russia
0% -20% -40% -60% -80%

Percentage change of non-CO2 emissions in 2020 (relative to 1990)

35

RITE_USA

Research Institute of Innovative
Technology for the Earthb) Emissions relative to 2005

Marginal Abatment Cost [$/tCO2]60

EPA_Annex I
EPA_EU-15

50

EPA_Japan
40
EPA_Russia
30

EPA_USARITE_AnnexI

20

RITE_EU-15
10
RITE_Japan
0140% 120% 100% 80% 60% 40% 20%

RITE_Russia

0% -20% -40% -60% -80%

RITE_USA

Percentage change of non-CO2 emissions in 2020 (relative to 2005)

Fig. 3-5 Comparison of marginal costs of non-CO2 GHG emissions of 2020 relative todifferent base years in EPA and RITE model

Non-CO2 GHG emission (MtCO2eq.)

12000

10000
SF6
PFCs

8000

HFCs
N2O Other sectors

6000

N2O Energy sector
N2O Other industrial sector

4000

N2O Energy-intensive sector
N2O Agricultural sector

2000

CH4 Other sectors
CH4 Energy sector

0

USA

EU27

Annex I

100[$/tCO2]

50[$/tCO2]

0[$/tCO2]100[$/tCO2]

50[$/tCO2]

0[$/tCO2]100[$/tCO2]

50[$/tCO2]

0[$/tCO2]100[$/tCO2]

50[$/tCO2]

0[$/tCO2]CH4 Other industrial sector
CH4 Energy-intensive sector
CH4 Agricultural sector

Non Annex I

Fig. 3-6 Non-CO2 GHG emissions of 2020 by marginal abatement cost and by region36Research Institute of Innovative
Technology for the EarthIV Summary of Analyses by RITE GHG Mitigation Assessment
Model: Marginal Abatement costs in Annex I parties
1.

Marginal abatement costs curves in 2020 based on 1990 GHG emissions
400

Marginal abatement cost [$/tCO2eq]

350

United States
Canada

300

EU-27

250

Japan
Russia200

Annex I

150
100
50
0
-60

-40

-20

0

20

40

60

Energy-related CO2 emission reduction relative to Kyoto 6 gases in Y1990 [% ](Energy-related CO2 emission in 1990 - Energy -related CO2 emission in 2020) / Kyoto 6 gases in 1990

Fig.4-1 Marginal cost curves of Energy-related CO2 emissions of 2020 (emission
reduction relative to 1990’s)
400

Marginal abatement cost [$/tCO2eq]

350

United States
Canada

300

EU-27

250

Japan
Russia200

Annex I

150
100
50
0
-60

-40

-20

0

20

40

60

Kyoto 5.5 gases emission reduction relative to Kyoto 6 gases in Y1990 [% ](Kyoto 5.5 gases emission in 1990 - Kyoto 5.5 gases CO2 emission in 2020) / Kyoto 6 gases in 1990

Fig.4-2 Marginal cost curves of Kyoto 5.5 gases (Non-Energy-related CO2, CH4, N2O
and F-gases) emissions of 2020 (emission reduction relative to 1990’s)

37Research Institute of Innovative
Technology for the Earth

400

Marginal abatement cost [$/tCO2eq]

350

United States
Canada

300

EU-27

250

Japan
Russia200

Annex I

150
100
50
0
-60

-40

-20

0

20

40

60

Kyoto 6 gases emission reduction relative to Kyoto 6 gases in Y1990 [% ](Kyoto 6 gases emission in 1990 - Kyoto 6 gases CO2 emission in 2020) / Kyoto 6 gases in 1990

Fig.4-3 Marginal cost curves of GHG (6 gases) emissions of 2020 (emission reduction
relative to 1990’s)
2.

Marginal abatement cost curves in 2020 based on 2005 GHG emissions
400

Marginal abatement cost [$/tCO2eq]

350

United States
Canada

300

EU-27

250

Japan
Russia200

Annex I

150
100
50
0
-30

-20

-10

0

10

20

30

40

50

60

Energy-related CO2 emission reduction relative to Kyoto 6 gases in Y2005 [% ](Energy-related CO2 emission in 2005 - Energy -related CO2 emission in 2020) / Kyoto 6 gases in 2005

Fig.4-4 Marginal cost curves of Energy-related CO2 emissions of 2020 (emission
reduction relative to 2005’s)

38Research Institute of Innovative
Technology for the Earth

400

Marginal abatement cost [$/tCO2eq]

350

United States
Canada

300

EU-27
Japan

250

Russia200

Annex I

150
100
50
0
-60

-40

-20

0

20

40

60

Kyoto 5.5 gases emission reduction relative to Kyoto 6 gases in Y2005 [% ](Kyoto 5.5 gases emission in 2005 - Kyoto 5.5 gases CO2 emission in 2020) / Kyoto 6 gases in 2005

Fig.4-5 Marginal cost curves of Kyoto 5.5 gases (Non-Energy-related CO2, CH4, N2O
and F-gases) emissions of 2020 (emission reduction relative to 2005’s)

400

Marginal abatement cost [$/tCO2eq]

350

United States
Canada

300

EU-27

250

Japan
Russia200

Annex I

150
100
50
0
-60

-40

-20

0

20

40

60

Kyoto 6 gases emission reduction relative to Kyoto 6 gases in Y2005 [% ](Kyoto 6 gases emission in 2005 - Kyoto 6 gases CO2 emission in 2020) / Kyoto 6 gases in 2005

Fig.4-6 Marginal cost curves of GHG (6 gases) emissions of 2020 (emission reduction
relative to 2005’s)

39

Research Institute of Innovative
Technology for the Earth

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Image Energy Regional (TIMER) Model-Technical Documentation, RIVM report.
EIA, 2005; International Energy Outlook 2005 Data, http://www.eia.doe.gov/oiaf/forecasting.
html
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www.epa.gov/methane/appendices.html
EPA, 2006a; Global Anthropogenic Non-CO2 Greenhouse Gas Emissions: 1990-2020.
EPA, 2006b; Global Mitigation of Non-CO2 Greenhouse Gases, http://www.epa.gov/
climatechange/economics/downloads/GlobalMitigationFullReport.pdf.
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Hyman, R.C., J.M. Reilly, M.H. Babiker, A. Valpergue De Masin and H.D. Jacoby, 2002;
Modeling non-CO2
greenhouse gas abatement , MIT Joint Program on the Science and Policy of Global
Change, Report No.24.
IEA, 2007a; CO2 emissions from fuel combustion, OECD/IEA.
IEA, 2007b; World Energy Outlook 2007, OECD/IEA.
IFPRI, 1999; Live stock to 2020- The Next Food Revolution-, IFPRI Discussion Paper 28,
http://www.ifpri.org/2020/dp/dp28.pdf.
IIASA, 2008; GAINS Model, http://gains.iiasa.ac.at/gains/EU/index.login?logout=1
IPCC, 1996; Greenhouse Gas Inventory Reference Manual. Revised 1996 IPCC Guidelines
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