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Originally published as:
Fuminori Sano, Kenichi Wada, Keigo Akimoto, Junichiro Oda (2013). Assessments of GHG emission reduction
scenarios of different levels and different short-term pledges through macro and sectoral decomposition analyses,
Technological Forecasting and Social Change, in press.
DOI:10.1016/j.techfore.2013.11.002
Available at http://www.sciencedirect.com© Elsevier

Research Institute of Innovative
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Assessments of GHG emission reduction scenarios of different levels anddifferent short-term pledges through macro and sectoral decomposition
analyses
Fuminori SANOa, Kenichi WADAa, Keigo AKIMOTOa,b and Junichiro ODAaa Systems Analysis Group, Research Institute of Innovative Technology for the Earth (RITE), 9-2 Kizugawadai,
Kizugawa-shi, Kyoto 619-0292, Japanb Graduare School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902,
Japan

Abstract
Macro- and sectoral-decomposition analyses were conducted using emission reduction scenarios from a
global energy-system model. Emission reduction scenarios with targets of 550 ppmCO2eq and 450
ppmCO2eq, which consider variations in short-term emission fixes, up until 2030, based on extensions of
the Copenhagen pledges, were selected from the AMPERE scenarios. All of the assessed emission
reduction scenarios are technologically feasible through radical transformations in energy systems. Within
the power sector, improvement of CO2 intensity requires wide deployment of carbon-dioxide capture andstorage, nuclear power, and renewable energies. In end-use sectors, not only energy intensity improvements
but also CO2 intensity improvements must be achieved by switching from fossil fuels to decarbonizedenergy by means of CO2 intensity improvements on the energy supply side. The feasibility of
improvements in CO2 and energy intensities differs between sectors according to the types of mitigation
options employed. The required carbon prices are $183/tCO2 for the 550 ppmCO2eq target and $645/tCO2
for the 450 ppmCO2eq target. When the short-term emission reduction is fixed at the level set by extensionsof the Copenhagen pledges, long-term emission reductions by 2050 are more difficult to achieve because
rapid and radical transformation of energy systems is required between 2030 and 2050.

1. Introduction
The development and deployment of climate change mitigation technologies are key measures forachieving ambitious greenhouse gas (GHG) emission reductions. It is, however, equally important toconsider how these ambitious emission reductions can be achieved under real world constraints such as
international politics and the availability of technology.
Decomposition analyses of GHG emissions proposed by Kaya [11] are frequently used by researchers
across the world, including in the Intergovernmental Panel on Climate Change (IPCC) assessment reports[9, 10], as they provide insight into the efficacy of emission reduction strategies. The Kaya approach
incorporates emissions per primary energy (carbon intensity of energy), primary energy per GDP (energy
intensity), GDP per capita, and population. At the sectoral level, the decomposition can be represented byemission per primary energy, primary energy per production (e.g., crude steel production), production percapita, and population, for example. Decreases in the GDP per capita or production per capita, andpopulation cause emission reductions and could also be considered for ambitious emission reductions;however, this paper particularly focuses on two factors, namely, the carbon intensity of energy and energy
intensity.
Improving the carbon intensity of energy includes fuel switching from coal to natural gas, deployment ofResearch Institute of Innovative
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nuclear power and renewables such as hydro power, wind power and solar, and carbon dioxide capture andstorage (CCS). These options are mainly considered as climate mitigation measures in the energy supply
sector. However, improving the carbon intensity of energy not only in the energy supply sector but also in
the energy end-use sector will also contribute to emission reductions, such as switching vehicle fuel fromgasoline or diesel to electricity or CCS introduction in the iron and steel sector. Improving the energy
intensity includes deployment of highly energy-efficient power plants in the energy supply sector, such as
ultra-supercritical coal power, integrated coal gasification combined cycle (IGCC), and advanced natural
gas combined cycle with high inlet temperature. In the energy end-use sector, various kinds of technologies
exist for improving energy intensity. For example, coke dry quenching (CDQ) and top-pressure recovery
turbines (TRT) are typical energy efficient technologies that can be used in the iron and steel sector. For
road transportation, highly efficient internal combustion engine vehicles (ICEV) and hybrid electricvehicles (HEV) are more energy efficient technologies than conventional ICEVs. Furthermore, electricvehicles (EV) and fuel-cell vehicles (FCV) are expected to improve energy efficiency and also allow fuelswitching to decarbonized energies combined with emission intensity improvements in the energy supply
sector.
To achieve ambitious emission reductions, such technologies should be evaluated not only for use in theenergy supply sector but also for the energy end-use sectors. However, there is a complex relationshipbetween the energy supply and end-use sectors for certain technologies, and these must be assessedconsistently across all sectors in the emission reduction scenarios. We have developed a technology-richglobal energy-system model, which we call DNE21+, and have conducted various analyses of climatechange mitigation [2, 3]. Studies by the IEA [6, 8] and Akashi and Hanaoka [1] were also analyzed for
global emission reduction scenarios using technology-rich global energy system models, which explicitly
considered several kinds of technologies both in the energy supply and end-use sectors. However, few
studies have accounted for the costs and achievability of technologies in the assessment of ambitious
emission reduction targets.
In this study, a subset of the AMPERE CO2 emission reduction scenarios [14] were analyzed using the
DNE21+ model and decomposition analyses of the scenarios were conducted not only on a macroeconomic
level but also on a sectoral level so as to better understand the implications of the results. The AMPERE is
a project which aims for a broad exploration of mitigation pathways and associated mitigation costs underreal-world limitations while offering insights into the differences across models and the relation tohistorical trends. 18 models including the DNE21+ model participate in the AMPERE study. The scenarios
include emission reductions to achieve 550 ppmCO2eq and 450 ppmCO2eq atmospheric concentrationlevels by 2100, which correspond to category III and category I stabilization targets in IPCC AR4 [10],respectively. In addition, variations of the short-term emission outlook to 2030 are examined in thesescenarios in order to assess current real world conditions for climate change mitigation.
Section 2 provides a description of the assessment model DNE21+ and the scenarios considered in thisstudy. Section 3.1 gives an overview of the results of the analysis for the scenarios with a macro
decomposition analysis. Section 3.2 outlines the sectoral decomposition analyses and discusses the results,
focusing on the power, iron and steel, and road transport including technology mix. Section 4 provides theconclusions of this study.
2. Assessment model
The DNE21+ model [2, 3] is used for the analysis in this paper. The model is an inter-temporal linear
programming model for assessing global energy systems and global warming mitigation. The overview of
the DNE21+ model is available in the electronic supplementary material.
2.1 Models for main sectors

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CO2 emissions from the power, iron and steel, and road transport sectors represent major contributions to
global emissions. Their CO2 emissions in 2010 are 9.7 GtCO2/y, 1.7 GtCO2/y, and 5.0 GtCO2/y,respectively, and the share of three sectors in the total CO2 emission is 53% [5]. Therefore, CO2 emission
reduction measures in these three sectors will have large impacts on global warming mitigation. In thissection, the modeling of the power, iron and steel, and road transport sectors is described.
In the power sector, both widely used technologies and novel technologies including CCS are considered[15, 16]. Technologies are explicitly modeled not only by energy source but also by generation efficiency
level. For example, a coal power plant is divided into three options: low efficiency (e.g., sub-critical),middle efficiency (e.g., critical in the present, super-critical (SC) in the future), and high efficiency (e.g.,
ultra SC in the present, IGCC and IGFC in the future). Current regional variations in generation efficiency[13] are considered as exogenous assumptions by estimating the installed capacity through technology
options in the model.
In the iron and steel sector, crude steel production by blast furnace–basic oxygen furnace (BF-BOF) andelectric arc furnace (EAF) are modeled [12]. For EAF, scrap-based EAF and direct-reduced iron
(DRI)-based EAF are considered. Several kinds of technology options for energy efficiency improvements
such as CDQ and TRT are also considered. Furthermore, CCS technologies for BF-BOF are also
considered. Crude steel production is included as an exogenous scenario by region, and the scenario isfixed for all the scenarios analyzed in this study. The world total crude steel production in 2050 is assumed
to be 2,200 million ton per year, which is almost double that in 2005.
Road transport is modeled with explicit consideration of specific technologies. The transportation service
is divided into five categories: small passenger car, large passenger car, bus, small truck, and large truck.
The transportation service is included as an exogenous scenario by region and by the five categories, andthe scenario is fixed for all the scenarios analyzed in this study. For example, the transportation service forsmall and large passenger cars in 2050 is assumed to be 31 trillion km/y, which is almost 2.7 times larger
than that in 2005. The technology options considered for passenger cars are ICEV, HEV, plug-in HEV, EV,
and FCV. In addition, alternative fuels such as bio-fuel and natural gas are also considered.
Further detail information of the iron and steel sector and road transport sector in the DNE21+ model isgiven in the electronic supplementary materials.
2.2 Emission reduction scenarios
This study analyzes a subset of AMPERE scenarios [14] (see Table 1). The Base-FullTech-OPT scenario
does not include any climate change mitigation policies, and it assumes that all of the technologies in themodel are available. The long-term emission target is defined by cumulative CO2 emissions between 2000
and 2050. The assumed cumulative CO2 emissions are 1,500 GtCO2 for achieving 450 ppmCO2eq by 2100
and 1,800 GtCO2 for achieving 550 ppm CO2eq by 2100.
In order to include current international discussions on climate change mitigation, the short-term emission
outlook is also assumed as a different GHG emission pathway to 2030, based on an extension of the
Copenhagen pledges for 2020 [14]. In this paper, emission pathways until 2030 are assumed as global
emission cap scenarios by time-point. The assumed global GHG emissions are 55 GtCO2eq/y and 61
GtCO2eq/y for 2020 and 2030, respectively, in the high estimate case. In the low estimate case, the global
emissions are 51 GtCO2eq/y and 53 GtCO2eq/y for 2020 and 2030, respectively. After 2030, global
emission cap scenarios are generated that achieve the assumed cumulative emissions between 2000 and
2050 for both the 550 and 450 ppm CO2eq scenarios. Therefore, the least-cost measures for global anduniform carbon prices across all countries are estimated by time-point.

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Table 1 Subset of AMPERE scenarios focused on in this paper.
Scenario nameLong-term target
Short-term target
Base-FullTech-OPTBase
OPT (Optimal)
450-FullTech-OPT
450 ppm CO2eq
OPT (Optimal)
450-FullTech-HST
450 ppm CO2eq
HST (High estimate case of extension of the
Copenhagen pledges)
450-FullTech-LST
450 ppm CO2eq
LST (Low estimate case of extension of the
Copenhagen pledges)
550-FullTech-OPT
550 ppm CO2eq
OPT (Optimal)
550-FullTech-HST
550 ppm CO2eq
HST (High estimate case of extension of the
Copenhagen pledges)
550-FullTech-LST
550 ppmCO2eq
LST (Low estimate case of extension of the
Copenhagen pledges)
3. Results and discussion
3.1 Macro decomposition analysis
Figure 1 shows global CO2 emissions, CO2 intensity (CO2 per total primary energy), and energy intensity(total primary energy per GDP) in the reference scenario (Base-FullTech-OPT) and in the emission
reduction scenarios. In this paper, GDP is expressed in market exchange rates (MER). In theBase-FullTech-OPT scenario, CO2 emissions increase toward 2050. The CO2 emissions in 2050 are 71
GtCO2/y, which are almost double those in 2005. CO2 intensity remains almost constant until 2050,
whereas energy intensity improves toward 2050. The annual average improvement in energy intensity from2005 to 2050 is 1.2%/y, which is fairly consistent with historical changes, e.g., the improvement rate from1980 to 2000 was 1.2%/y [7] globally.
a

CO2 emission [GtCO2/y]80
70

Base-FullTech-OPT

60450-FullTech-OPT

50450-FullTech-HST

40450-FullTech-LST30550-FullTech-OPT

20

550-FullTech-HST

10
0
2005

550-FullTech-LST
2010

2015

2020

2025 2030
Year

2035

2040

2045

2050

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CO2 per Total Primary Energy[Y2005 = 1]

1.2
1
0.8

Base-FullTech-OPT
450-FullTech-OPT
450-FullTech-HST

0.6450-FullTech-LST

0.4550-FullTech-OPT

0.2

550-FullTech-HST

02005 2010 2015 2020 2025 2030 2035 2040 2045 2050
Year

550-FullTech-LSTcTotal Primary Energy per GDP[Y2005 = 1]

1.2
1
0.8
0.6
0.4
0.2

Base-FullTech-OPT
450-FullTech-OPT
450-FullTech-HST
450-FullTech-LST
550-FullTech-OPT
550-FullTech-HST
550-FullTech-LST02005 2010 2015 2020 2025 2030 2035 2040 2045 2050
Year

Figure 1 Global CO2 emissions, CO2 intensity (CO2 per total primary energy), and energy intensity (total
primary energy per GDP) in Base-FullTech-OPT scenario and emission reduction scenarios. (a) CO2
emission. (b) CO2 intensity (CO2 per total primary energy). (c) Energy intensity (total primary energy per
GDP).
In the emission reduction scenarios, large reductions in CO2 emissions are required from theBase-FullTech-OPT scenario in order to meet the assumed targets. In 2050, the required CO2 emission
reductions from the Base-FullTech-OPT scenario are varied from 45 GtCO2/y to 68 GtCO2/y. CO2
emissions in 2050 relative to 2000 vary from -12% to -48% for the 550 ppm CO2eq scenarios and from-46% to -90% for the 450 ppm CO2eq scenarios, respectively. These emissions are almost consistent withthe emission range in the study by van Vuuren and Riahi [17]. They classified characteristics of the CO2
emission pathways of new scenario literature published after IPCC AR4 in line with the categories of IPCC
AR4. However, our upper limits (-48% for the 550-FullTech-HST scenario and -90% for the
450-FullTech-HST scenario) are lower than the emission range of the IPCC categories III and I reported invan Vuuren and Riahi [17]. Both CO2 intensity and energy intensity must be improved significantly toachieve the CO2 emission reductions. The CO2 intensities in both the 550-FullTech-OPT scenario and the450-FullTech-OPT scenario improve continuously toward 2050, and the intensities in 2050 are 0.46 and0.30 of the 2005 value, respectively. The annual average improvement rates of the CO2 intensity from 2005

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to 2050 are 1.7%/y and 2.7%/y. Until 2030, the scenarios with assumed short-term emission fixes show
slower improvements in CO2 intensity than the scenarios without short-term CO2 fixes. After 2030, rapid
improvements in CO2 intensity are required to meet the long-term targets by 2050. For the 550 ppmCO2eq
scenarios, the annual average improvement in CO2 intensity from 2030 to 2050 is 5.7%/y with theshort-term emission fix by the high estimate, and 3.6%/y with the low estimate. For the 450 ppmCO2eq
scenarios, the corresponding values are 14.3%/y and 9.8%/y. These annual average improvement rates are
significantly higher than those in historical data or in the Base-FullTech-OPT scenario.
The energy intensities in both 550-FullTech-OPT and 450-FullTech-OPT scenarios also improve with time.
The energy intensities in 2050 in the 550-FullTech-OPT and the 450-FullTech-OPT scenarios are 0.46 and0.44 times the 2005 value, respectively. The annual average improvement rates of the energy intensity from2005 to 2050 for the two scenarios are 1.7%/y and 1.8%/y, respectively. The difference in the energy
intensities in 2050 between the six emission reduction scenarios is smaller than that in the CO2 intensities.
There are opportunities for energy intensity improvements in the reference scenario and the 550
ppm-CO2eq scenario. However, additional cost-effective measures to improve the energy intensities of the
550 ppm-CO2eq and 450 ppm-CO2eq scenarios are limited. In comparison to historical trends of global
CO2 intensity improvement, e.g. 0.5%/y between 1980 and 2000 [5, 7], a huge contribution from CO2
intensity improvements to reduce CO2 emissions is required between 2030 and 2050, which is the period
during which large CO2 emission reductions will be required.

80
Others60
40

Residential and commercial sector

20

Transportation sector

0

2030

550-FullTech-LST

550-FullTech-HST

550-FullTech-OPT

450-FullTech-LST

450-FullTech-HST

450-FullTech-OPT

Base-FullTech-OPT

550-FullTech-LST

550-FullTech-HST

550-FullTech-OPT

450-FullTech-LST

450-FullTech-HST

Industrial sector

450-FullTech-OPT-20

Base-FullTech-OPT

CO2 Emission [GtCO2/y]

3.2 Sectoral decomposition analysis
Huge improvements in the CO2 intensity and the energy intensity are required to achieve the emission
reduction targets assumed in the scenarios, as shown in the previous section. Decomposition analyses at themacroeconomic level provide the general direction of the assumed targets. However, a more detailed
understanding on how they can be achieved in each sector is also important. In this section, the sectoral
emission reduction measures and the sectoral decomposition analyses are described.
Figure 2 shows the sectoral CO2 emissions in 2030 and 2050 in the reference and emission reduction
scenarios. In the Base-FullTech-OPT scenario, CO2 emissions from the power sector are largest, accountingfor 23 GtCO2/y and 28 GtCO2/y in 2030 and 2050, respectively, which is almost 40% of the total in theboth years. The CO2 emissions from the energy end-use sectors (industrial, transportation, and residential
and commercial sectors) are 28 GtCO2/y in 2030 and 34 GtCO2/y in 2050. Within the energy end-use
sectors, the CO2 emissions from the industrial sector are the largest.

Other energy supply sector
Power sector

2050

Figure 2 Sectoral CO2 emissions in 2030 and 2050 in the Base-FullTech-OPT scenario and the emission
reduction scenarios (World total).

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In the emission reduction scenarios, CO2 emissions from the power sector are significantly reduced
relative to the Base-FullTech-OPT scenario. They are almost zero for the 550 ppmCO2eq scenarios in 2050
and negative for the 450 ppmCO2eq scenarios. To improve the CO2 intensity, technology mixes in the
power sector are significantly different to the Base-FullTech-OPT scenario. Large CO2 emission reductions
in energy end-use sectors are also observed to achieve the assumed emission reduction targets. CO2
emissions from the energy end-use sectors for the 450 ppmCO2eq scenarios are 11–19 GtCO2/y in 2050,
and their emission reduction rates relative to the Base-FullTech-OPT scenario are 45%–68%. Significant
changes to energy systems are required to achieve such ambitious emission reductions, not only in theenergy supply sector but also in the end-use sector. Furthermore, a large amount of CO2 emission reduction
through CO2 fixation in 2050 will be required as negative CO2 emissions from others seen in Figure 2.
In the following part of this section, quantitative analyses of the CO2 and energy intensities andtechnological measures for the power and iron and steel sectors, and the passenger cars road transport aredescribed.
3.2.1 Power sector
Figure 3 shows global CO2 emissions, CO2 intensity (CO2 per electricity generation), and energy intensity(electricity generation energy per GDP) for the power sector in the reference scenario (Base-FullTech-OPT)and the emission reduction scenarios. As shown in Figure 2, CO2 emissions from the power sector in theemission reduction scenarios are drastically reduced by 2050. Radical emission reductions after 2030 are
required especially for the scenarios in which the short-term emissions are constrained. The improvements
in CO2 intensity are considerably larger than the improvements in energy intensity, while both intensities
must be improved to achieve the targeted emission reductions. On the other hand, the energy intensities in
the 450-FullTech-HST and the 450-FullTech-LST scenarios are worse after 2040. This is because (1) the
expansion of CCS requires a lot of power to capture CO2, (2) electrolytic hydrogen production is developed
to partly meet the demand for liquid energies in the transportation sector and natural gas demand in theindustrial, residential, and commercial sectors, and (3) electrical technologies in various sectors, including
EVs in the road transport sector, are widely deployed. The latter two reasons have a large effect on large
CO2 emission reductions, which rely on the low CO2 intensity of electricity.
a

CO2 emission [GtCO2/y]35
30

Base-FullTech-OPT

25450-FullTech-OPT

20450-FullTech-HST

15450-FullTech-LST

10550-FullTech-OPT

5550-FullTech-HST

0550-FullTech-LST-52005 2010 2015 2020 2025 2030 2035 2040 2045 2050
Year

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CO2 per Electricity Generation[Y2005 = 1]

b
1.2
1
0.8
0.6
0.4
0.2
0

Base-FullTech-OPT
450-FullTech-OPT
450-FullTech-HST
450-FullTech-LST
550-FullTech-OPT
550-FullTech-HST
550-FullTech-LST-0.22005 2010 2015 2020 2025 2030 2035 2040 2045 2050
Yearc
Electricity generation per GDP[Y2005 = 1]

1.2
1
0.8

Base-FullTech-OPT
450-FullTech-OPT
450-FullTech-HST

0.6450-FullTech-LST

0.4550-FullTech-OPT

0.2

550-FullTech-HST
550-FullTech-LST02005 2010 2015 2020 2025 2030 2035 2040 2045 2050
Year

Figure 3 Global CO2 emissions, CO2 intensity (CO2 per electricity generation), and energy intensity(electricity generation per GDP) for power sector in Base-FullTech-OPT scenario and emission reduction
scenarios. (a) CO2 emission. (b) CO2 intensity (CO2 per electricity generation). (c) Energy intensity(Electricity generation per GDP).
The electricity generation mixes in 2030 and 2050 are shown in Figure 4, and differences are seen
between the emission reduction scenarios and the Base-FullTech-OPT scenario to achieve improvements in
the CO2 intensities, as shown in Figure 3. In 2030, the measures used to improve the CO2 intensities arefuel switching from coal to gas, deployment of CCS, and expansion of zero-emission technologies such
nuclear power, wind power, and solar. Efficiency improvements of fossil-fuel power plants through thewidespread deployment of high-efficiency fossil-fuel power plants are also undertaken as cost effective
measures. In 2050, improvements to the CO2 intensities from CCS and the zero-emission power supply
technologies become larger than in 2030. Many fossil-fueled power plants, particularly coal power plants
with low efficiency, cease operating before the end of their assumed lifetime (40 years) to realize rapid CO2
intensity improvements by the deployment of zero-emission technologies.

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550-FullTech-LST

OthersSolar
Wind
Hydro & Geothermal
Biomass w/ CCS
Biomass w/o CCS
NuclearGas w/ CCS
Oil w/ CCS
Coal w/ CCS
Gas w/o CCS
Oil w/o CCS
Coal w/o CCS

550-FullTech-LST

550-FullTech-HST

550-FullTech-OPT

450-FullTech-LST

450-FullTech-HST

450-FullTech-OPT

140
120
100
80
60
40
20
0

Base-FullTech-OPT

Electricity generation [EJ/y]a

OthersSolar
Wind
Hydro & Geothermal
Biomass w/ CCS
Biomass w/o CCS
NuclearGas w/ CCS
Oil w/ CCS
Coal w/ CCS
Gas w/o CCS
Oil w/o CCS
Coal w/o CCS

550-FullTech-HST

550-FullTech-OPT

450-FullTech-LST

450-FullTech-HST

450-FullTech-OPT

350
300
250
200
150
100
50
0

Base-FullTech-OPT

Electricity generation [EJ/yr]b

Figure 4 Electricity generation mix in Base-FullTech-OPT scenario and emission reduction scenarios in2030 and 2050 (World total). (a) 2030. (b) 2050.
Note: Others include hydrogen power generation and PV generation for electrolysis without gridconnection.
3.2.2 Iron and steel sector
Figure 5 shows the global CO2 emissions, CO2 intensity (CO2 per final energy consumption), and energy
intensity (final energy consumption per crude steel production) from the iron and steel sector in theBase-FullTech-OPT scenario and the emission reduction scenarios. CO2 emissions include indirect CO2
emissions from electricity consumption. As mentioned in Section 2.1, the assumed global crude steel
production grows with time and by 2050 is almost double that in 2005. CO2 emissions increase toward2050. CO2 emissions from the iron and steel sector in the Base-FullTech-OPT scenario are 3.4 GtCO2/y in
2050, which is 1.5 times larger than the corresponding value in 2005. Although the CO2 intensity remains
almost constant until 2050, the energy intensity is improved by the deployment of highly energy efficientplants.

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CO2 Emission [GtCO2/y]4
3.5

Base-FullTech-OPT

3450-FullTech-OPT

2.5

450-FullTech-HST

2450-FullTech-LST

1.5550-FullTech-OPT

1

550-FullTech-HST

0.5550-FullTech-LST02005

CO2 per Energy Consumption[Y2005 = 1]

b

c

2010

2015

2020

2025 2030
Year

2035

2040

2045

2050

1.2
1
0.8
0.6
0.4
0.2

Base-FullTech-OPT
450-FullTech-OPT
450-FullTech-HST
450-FullTech-LST
550-FullTech-OPT
550-FullTech-HST
550-FullTech-LST02005 2010 2015 2020 2025 2030 2035 2040 2045 2050
Year

Energy Consumption per
Production [Y2005 = 1]

1.2
1
0.8

Base-FullTech-OPT
450-FullTech-OPT
450-FullTech-HST

0.6450-FullTech-LST

0.4550-FullTech-OPT

0.2

550-FullTech-HST
550-FullTech-LST02005 2010 2015 2020 2025 2030 2035 2040 2045 2050
Year

Figure 5 Global CO2 intensity (CO2 per energy consumption) and energy intensity (energy consumption
per crude steel production) for crude steel production in Base-FullTech-OPT scenario and emission
reduction scenarios. (a) CO2 emission. (b) CO2 intensity (CO2 per energy consumption). (c) Energy
intensity (Energy consumption per crude steel production).
Note: Indirect CO2 emission from electricity consumption is included.

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The CO2 intensities in the emission reduction scenarios are drastically improved compared to those in theBase-FullTech-OPT scenario, while the energy intensities improve steadily, and the differences between
scenarios are not very large. Technological measures to facilitate drastic CO2 intensity improvements
include widespread deployment of CCS for BF-BOF and expansion of highly efficient EAF combined withdecarbonization in the power sector, as mentioned above. In 2050, CCS is deployed for almost all BF-BOF,
as shown in Figure 6, not only in the 450 ppmCO2eq scenarios but also in the 550 ppmCO2eq scenarios. In
the 450-FullTech-HST and 450-FullTech-LST scenarios, after 2030, the CO2 intensity improvements arequite rapid to meet the total CO2 emission reductions, as shown in Figure 3. Steel production plants areusually operated for a long time, and the lifetime is assumed as 40 years in the DNE21+ model. Such rapid
emission reductions require the operation of many low efficiency steel production plants to be ceased
earlier than this, and that higher efficiency plants be introduced to improve the CO2 intensity in the short
term.

Scrap-based EAF (High-eff.)

80

Scrap-based EAF (Middle-eff.)60

Scrap-based EAF (Low-eff.)

40

DRI EAF (High-eff.)
DRI EAF (Middle-eff.)

550-FullTech-LST

550-FullTech-HST

550-FullTech-OPT

450-FullTech-LST

450-FullTech-HST

0450-FullTech-OPT

20

100

BF-BOF (High-eff. with next generation cokeoven) with CCS
BF-BOF (High-eff.) with CCS
BF-BOF (High-eff. with next generation cokeoven)
BF-BOF (High-eff.)
BF-BOF (Middle-eff.)
BF-BOF (Low-eff.)

Scrap-based EAF (High-eff.)

20

DRI EAF (Middle-eff.)

0550-FullTech-LST

DRI EAF (High-eff.)

550-FullTech-HST

40

550-FullTech-OPT

Scrap-based EAF (Low-eff.)

450-FullTech-LST60450-FullTech-HST

Scrap-based EAF (Middle-eff.)

450-FullTech-OPT

80

Base-FullTech-OPT

Crude Steel Production [%]b

100

Base-FullTech-OPT

Crude Steel Production [%]a

BF-BOF (High-eff. with next generation cokeoven) with CCS
BF-BOF (High-eff.) with CCS
BF-BOF (High-eff. with next generation cokeoven)
BF-BOF (High-eff.)
BF-BOF (Middle-eff.)
BF-BOF (Low-eff.)

Figure 6 Technology mix for crude steel production in Base-FullTech-OPT scenario and emission
reduction scenarios in 2030 and 2050 (World total). (a) 2030. (b) 2050.

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3.2.3 Passenger cars road transport
Figure 7 shows global CO2 emissions, CO2 intensity (CO2 per final energy consumption), and energy
intensity (final energy consumption per transportation service) of passenger cars in the Base-FullTech-OPT
scenario and the emission reduction scenarios. The assumed level of transportation by passenger car grows
toward 2050 and is 2.7 times greater than that in 2005, as mentioned in Section 2.1. CO2 emissions in theBase-FullTech-OPT scenario increase according to this growth. However, CO2 emissions in 2050 are only
twice those in 2005 as a result of predicted improvements in energy intensity. Improved energy intensity inthe Base-FullTech-OPT scenario arises from presumed technological advances in the road transport sector
which, even in that scenario, are driven by factors such as strengthened fuel economy policies and higher
oil prices. On the contrary, the CO2 intensity remains almost constant.
In the emission reduction scenarios, both of the intensities improve. Significant improvement in the CO2
intensity is achieved through switching from conventional fossil fuels to bio-fuel, electricity, and hydrogen.
The CO2 intensity of electricity is substantially improved by decarbonization in the power sector, as
previously mentioned. Hydrogen is produced by electrolysis with decarbonized electricity or natural gas
reforming with CCS, and therefore, the CO2 intensity of hydrogen production is also low. An application of
CCS for bio-fuel production is not considered in this study and the future assessments are important. Thevery low CO2 intensities in the 450-FullTech-HST and 450-FullTech-LST scenarios after 2030 are achievedby a huge expansion of those three energy carriers. The energy intensity is improved through the
deployment of highly efficient vehicles. In 2030, ICEVs have the largest share in the passenger car market,
followed by HEVs, as shown in Figure 8. In 2050, further deployment of HEVs contributes to the energy
intensity improvement. Furthermore, plug-in HEVs, EVs, and FCVs also contribute to that improvement.
In the 450-FullTech-HST and 450-FullTech-LST scenarios, which require more significant emission
reductions in 2050, EVs represent the largest share in the passenger car market, and substantial
improvements in the energy intensity are realized. Compared with fossil-fuel power plants and steel
production plants, the lifetime of a passenger car is shorter (the lifetime is assumed to be 10–20 years,
depending on the region). However, changes in the production systems of automobile manufacturers forvarious kinds of vehicle and infrastructure transition from conventional liquid fuel to electricity andhydrogen are required to realize such a technology-mix transition in passenger cars, and this will depend on
consumer demand and preference. Therefore, rapid transition of the technology mix of passenger cars also
faces real world difficulties, as in the other sectors.
a

CO2 Emission [GtCO2/y]65Base-FullTech-OPT
450-FullTech-OPT

4450-FullTech-HST

3450-FullTech-LST
550-FullTech-OPT

2

550-FullTech-HST
550-FullTech-LST

1
0
2005

2010

2015

2020

2025 2030
Year

2035

2040

2045

2050

Research Institute of Innovative
Technology for the Earthb
CO2 per Energy Consumption[Y2005 = 1]

1.2
Base-FullTech-OPT

1450-FullTech-OPT

0.8450-FullTech-HST
450-FullTech-LST

0.6550-FullTech-OPT

0.4

550-FullTech-HST
550-FullTech-LST

0.202005 2010 2015 2020 2025 2030 2035 2040 2045 2050
Yearc

Energy Consumption perTransportation Service[Y2005 = 1]

1.2
1

Base-FullTech-OPT
450-FullTech-OPT

0.8450-FullTech-HST
450-FullTech-LST

0.6550-FullTech-OPT

0.4

550-FullTech-HST
550-FullTech-LST

0.202005 2010 2015 2020 2025 2030 2035 2040 2045 2050
Year

Figure 7 Global CO2 intensity (CO2 per energy consumption) and energy intensity (energy consumption
per transportation service) for passenger cars in Base-FullTech-OPT scenario and emission reduction
scenarios. (a) CO2 emission. (b) CO2 intensity (CO2 per energy consumption). (c) Energy intensity (Energy
consumption per transportation service).
Note: Indirect CO2 emission from electricity consumption is included.

100

FCV (Fuel Cell Vehicle)

80
60

EV (Electric Vehicle)

40
Plug-In HEV
550-FullTech-LST

550-FullTech-HST

550-FullTech-OPT

450-FullTech-LST

450-FullTech-HST

0450-FullTech-OPT

20
Base-FullTech-OPT

Passenger Car [%]a

HEV (Hybrid Electric
Vehicle)ICEV (Internal
Combustion Engine
Vehicle)Research Institute of Innovative
Technology for the Earth100

FCV (Fuel Cell Vehicle)

80
60

EV (Electric Vehicle)

4020
550-FullTech-LST

550-FullTech-HST

550-FullTech-OPT

450-FullTech-LST

450-FullTech-HST

450-FullTech-OPT

0Plug-In HEV
Base-FullTech-OPT

Passenger Car [%]b

HEV (Hybrid Electric
Vehicle)ICEV (Internal
Combustion Engine
Vehicle)Figure 8 Technology mix for passenger cars in Base-FullTech-OPT scenario and emission reduction
scenarios in 2030 and 2050 (World total). (a) 2030. (b) 2050.
3.2.4 Cross-sectoral analysis
Figure 9 shows the correlation between the reduction in energy intensity and the reduction in carbon
intensity in the Base-FullTech-OPT scenario, the 550-FullTech-OPT scenario, and the 450-FullTech-OPT
scenario for all sectors, and for the power sector, iron and steel sector, and passenger cars road transport
separately. The CO2 intensity improvements in the power sector for achieving the assumed long-termemission reduction targets are substantially larger than those in the other sectors because various kinds oftechnology options such as CCS, nuclear power, and renewable energies are available. In the iron and steel
sector, the energy intensity improvement in the emission reduction scenarios is not so large compared withthat in the Base-FullTech-OPT scenario, and the emission reductions tend to rely on CO2 intensity
improvements. The trend is the same as that in the power sector, but the level of CO2 intensity
improvements is smaller due to the limitations of the technological options. On the other hand, the energy
intensity improvement for the passenger car contributes more to the CO2 emission reduction than the CO2
intensity improvement through the deployment of more efficient vehicles such as HEVs, as shown in
Figure 8. CO2 intensity improvement is more difficult than energy intensity improvement according tohistorical data. Measures to accelerate the deployment of highly efficient vehicles (e.g., regulation ofenergy efficiency) may be practical in the real world.
The assumption of future technological advances is a key factor that affects sectoral energy and CO2
intensity improvements. Further consideration and analysis (e.g., sensitivity analysis) of futuretechnological advances will be needed, although the model considers several kinds of innovativetechnologies. Analyses of inter-sectoral impacts of efficiency improvement measures are also important; forexample, the substitution of steel in vehicles by other materials will influence emissions from both the roadtransport and the iron and steel sectors.

Research Institute of Innovative
Technology for the Earth

Reduction in carbon intensity compared to2005 [%]120

All sector (Base-FullTech-OPT)All sector (450-FullTech-OPT)100

All sector (550-FullTech-OPT)
Power sector (Base-FullTechOPT)
Power sector (450-FullTech-OPT)

80
60

Power sector (550-FullTech-OPT)

40
20
0
-20

-20

0
20
40
60
80
100
Reduction in energy intensity compared to 2005 [%]120

Iron and steel sector (BaseFullTech-OPT)
Iron and steel sector (450FullTech-OPT)
Iron and steel sector (550FullTech-OPT)
Passenger car sector (BaseFullTech-OPT)
Passenger car sector (450FullTech-OPT)
Passenger car sector (550FullTech-OPT)

Figure 9 Correlation between reduction in energy intensity and reduction in carbon intensity in
Base-FullTech-OPT scenario, 550-FullTech-OPT scenario, and 450-FullTech-OPT scenario.
3.2.5 Carbon price and effects of short-term emission fixes
Figure 10 shows the carbon price for the emission reduction scenarios. The carbon prices in 2020, 2030,
and 2050 are $23/tCO2, $52/tCO2, and $183/tCO2, respectively, in the 550-FullTech-OPT scenario. The
carbon price in the 450-FullTech-OPT scenario is much higher than that in the 550-FullTech-OPT scenario.
The carbon prices in 2020, 2030, and 2050 are $46/tCO2, $156/tCO2, and $645/tCO2, respectively. IEA [6,8] and Akashi and Hanaoka [1] evaluated the carbon price for achieving huge emission reductions,corresponding to 450 ppmCO2eq targets, using a technology-rich model. The carbon price in 2050 as aresult of halving global emissions relative to 2005 is $175/tCO2 in IEA ETP 2010 [8]. The previous
analysis for the same emission reduction target by IEA ETP 2008 [6] showed that the price in the most
optimistic and pessimistic cases is $200/tCO2 and $500/tCO2, respectively. The study by Akashi and
Hanaoka [1] estimates that the carbon price in 2050 for halving global emissions relative to 1990 is$600/tCO2. The estimated carbon price for achieving such large emission reduction targets is very highbased on these studies and also on this study. In addition, uniform carbon pricing across all countries isassumed in these analyses, so that emission reduction is conducted by the lowest cost measures. However,this assumption is unrealistic in the real world, and the economic impact will be larger than that estimatedin this analysis.
The impact of the assumed short-term emission fixes based on the extensions of the Copenhagen pledges
for 2020 on carbon price is very large. Rapid transformation of energy systems after 2030 is required tomeet the assumed CO2 emission budget, which is an indicator of the GHG concentration targets. Thus,
carbon prices in the emission reduction scenarios with the assumed short-term emission fixes increase
greatly after 2030. For the 550 ppmCO2eq scenarios, carbon prices in 2050 are $333/tCO2 for the
550-FullTech-LST scenario and $1095/tCO2 for the 550-FullTech-HST scenario, which are about 2 and 6
times larger than that in the 550-FullTech-OPT scenario. The impact of the assumed short-term emission
fixes in the 450 ppmCO2eq scenarios is more serious than in the 550 ppmCO2eq scenarios, because larger
emission reductions are needed after 2030. Carbon prices in 2050 for the 450-FullTech-HST scenario andthe 450-FullTech-LST scenario are $7235/tCO2 and $3866/tCO2, respectively. Such high carbon prices are
required for the radical transformation of energy systems needed to achieve rapid CO2 emission reductions.

Research Institute of Innovative
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Carbon Price [$/tCO2]

1000
800
600
400
200

450-FullTech-OPT
450-FullTech-HST
450-FullTech-LST
550-FullTech-OPT
550-FullTech-HST
550-FullTech-LST02005 2010 2015 2020 2025 2030 2035 2040 2045 2050
Year

Figure 10 Carbon prices in emission reduction scenarios.
Note: Carbon prices in 2050 of the 450-FullTech-HST scenario, the 450-FullTech-LST scenario, and the550-FullTech-HST scenario are $7235/tCO2, $3866/tCO2, and $1095/tCO2.
3.2.6 Discussion
In this paper, a decomposition analysis of various emission reduction scenarios was conducted using the
DNE21+ model, which is a technology-rich energy system model that treats all sectors consistently. All of
the evaluated emission reduction scenarios are technologically feasible. However, a radical transformationof energy systems is required to achieve the ambitious emission reductions, especially for the scenarios
according to the emission outlooks based on the extensions of the Copenhagen pledges. The required
carbon prices for such radical energy system transformation are very high, especially for the 450
ppmCO2eq target, as shown in Figure 10. The acceptance of such carbon prices in the real world isuncertain. Furthermore, an ideal allocation of emission reductions by region and sector is assumed in thispaper. In the short term at least, uniform carbon pricing across all countries will be very difficult, and large
differences in carbon prices by country for the Copenhagen pledges are projected [18]. CO2 and energy
intensities in various sectors differ by region at this time [13], and it is difficult to see how the mostefficient intensities will be achieved under such fragmented regional circumstances, including carbon price;
thus, achieving the required radical transformation in the energy system across the world will be difficult.
The assumed short-term emission fixes based on the extensions of the Copenhagen pledges have a large
impact on the long-term targets for 2050. For the 450 ppmCO2eq target, carbon prices will jump up to
several thousand dollars per ton CO2 after 2030, which not seems to be practical solutions. On the other
hand, large emission reductions will be difficult in the near term under the conditions of current
international discussions on climate policies including the Copenhagen pledges, although large emission
reductions by 2030 are required in the 450-FullTech-OPT scenario.
A stringent stabilization level, such as the 450 ppmCO2eq target, requires radical transformation of energy
systems both in the energy supply and end-use sectors and could have a huge negative impact on theeconomy, which will have trade-offs with other sustainable goals, such as indicators of income, poverty,
food access, etc. [4]. More flexible targets or approaches will be important to explore more practical andfeasible solutions at a global level.
4. Conclusions
A decomposition analysis of emission reduction scenarios was conducted at the macroeconomic level andalso at the sectoral levels using a technology-rich model, DNE21+.
All of the assessed emission reduction scenarios considered in this study for the 550 ppmCO2eq and 450
ppmCO2eq targets with and without short-term emission fixes are technologically feasible by implementingResearch Institute of Innovative
Technology for the Earthradical transformations of global energy systems both in the energy supply sector and end-use sectors,
including the early shutdown of inefficient plants and the widespread deployment of CCS, nuclear power,
and renewable energies such as solar. In energy end-use sectors, energy intensity improvements areaccelerated, and CO2 intensity improvements play a significant role for greater emission reductions. CO2
intensity improvements can be achieved within each sectors but require the widespread adoption of
decarbonized energies in the energy supply sector through CO2 intensity improvements. Feasibilities ofimprovements in CO2 and energy intensities are different by sector because of differences in types of
mitigation options across sectors. Power sector has various kinds of technological options to improve theCO2 intensity, and the potentials of the CO2 intensity improvement are larger than those in the iron andsteel sector and passenger cars. For the energy intensity improvements, passenger cars have larger
potentials through deployment highly efficient vehicle than those in the iron and steel sector.
The carbon prices resulting from the assumed emission reductions, particularly for the 450 ppmCO2eq
target, are very high ($645/tCO2 in 2050 for the 450 ppmCO2eq) and could negatively affect the economy,
even if the lowest cost measures are assumed.
When the short-term emission pathways are fixed at the levels of the extensions of the Copenhagen
pledges, the predicted carbon prices after 2030 for the 450 ppmCO2eq target are several thousand dollars
per ton CO2. According to our model, there are technologically feasible solutions, but they would be reallydifficult to carry out in the real world. Although greater short-term emission reductions than those in theextensions of the Copenhagen pledges could reduce the impact of this, the 450 ppmCO2eq pathways will
still lead to high carbon prices in the real world.
References[1] O. Akashi and T. Hanaoka, Technological feasibility and costs of achieving a 50% reduction of global
GHG emissions by 2050: mid- and long-term perspectives, Sustain. Sci., 7(2) (2012) 139-156.
[2] K. Akimoto et al., Global emission reductions through a sectoral intensity target scheme, Clim. Policy, 8(2008) S46-S59.
[3] K. Akimoto et al., Estimates of GHG emission reduction potential by country, sector, and cost, Energy
Policy, 38(7) (2010) 3384-3393.
[4] K. Akimoto et al., Consistent assessments of pathways toward sustainable development and climate
stabilization, Nat. Resour. Forum, 36(4) (2012) 231-244.
[5] International Energy Agency (IEA), CO2 emissions from fuel combustion, OECD/IEA, 2012.
[6] International Energy Agency (IEA), Energy Technology Perspectives 2008, OECD/IEA, 2008.
[7] International Energy Agency (IEA), Energy balances of Non-OECD countries,OECD/IEA, 2012[8] International Energy Agency (IEA), Energy Technology Perspectives 2010, OECD/IEA, 2010.
[9] Intergovernmental Panel on Climate Change (IPCC), Climate change 2001 – Mitigation, Cambridge
University Press, 2001[10] Intergovernmental Panel on Climate Change (IPCC), Climate change 2007 – Mitigation, Cambridge
University Press, 2007[11] Y. Kaya, Impact of carbon dioxide emission control on GNP growth: interpretation of proposed
scenarios, paper presented to the IPCC energy and industry subgroup, response strategies working group,
Paris, 1990.
[12] J. Oda et al., Diffusion of energy efficient technologies and CO2 emission reductions in iron and steel
sector, Energy Econ., 29 (2007) 868-888.
[13] J. Oda et al., International conmarison of energy efficiency in power, steel and cement industries,
Energy Policy, 44 (2012) 118-129.
[14] K. Riahi et al., Locked into Copenhagen pledges – Implications of short-term emission targets for thecost and feasibility of long-term climate goals, Technol. Forecast. Soc. Change, in this issue.
[15] F. Sano et al., Analysis of asian long-term climate change mitigation in power generation sector, 3rd

Research Institute of Innovative
Technology for the EarthIAEE Asian Conference, Kyoto Japan (2012)[16] F. Sano et al., Analysis of CCS diffusioin barriers for CO2 emission reduction considering technology
diffusion barriers in the real world, GHGT-11, Kyoto Japan (2012)[17] D.P. van Vuuren and K. Riahi, The relationship between short-term emissions and long-term
concentration targets, Clim. Change, 104 (2011) 793-801.
[18] K. Wada et al., Assessment of Copenhagen pledges with long-term implications, Energy Econ., 34(S3)(2012) S481-S486.

Research Institute of Innovative
Technology for the Earth

Electronic Supplementary Materials
Assessments of GHG emission reduction scenarios ofdifferent levels and different short-term pledges throughmacro and sectoral decomposition analyses
S1 Overview of DNE21+ model
The DNE21+ model (Akimoto et al. 2008, Akimoto et al. 2010) is an inter-temporal linear programming
model used for assessing global energy systems and global warming mitigation strategies. In this model,
the sum of the discounted total world energy systems costs is minimized. The model covers the first half ofthe 21st century, with the years 2000, 2005, 2010, 2015, 2020, 2025, 2030, 2040, and 2050 as
representative points. The model represents energy systems (e.g., capacities of energy-related facilities, andperformances and costs of various technologies) consistently via a minimum-cost combination oftechnologies in order to meet assumed production activities (e.g., amount of crude steel production),
service activities (e.g., amount of traffic in the transportation sector), and final energy demands in othersectors where technologies are not explicitly modeled (e.g., non-energy-intensive industries). If any
emission restrictions (e.g., carbon taxes) are applied, the model determines the energy systems with theminimum costs while satisfying the assumed requirements. The salient features of the DNE21+ model aredescribed in the following.
(1) The world is divided into 54 regions. Large countries such as the United States, Canada, Australia,
China, India, and Russia are further disaggregated into several regions, which allows for the considerationof regional differences, such as in the distribution of fossil fuels and renewable energies, and thetransportation of energy and CO2 across regions.
(2) About 300 specific technologies in both the energy supply and end-use sectors are explicitly modeled.
The sectors are fully integrated and can be assessed consistently in terms of the included technologies. The
lifetimes of facilities are also explicitly taken into account, and the transition of technology deployment canbe evaluated explicitly. The model includes technologies such as carbon capture and storage (CCS) that arein their incipient stages as well as already available technologies. In addition, CO2 fixation throughafforestation and the use of bioenergy technologies are also explicitly modeled by estimating the potential
productivity of land areas via a food supply and demand and land-use model (Hayashi et al. in press, Kii etal. in press).
A non-CO2 GHG (greenhouse gas) assessment model has been also developed based on Hyman et al.
(2003), and with some modifications considering new insights for non-CO2 GHGs. The non-CO2 GHG
assessment model is calibrated using recent historical emissions of non-CO2 GHGs.
References for S1[1] K. Akimoto et al., Global emission reductions through a sectoral intensity target scheme, Clim. Policy, 8(2008) S46-S59.
[2] K. Akimoto et al., Estimates of GHG emission reduction potential by country, sector, and cost, Energy
Policy, 38 (7) (2010) 3384-3393.
[3] A. Hayashi et al., Global evaluation of effects of agriculture and water management adaptation on thewater-stress population, Mitig. Adapt. Strateg. Glob. Change (in press).
[4] R.C. Hyman et al., Modeling non-CO2 greenhouse gas abatement, Environ. Model. Assess., 8 (2003)
175-186.
[5] M. Kii et al., Risk of hunger under climate change, social disparity, and agroproductivity scenarios,
Environ. Model. Assess. (in press).

Research Institute of Innovative
Technology for the EarthS2 Iron and steel sector in DNE21+ model
The iron and steel sector in the DNE21+ model is expressed in a bottom-up fashion with exogenous
assumptions of technology options and crude steel production scenarios.
Blast furnace-basic oxygen furnace (BF-BOF) and electric arc furnace (EAF) are explicitly modeled for
crude steel production. Scrap-based and direct-reduced iron (DRI)-based EAF types are considered. Several
technological options for improved energy efficiency are considered, such as coke dry quenching (CDQ)
and top-pressure recovery turbines (TRT). Several steelmaking routes with differing energy efficiencies aremodeled, including four types of BF-BOF steelmaking. Assumed capital costs and energy efficiencies areavailable in Oda et al. (2007).
Furthermore, the DNE21+ model also models CCS technologies for BF-BOF. CCS is applicable tohigh-efficiency BF-BOF options (Types III and IV in Oda et al. 2007), and CO2 captured by CCS is
assumed to be 0.6 tCO2/t-Crude steel, which corresponds to about 30% of the direct CO2 emissions from
BF-BOF steelmaking. Table S1 shows the assumed capital cost and energy consumption of CCS for
BF-BOF. These assumptions are based on several studies, e.g., Daniels (2002), IEA (2006), and Tonomura(2013).
Table S1 Assumed capital cost and energy consumption of CCS for BF-BOF
Facility cost [Million $/(ktC/day)]
66.9–57.6
Electricity consumption [kWh/t-Crude steel]
111–90Utility consumption [GJ/t-Crude steel]
0.98–0.43
Note: The ranges in the table depend on the time period (2010 to 2050).
Crude steel production is assumed as an exogenous scenario by region. For the top sixteen steel-producing
countries, we assumed a future scenario of apparent total consumption of crude steel per capita, taking into
consideration historical trends, the relationship with GDP per capita, and the theoretical scope that steel
consumption would increase steeply during the primary stage of economic growth then later stabilize or
decline during a fully developed economic stage. The crude steel production scenario for the minor
steel-producing countries was assumed simply based on the relationship between the historical GDP and
future GDP scenario. Future population and GDP scenario is assumed to be harmonized within the
AMPERE project (Riahi et al. this issue).
Assumed crude steel production is fixed for all the scenarios analyzed in this study. Therefore, the impactsof climate change mitigation on the amount of crude steel production cannot be evaluated endogenously inthis analysis.
The technology mix to meet the assumed crude steel production is evaluated by the DNE21+ model. The
availability of scrap for EAF is a key factor in this evaluation. In this study, the lower and upper bounds ofscrap-based EAF steel production scenarios are assumed by region, taking steel stock into consideration.
Future steel stock is estimated based on crude steel production (historical data is derived from World Steel
Association, etc.), assumed market share, and the lifetime of finished products. The market is classified into
civil engineering, buildings, machinery and appliances, transport, shipbuilding, and others. Future
end-of-life recycling rate (old scrap per old scrap + waste + obsolete stock) is assumed as 53%, which isestimated based on historical data (Oda et al. Unpublished results).
Figure S1 shows the assumed scenarios of world total crude steel and scrap-based EAF steel production.
The world total crude steel production in 2050 is assumed to be 2,200 million tons per year, which isalmost double that in 2005. Scrap-based EAF steel production also increases over time and the assumed
lower and upper bounds in 2050 are 680 and 770 million tons per year, which correspond to 31% and 36%
of total crude steel production, respectively. The assumed regional total crude steel production is shown in
Figure S2 for selected countries. In the assumed scenario, China remains the top steel-producing country
until 2050; however, growth in China’s steel production slows after 2010 and India assumes a greater share.
In 2050, total steel production in China and India is assumed to be 680 million and 500 million tons per
year respectively, representing 31% and 23% of the global total.

Research Institute of Innovative
Technology for the Earth

Crude steel production [Mt-Crude steel/y]

2500

2000
Total crude steel
production

1500

Scrap-based EAF steel
production (Max. limit)

1000

Scrap-based EAF steel
production (Min. limit)

500

0
2000

2010

2020

2030

2040

2050

Figure S1 Assumed scenarios of world total crude steel and scrap-based EAF steel production

Crude steel production [Mt-Crude steel/y]

800
700

USA

600

FranceGermany

500

Japan
China

400

Korea

300

India

200

Turkey100

Russia0
2000

Brazil

2010

2020

2030

2040

2050

Figure S2 Assumed scenarios of total crude steel production for selected countries

Research Institute of Innovative
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Share of scrap-based EAF steel productioin [%]

100
90
USA

80

France70

Germany

60

Japan

50

China
Korea

40

India

30

Turkey20

Brazil10

Russia

0
2000

2010

2020

2030

2040

2050

Figure S3 Assumed production of scrap-based EAF steel as a proportion of total crude steel production for
selected countries.
Note: The share is calculated based on median in lower and upper bounds of scrap-based EAF steel
production scenario.
Figure S3 shows the assumed regional scenario for scrap-based EAF steel as a share of total crude steel
production for selected countries. Currently, the proportions of scrap-based EAF steel production vary bycountry. In the assumed future scenario, this variation is taken into account because steel production plants
usually have a long operational lifespan.
References for S2[1] B.W. Daniels, Transition paths towards CO2 emission reduction in the steel industry, Universal Press,
Veenendaal(2002).
Available
onlineandlastaccessed
August2013at:http://dissertations.ub.rug.nl/faculties/science/2002/b.w.daniels/[2] International Energy Agency (IEA), Energy Technology Perspective 2006, OECD/IEA, 2006.
[3] J. Oda et al., Diffusion of energy efficient technologies and CO2 emission reductions in iron and steel
sector, Energy Econ., 29 (2007) 868-888.
[4] J. Oda et al., Long-term global availability of steel scrap, Resources, Conservation and Recycling,
Unpublished results (in review).
[5] K. Riahi et al., Overview WP2 – Locked into Copenhagen pledges – implications of short-termemissions targets for the costs and feasibility of long-term climate stabilization, Technol. Forecast. Soc.
Change, in this issue.
[6] S. Tonomura, Outline of Course 50, Energy Procedia, 37 (2013) 7160-7167.
[7] World Steel Association, Steel Statistical Yearbook, 1978-2011.

Research Institute of Innovative
Technology for the EarthS3 Road transport sector in DNE21+ model
The road transport sector in the DNE21+ model is expressed in a bottom-up fashion with exogenous
assumptions of technology options and traffic service demand scenarios.
Modeling of energy use of the road transport sector is shown in Figure S4. The transportation service isdivided into five categories: small passenger car, large passenger car, bus, small truck, and large truck. The
transportation service is assumed to be exogenous according to region and the five categories, and thescenario is fixed for all the scenarios analyzed in this study. Therefore, the impacts of climate change
mitigation on the level of road transport service cannot be evaluated endogenously in this analysis.
We assumed the transportation service scenario based on: (1) relationship between vehicle stock per capita
and GDP per capita; and (2) relationship between transportation service and vehicle stock. Historical datafor vehicle stock and transportation service are derived from several references such as JAMA and OECDstatistics. However, the available data are insufficient to develop the transportation service scenario byregion and by the five categories. Therefore, data in the IEA/SMP model (Fulton and Eads 2004) is used forinterpolation. Future population and GDP scenarios are assumed to be harmonized within the AMPERE
project (Riahi et al. this issue).
Figure S5 shows the assumed scenario of world total transportation service for passenger cars, and Figure
S6 shows the assumed regional scenarios for selected countries. In the assumed scenario, global use ofpassenger cars increases continuously to 31 trillion km/y in 2050. Passenger car usage in China is assumed
to exceed that of the USA after 2030, reaching 9 trillion km/y in 2050, which is about 30% of the global
total.
For technology options, not only internal combustion engine vehicles but also hybrid electric vehicles,
electric vehicles, and fuel-cell vehicles are considered. The assumed vehicle price and vehicle efficiency forpassenger cars are shown in Tables S2 and S3. The assumption incorporates several references, e.g., IEA
2008, JHFC/JARI 2011, and the 2006 study group on next-generation vehicle battery technology. Cost
reduction of vehicles and improved vehicle efficiency through technological development are exogenously
assumed.

Research Institute of Innovative
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GasolineBus

Gasoline +Ethanol (≧20%)

Ethanol

Passenger car(Small)Passenger car(Large)Passenger car(Small/Large)

Gasoline +Ethanol (＜20%)

Bus

Truck
(Small/Large)
Diesel Engine base

Light OilBio diesel

Passenger car(Small/Large)

Light oil +Bio diesel (＜20%)
Light oil +Bio diesel (≧20%)

Bus

Transportation service

Truck(Small/Large)

Electricity

Freight transportation service

Passenger car(Small)Truck
(Small)CNG

Truck(Small)Truck(Large)Passenger car(Small/Large)
Bus

Hydrogen

Passenger transportation service

Gasoline Engine base

Truck
(Small/Large)

Figure S4 Modeling of energy use of the road transport sector in DNE21+
Transportation service of passenger car [billion km/y]

35000

30000

25000

20000
Large passenger car
Small passenger car

15000

10000

5000

0
2000

2010

2020

2030

2040

2050

Figure S5 Assumed scenario of world total transportation service for passenger cars

Transportation service of passenger car [billion km/y]

10000
9000
8000

USA

7000

FranceGermany

6000

Japan
China

5000

Korea

4000

India
Turkey3000

Brazil

2000

Russia

1000
0
2000

2010

2020

2030

2040

2050

Figure S6 Assumed scenario for transportation service of passenger cars for selected countries

Research Institute of Innovative
Technology for the Earth

Table S2 Assumed vehicle price and vehicle efficiency for small passenger cars
Vehicle price
Vehicle efficiency[Thousand $/vehicle][L-gasoline eq./100 km]
ICEV Gasoline (Low Eff.)
17.6
8.0 – 7.1
ICEV Gasoline (High Eff.)
20.7
4.4 – 3.9
ICEV Bio ethanol (Low Eff.)
19.0 – 17.8
8.4 – 7.5
ICEV Bio ethanol (High Eff.)
22.4 – 20.9
4.6 – 4.1
ICEV CNG (Low Eff.)
24.2 – 18.7
8.4 – 7.5
ICEV CNG (High Eff.)
28.5 – 22.0
4.6 – 4.1
HEV Gasoline
24.0 – 21.0
3.4 – 1.9
HEV Bio ethanol
26.0 – 21.2
3.5 – 2.0
HEV CNG33.1 – 22.3
3.5 – 2.0
Plug in HEV Gasoline
36.9 – 21.6
2.6 – 1.5
Plug in HEV Bio ethanol
40.0 – 21.8
2.7 – 1.5
ICEV Diesel (Low Eff.)
18.5
7.0 – 6.2
ICEV Diesel (High Eff.)
21.7
3.8 – 3.4
ICEV Bio diesel (Low Eff.)
20.0 – 18.6
7.3 – 6.5
ICEV Bio diesel (High Eff.)
23.5 – 21.9
4.0 – 3.5
HEV Diesel
25.2 – 22.0
2.9 – 1.7
HEV Bio diesel
27.3 – 22.3
3.1 – 1.7
Plug in HEV Diesel
38.8 – 22.7
2.3 – 1.3
Plug in HEV Bio diesel
42.0 – 22.9
2.4 – 1.4
EV
84.7 – 22.7
1.5 – 0.9
FCV
133.6 – 24.1
5.1 – 2.1
Note 1: The ranges in the table depend on the time period (2010 to 2050).
Note 2: ICEV: Internal Combustion Engine Vehicles, HEV: Hybrid Electric Vehicles, EV: Electric Vehicles,
FCV: Fuel Cell Vehicles.
Note 3: Flexible-fuel vehicles are noted as ‘Bio ethanol’ and ‘Bio diesel’. Flexible-fuel vehicles are allowed
to use bio fuel with high blend rate (more than 20%).
Table S3 Assumed vehicle price and vehicle efficiency for large passenger cars (more than 2,000 cc class)
Vehicle price
Vehicle efficiency[Thousand $/vehicle][L-gasoline eq./100km]
ICEV Gasoline
38.3
12.1 – 10.7
ICEV Bio ethanol
41.4 – 38.6
12.7 – 11.2
ICEV CNG
52.7 – 40.6
12.7 – 11.2
HEV Gasoline
48.0 – 42.0
6.6 – 3.7
HEV Bio ethanol
52.0 – 42.4
6.9 – 3.9
HEV CNG66.1 – 44.6
6.9 – 3.9
Plug in HEV Gasoline
73.8 – 43.2
5.0 – 2.9
Plug in HEV Bio ethanol
80.0 – 43.6
5.2 – 3.0
ICEV Diesel
40.2
10.5 – 9.3
ICEV Bio diesel
43.5 – 40.6
11.0 – 9.8
HEV Diesel
50.4 – 44.1
5.7 – 3.2
HEV Bio diesel
54.6 – 44.5
6.0 – 3.4
Plug in HEV Diesel
77.5 – 45.3
4.4 – 2.5
Plug in HEV Bio diesel
84.0 – 45.8
4.6 – 2.7
FCV
267.1 – 48.2
9.9 – 4.1
Note 1: The ranges in the table depend on the time period (2010 to 2050).
Note 2: ICEV: Internal Combustion Engine Vehicles, HEV: Hybrid Electric Vehicles, EV: Electric Vehicles,
FCV: Fuel Cell Vehicles.
Note 3: Flexible-fuel vehicles are noted as ‘Bio ethanol’ and ‘Bio diesel’. Flexible-fuel vehicles are allowed
to use bio fuel with high blend rate (more than 20%).

Research Institute of Innovative
Technology for the Earth

References for S3[1] L. Fulton & G. Eads, IEA/SMP model documentation and reference case projection (2004).
[2] International Energy Agency (IEA), Energy Technology Perspective 2008, OECD/IEA, 2008.
[3] Japan Automobile Manufacturers Association (JAMA), World Motor Vehicle Statistics (in Japanese).
[4] Japan Hydrogen and Fuel Cell Demonstration Project (JHFC)/Japan Automobile Research Institute(JARI), Report for analysis of total efficiency and GHG emission (2011) (in Japanese).
[5] OECD Statistics. Available online and last accessed January 2013 at:
http://stats.oecd.org/[6] K. Riahi et al., Locked into Copenhagen pledges – Implications of short-term emission targets for thecost and feasibility of long-term climate goals, Technol. Forecast. Soc. Change, in this issue.
[7] Study group on next-generation vehicle battery technology, Recommendations for the future ofnext-generation vehicle batteries (2006) (in Japanese).