Global Environmental Research Fund (GERF)

Japan Roadmaps towards Low-Carbon
Societies (LCSs)
350

Scenario A
CO2 emissions [MtC]

300

250
200
150
100
50

Action 10
Action 8
Action 6
Action 3
Action 1

Action 9
Action 7
Action 5
Action 2CO2 emissions

0
2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
8

Scenario A
Additional investment [Tril. JPY]

6

4
2

0
-2

Ene: Fuel saving
Ene: Additional fixed investment
Trp: Fuel saving
Trp: Additional fixed investment
Ind: Fuel saving
Ind: Additional fixed investment
Res: Fuel saving
Res: Additional fixed investment
Total

-4
-6
-8
2000

2005

2010

2015

2020

2025

2030

2035

2040

2045

2050

August 2009
“2050 Japan Low-Carbon Society” Scenario TeamNational Institute for Environmental Studies (NIES),
Kyoto University,and Mizuho Information and Research InstituteKey conclusions
This study shows quantitative roadmaps for introducing countermeasures and policies for
reducing CO2 emission by 70% compared to the 1990 level by 2050 in Japan. This report is a series
of Japan Low-Carbon Society scenario study, followed “Japan Scenarios and Actions towards
Low-Carbon Societies (LCSs)”.
Assumptions
This study has analyzed roadmaps for reducing CO2 emissions by 70% by 2050 minimizing thetotal cost from 2000 to 2050 while satisfying the future service demand assumed in Scenarios A
and B shown in “Japan Scenarios and Actions towards Low-Carbon Societies (LCSs)”.



A list of 600 options (around 400 countermeasures and 220 policies) were prepared for each ofwhich the necessary implementation period and cost were estimated based on literatures, expert
judgments, and market survey.

Overview of a methodology: Backcast model

The Backcast model used here has a linear optimization framework and can evaluate thepossibilities of when and which options are invested so that service demand is satisfied underthe minimization of total cost of options based on the actions suggested in “A Dozen Actions”
and their relationships (see the Appendix for the detailed information of the model).



The model includes performance improvements by future technical innovation and the„learning-by-doing‟ of fixed costs as the cumulative amount of introduction increases.

Outputs are: Pathways for reducing CO2 which are in conformity with, the optimization
standards, changes in the annual amount of investment, timings for implementing options, anddevelopment of Gantt chart for the time line.

Major FindingsTaking early action could lead cost-minimizing way to achieve a low-carbon society by 2050.



Early actions are advantageous mainly due to the following five reasons:
Points revealed by the model analysis:
1. Technologies have „learning-by-doing‟ effects: An additional cost for reducing CO2
emissions will be reduced as the technologies spread.
2. If actions are delayed, „learning-by-doing‟ effects may not work sufficiently, resulting in
the increase the total investment required to achieve a low-carbon society.
3. Infrastructure cannot be built immediately, making it difficult to switch suddenly to a
low-carbon type just before 2050.
4. The future technological development has several uncertainties. If the development of adominant technology at this moment falls behind schedule, it will not be able to be spreadas expected and CO2 emission target will not be achieved. Early action creates for
spreading alternative actions for the LCSs in case such a situation should come.
iAnother point to be investigated is:
5. Because the cumulative emissions affects global warming, reducing the CO2 emissions in
only 2050 will not be able to stabilize the climate, which is the one of the important aim ofa low-carbon society.
Costs for early actions


A large investment is needed at the initial stage to achieve a low-carbon society in 2050 bytaking early actions. Particularly, the residential and commercial sector needs an annual
investment of 2.5 trillion JPY a year from 2010 to 2025, and the transportation sector needs 2.5
trillion from 2010 to 2015.

Prompt investment will promote technology innovations as a result of increased domestic
demand as well as will enhance international competitiveness of domestic industries since a
low-carbon society is a global trend.



If actions are delayed, other nations, not only developed countries but also developing countries
like China and India, are likely to invest in low-carbon technologies, which will mature there.
Low-carbon technologies introduced late in Japan leading up to 2050 will get developed inthese nations and could adversely affect Japanese industries.

Towards a low-carbon society in Japan

Strategies for achieving Japan low-carbon society require not only short-to-medium-term views
but also approaches based on long-term and broad perspective backcasting view, which may
involve thorough use of energy efficient and environmental-friendly technologies and structuralchanges such as switching to low-carbon infrastructure.



Early actions are more likely to achieve a low-carbon society than delayed actions because of
the learning-by-doing effects of spreading technologies, the necessary lead time for switchingfrom the current infrastructure to low-carbon one, and uncertainties that may cause delays intechnology R&D and the implementation of innovative technologies for reducing CO2
emissions.



The need for early actions is likely to be emphasized as cumulative CO2 emissions by 2050
affect future temperature rises, but this is outside the scope of the present study.

To achieve a low-carbon society with cost-minimization, investments should be focused first on
those actions that have large rooms for future improvements in energy efficiency and costs
reduction, such as energy efficient devices in homes and offices, well-insulated houses andnext-generation automobiles in the transportation sector. The investments will expand themarket, improve efficiency and reduce costs as well as will reduce CO2 emissions.



It is essential for achieving Japan low-carbon society in 2050 to draw up the target vision andtake the first step in the correct direction by political leadership based on quantitative analyses
and long-term broad-ranging scientific knowledge.
ii

1. Introduction
In the Japan Low-Carbon Society Scenarios towards 2050 Project, which was sponsoredby the Global Environment Research Fund (GERF) of the Ministry of the Environment, thebackcasting approach was used to quantitatively investigate the pathways required to achieve a
low-carbon society by 2050. Unlike forecasting, where a future image is drawn by gathering present
measures, backcasting involves drawing up a target (or sometimes intolerable) image andinvestigating measures needed for achieving the target.
Earlier studies point out that, countermeasures for global warming and/or CO2 reduction
should be taken by waiting for more technology development in future. For the development ofinnovative technologies, various actions need to be taken in advance, including knowledge
accumulation of technologies, changes of social/financial/market systems, and social infrastructure
for accepting the new technologies. A backcast model has been used to investigate the timing of
introducing each option considering these elements.
Prior to the analysis, two scenarios for achieving a low-carbon society in Japan by 2050
were developed and images of a society in 2050 in which CO2 emissions are 70% lower than the1990 level were quantitatively drawn up using various models. The images were then used as thetarget in the backcasting model. Combinations of options (countermeasures and policies) that areneeded for achieving such a society were presented as “A Dozen Actions”, which comprehensively
describes various actions required to help achieve a low-carbon society and summarizes them basedon their chronological inter-relationships. However, “A Dozen Actions” does not quantitatively
investigate when and how strongly each option should be taken.
This study quantitatively assesses the procedures for implementing the actions using thebackcasting model and summarizes the analysed results. An overview of the procedures of paststudies and outcomes of this study has been elucidated below.

2． Procedure of developing low-carbon society scenarios towards 2050
As the first step of the Japan Low-Carbon Society Scenarios towards 2050 Study Project,requisites for achieving a low-carbon society by 2050 were defined. To stabilize the average global
temperature to within 2°C higher than that prior to the Industrial Revolution, the study predicted
future global emission paths of greenhouse gases and decided to set the carbon emission goal ofJapan at 60 to 80% lower than the 1990 level.
In the second stage, the social and economic states of Japan in 2050 were predicted basedon references and interviews with experts and depicted as two narrative scenarios

shown in Table 1.

Scenario A assumes a vivid and technology-driven society, and Scenario B is a slow andnature-oriented society. Using snap shot model, these two scenarios were confirmed to havecombinations of measures that would reduce CO2 emission by 70% in 2050 as shown in Figure 1.
1

Table 1 Two narrative scenarios for Japan low-carbon society at 2050
Scenario A

Scenario B

Vivid, Technology-driven

Slow, Natural-oriented

Urban/Personal

Decentralized/Community

Technology breakthroughCentralized production/recycling

Self-sufficient
Produced locally, consumed locally

Comfortable and Convenient

Social and Cultural Values

2%/yr GDP per capita growth

1%/yr GDP per capita growth

Dependent on import products in agriculture
sector

Revival of public interest in agriculture

Shifting production sites to overseas

High-mix low-volume production with local brandMarket deregulation

Adequate regulated rules apply in market

Change of activity
6
1
24
10
13
38
9
7
28

Industry90

6

Residential &commercial

Reductionof demand
Improvement ofenergy intensityof end-use

17

41
77

Transportation

36

2050 CO2 Emission

36
CCS

Energy supply

Energy demand sector
Energy supply sector

21

Improvement of Improvement ofcarbon intensity carbon intensityof energy supplyof end-use

70% reduction

2000 CO2 Emission

1990 CO2 Emission

Changeof activity

・High economic growth, Increase of service demand perhousehold, Increase of office floor (increase)・Servicizing of industry, Decline in number of households,
Increase of public transportation (decrease)

Reduction ofservice demand
Improvement ofenergy intensity
Improvement ofcarbon intensity

・Farm products produced and consumed in season

Reduction ofservice demand

・Insulation
・Energy use management (HEMS/BEMS)
・Efficient heat pump air-conditioner, Efficient water heater,
Efficient lighting equipment
・Development and widespread use of fuel cell・All-electric house
・Photovoltaic

Improvement ofenergy intensity
Improvement ofcarbon intensity

Reduction ofservice demand
Improvement ofenergy intensity
Improvement ofcarbon intensity

Improvement ofcarbon intensity
Carbon Capture
Storage・Fuel switch from coal and oil to natural gas

・Advanced land use / Aggregation of urban function
・Modal shift to public transportation service
・Widespread use of motor-driven vehicle such as
electric vehicle and fuel-cell electric vehicle
・High efficiency freight vehicle
・Improvement of energy efficiency (train/ship/airplane)
・Fuel mix change to low carbon energy sources such as
natural gas, nuclear energy, and renewable energy
・Effective use of night power / Electricity storage
・Hydrogen (derived from renewable energy) supply
・Power generation without CO2 emission
・Hydrogen production without CO2 emission

Figure 1 CO2 emission in 2050 and options for achieving the reduction (Scenario A)

In Scenario A shown in Figure 1, CO2 emissions will decline due to the following factors:
2The total service demand will be almost equal to the 2000 level, assuming that the annual
growth rate per capita GDP will be 2%, the population will decrease by 20% from the 2000
level, and the industrial structure will be servisized like those in European and North American
nations.

In such a society, where people can live in pedestrian-friendly city,

energy consumption will

reduce, the energy service demand will decline, enabling a reduction of 40%. Additionally, theuse of renewables, nuclear power, carbon capture and storage (CCS) and other low-carbon
energy technologies will enable a further 30% reduction, making a total reduction of 70% fromthe 1990 level.
Of the CO2 emission reduction (about 40% lower than the 1990 level) by decrease in theend-use sector, 9% will be achieved by spread of energy efficient transportation machines,
likeelectric vehicles etc., 7% by the introduction of energy-saving devices in industry, and 3%
by the introduction of photovoltaic system, etc. into houses and offices. The remaining 21%
will be achieved by the rational diffusion of about 400 elements of various technologies,
including existing technologies and those which are expected to be commercialized by 2050.

In the energy supply sector, widespread use of wind power and the safe use of nuclear power
will be promoted, energy efficient natural gas will be used for thermal power generation insteadof coal- and oil-fired power plants, and carbon capture and storage (CCS) facilities will be built
adjacent to thermal power plants. This is estimated to achieve a 30% reduction out of the 70%
reduction.

In the third stage, all possible options (countermeasures and policies) for reducing CO2
emission by 70% compared to the 1990 level by 2050 were identified and compiled as “A Dozen
Actions towards a Low-Carbon Society” as shown Figure 2 according to the relationships andcompatibility among the options. There are several technical and social barriers to achieving the
reduction, which must be overcome by taking action systematically and steadily. With such mutual
relationships in mind, countermeasures and policies were combined, and those that are likely to beeffective are called “Actions”. Each Action consists of a target future image, barriers to achievement,
staged strategies, and qualitative progress schedule. The target future image is a narrative descriptionof the social system to be constructed by 2050. As staged strategies, strategies for removing
technical and/or social system barriers to achieving the future image are described. The progress
schedule qualitatively shows the results of chronological combinations of measures and policies.

3Figure 2 “A Dozen Actions” and its contribution to CO2 reduction in 2050
The chronological images of the combinations of options (countermeasures and policies)
shown in “A Dozen Actions” are qualitative, and need to be quantified to take actual actions. To dothis, a backcast model was used. Identification of the pathways for introducing the options byconducting model analysis is the final stage of drawing up the Japan Low-Carbon Society Scenarios
towards 2050, which quantitatively depict the pathways for introducing actions for achieving a
low-carbon society in 2050 for Scenarios A and B separately. The backcast model was prepared byextending the linear optimization framework where investments are allocated to different time
periods so that service demands are satisfied and the total cost of introducing the actions is
minimized, thus enabling the actions investigated in “A Dozen Actions” and their relationships canbe assessed. Using the model, the pathways for introducing the options were analyzed by setting theemission goal in 2050 as the limiting condition.

43． Pathways towards 70% reduction in 2050 using the backcast model
Pathways for achieving a 70% reduction of CO2 emission compared to the 1990 level
while satisfying service demands along Scenarios A and B were visualized as shown Figure 3byusing the backcast model with the objective function as minimization of the total costs during theperiod (see the Appendix for the data of each option assumed and the details of the model structure).
In cases where a CO2 reduction goal was not established, the CO2 emission was estimated to start
decreasing after peaking around 2020 due to changes in the number of households and industrial
structure, and the CO2 emission in 2050 will be similar to the 1990 level, although the results
slightly differ by scenario. As this result was far from the targeted 70% reduction, the CO2 emission
in 2050 was set as the limiting condition for the subsequent investigations.
In case where the CO2 emission target in 2050 was set to be a 70% reduction compared to
the 1990 level, the pathways to reach this in Scenarios A and B were quite similar, showing that the
optimum course in terms of total cost is to start introducing measures as soon as in 2010, reducing
emissions by 17% of 1990 level in 2020 and by 30% in 2030.

350
CO2 emissions [MtC]

300

250
200
150
100
50

Scenario A: No target
Sceanrio A: 70% reduction
Scenario B: No target
Sceanrio B: 70% reduction

0
2000

2010

2020

2030

2040

2050

Figure 3 Pathways of CO2 emissions toward 70% reduction in 2050
When the CO2 emission was constrained, the reduction effects of the actions described in“A Dozen Actions” were analyzed as shown in Figure 4. Action 4 is included in Actions 1 and 2.The
effects of Actions 11 and 12 are not explicitly shown because they are cross sectional. Introductionof all actions has been depicted to start at an early stage of the analyzed period.

5

350

Scenario A
CO2 emissions [MtC]

300

250
200
150
100
50

Action 10
Action 8
Action 6
Action 3
Action 1

Action 9
Action 7
Action 5
Action 2CO2 emissions

0
2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

350

Scenario B
CO2 emissions [MtC]

300
250
200
150

100
50

Action 10
Action 8
Action 6
Action 3
Action 1

Action 9
Action 7
Action 5
Action 2CO2 emissions

0
2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
Figure 4 CO2 reduction wedges by “A Dozen Actions” in Scenarios A and B

6

The timings of introducing the assumed options were organized in form of a Gantt chart(Figure 5). In the figure, countermeasures are shown in red and policies are shown in green. The
periods shown with bars are those for actively spreading the measure or for spreading the policy
nationwide. Arrows denote periods for maintaining the ratio of spread of the countermeasure or theperiod for continuing the policy. Diamonds are the timing for drawing up and enforcing the policy.
The energy efficient household appliances in the figure were analyzed by preparing data for specifictechnologies separately, such as air conditioners and kerosene heaters, but the technologies are
summarized in the figure as the analysis results were similar.

7

2010

2020

2030

2040

2050

Residential and commertial sector
Introduction of simplified methods for assessing theperformance of buildings
Spread technologies of insulated houses and buildings
Spread insulated houses and buildlings
Introduce a system for labeling houses and buildings
Revice the top-runner standards
Spread the use of energy efficient household appliances
Spread the use of energy efficient office appliances
Introduce solar water heating appliances
Agriculture
Introducing labeling of farm products
Increase consumer awareness of low-carbon farm products
Encourage the consumption of seasonal foods
Spread energy efficient devices in agriculture
Industry
Introduce a CO2 emission disclosure system by eachcompany and office
Establish CO2 emission disclosure system on the entire
industry
Spread energy efficient devices in industry
Fuel shift to gas in industry
Promote electrification in industry
Spread steel furnaces installed with CCS
Passenger transportation
Change the city structure by revising land use
Introduce a system for drawing up intensive land use andtransport plans together with citizens
Promote the concentration of houses, etc. in the center ofcitiesCreate a system of preferential treatment for low-carbon
vehiclesSpread energy efficient automobiles
Spread biomass energy use
Spread energy efficient railways
Spread energy efficient vessels
Spread energy efficient airplanes
Spread energy efficient light automobiles
Conduct technological demonstrations near existinghydrogen supply stations
Investigate plans for positioning hydrogen supply stations
Expand research and development of fuel cells
Introduce fuel cell vehicles
Freight transportation
Revise the top-runner standards
Spread energy efficient trucks
Promote a modal shift in logistics
Spread biomass fuel
Energy supply sector
Introduce energy efficient thermal power plants
Promote the development of CCS technologies and safetyassessment
Establish safe and efficient CCS technologies
Spread thermal power plants equipped with CCS
Promote lower costs of introducing renewables
Raise the exercise price of renewables and construct awarranty system with a certain guarantee period
Increase solar and wind power generation
Colors in the figure: red: measure, green: policy
Width of the lines: Bars denote periods for actively spreading the measure or for spreading the policy nationwide. Arrows denote periods formaintaining the ratio of spread of the measure or the period for continuing the policy. Diamonds are the timing for drawing up and enforcing
the policy.

Figure 5 Gantt chart of options towards 70% reduction of CO2 emission by 2050
8

4．Viewpoints of future pathways: five reasons for taking early action
The investigation of the pathways until 2050 shows that options (measures and policies)
towards a low-carbon society should be executed promptly to minimize the total cost needed toachieve the goal. As listed below, there are five main reasons for taking early actions. The
investigation has also revealed

a key issue in acting promptly i.e. to find necessary initialinvestment.
Key reasons for early action
1） Technologies have learning-by-doing effects, and the additional cost of low-carbon technologies
will be reduced as the technologies spread.
2） If actions are delayed, learning-by-doing effects may not work sufficiently, thus increasing thetotal additional investment required to achieve a low-carbon society.
3） Infrastructure cannot be built immediately, making it difficult to replace suddenly to a
low-carbon society before 2050.
4） The technological development in future has uncertainties. If the development of a dominant
technology at this moment falls behind schedule, it will not be able to be spread as expected andCO2 emission target will not be achieved. Early action creates for spreading alternative actions
for the LCSs in case such a situation arises.
5） As the cumulative emissions affect global warming, reducing the CO2 emissions in only 2050
will not be able to stabilize the climate, which is the one of the important aim of a low-carbon
society.

Hereinafter, each of the reasons has been explained using investigated and quantitatively
analyzed results. In this study the „reason 5‟ has been mentioned but not analyzed in detail.

1）Learning-by-doing effects
Technologies have learning-by-doing effects in general. Costs are known to decrease as theintroduction of technologies increases as shown in Figure 6. For example, the learning rate (thepercentage cost reduction by doubling the cumulative amount) of photovoltaic (PV) system isestimated to be about 20% (Figure 7). According to a meta-study1 on the learning-by-doing effectsof various energy devices in end-use and energy supply sector, the learning effects vary dependingon the technology and are not uniform. The learning-by-doing effects have been reported for various
technologies besides PV system in the Table 2. In studying the pathways, the fixed costs of
low-carbon technologies were assumed to decrease as the cumulative amount of introduction
increases (as learning progresses).

1

Alan McDonald, Leo Schrattenholzer (2001) “Learning rates for energy technologies” Energy Policy 29 (2001), PP
255-261

9

Cost
Doubling cumulative amount

Reducing cost by 20%
Learning rate is
80%

Cumulative amountFigure 6 Schematics of learning-by-doing effects

Figure 7 Historical learning curve of Photovoltaic system2

Table 2 Examples of learning-by-doing of energy technologies3
Technology
Gas turbine
Nuclear power plant
Hydro power plant
Coal-fired plant
Gus turbine combined cycle (GTCC)Wind power
Photovoltaics
Ethanol production
Bulb-shaped fluorescent lamp
Air-conditioner

Regional
scale
World
OECD
OECD
OECD
OECD
OECD
World
BrazilU.S.
Japan

Period1958-1980
1975-1993
1975-1993
1975-1993
1984-1994
1981-1995
1968-1998
1979-1995
1992-1998
1972-1997

Learning
rate (%)
13
5.8
1.4
7.6
34
17
20
20
16
10

R2
0.94
0.95
0.89
0.90
0.78
0.94
0.99
0.89
0.66
0.82

Whether to take learning-by-doing effects into account or not greatly affects the estimatedcost and optimum timing of incorporating the options. Differences in the unit cost of introducing
options for reducing CO2 caused by learning-by-doing effects are shown in Figure 8 using water
2

EPIA (2009) “Set for 2020 Solar Photovoltaic electricity: A mainstream power source in Europe by 2020”
Alan McDonald, Leo Schrattenholzer (2001) “Learning rates for energy technologies” Energy Policy 29 (2001), PP
255-2613

10

heaters in the residential sector as an example. When learning-by-doing effects are considered, thecost of introducing the appliances, which was assumed to start in 2010, were estimated to gradually
decline after 2015.
2.5E+06

Unit additional cost [JPY/unit]

2.0E+06

1.5E+06

1.0E+06

Energy efficient oil heater (Learning-by-doing)
Energy efficient oil heater (None)
Energy efficient gas heater (Learning-by-doing)
Energy efficient gas heater (None)
Energy efficient electric heat-pump (Learning-by-doing)
Energy efficient electric heat-pump (None)

5.0E+05

0.0E+00
2000

2010

2020

2030

2040

2050

Figure 8 Differences in unit additional cost by learning-by-doing effects (water heater, Scenario A)

The differences in learning-by-doing effects also affect the optimum timing of introducing
the options, and the pathway of CO2 reduction will be delayed if there are no learning effects (Figure9). Learning-by-doing effects also resulted in an almost three-fold difference in the estimatedcumulative investment from 2000 to 2050 when reductions in fuel costs were also considered: 12
trillion JPY with learning effects and 34 trillion JPY without learning effects (assumed discount rate:3%). This was also the similar in Scenario B.

350
Scenario A
CO2 emissions [Mt-C]

300

250
200

150
100
50

No CO2 target
70% reduction with learning-by-doing
70% reduction without learning-by-doing

0
2000

2010

2020

2030

2040

2050

Figure 9 Comparison of CO2 pathways with and without learning-by-doing effects
11

Introducing major CO2 reduction options by quickly investing large sums will not only
gradually reduce the cost by learning-by-doing effects but also reduce the total investment needed
for achieving a low-carbon society. In this analysis, CO2 reduction of only 70% by 2050 wasinvestigated, but further reductions could be sought in future as international negotiations progress.
Early investment and lower costs by learning effects will also help achieve such demands.

2）Delays in starting actions will result in higher costs
If initiation of actions is delayed, the total additional investment is likely to be higher as
learning-by-doing effects will not be able to work effectively as shown Figure 10. In this analysis,
fixed costs such as equipment costs and reduction of fuel costs by reduced energy consumption are
considered and converted into present values at the discount rate of 3% to determine the total
investment. Additional investment is the difference in fixed cost compared to that of conventional
devices minus the reduction of fuel costs. As for the fuel costs in this study, we have usedthe future
fuel prices based on assumptions of International Energy Agency and U.S. Department of Energy.
If actions are taken quickly (early action), the total additional fixed cost from 2010 to 2050
will be 80 trillion JPY in Scenario A and 62 trillion JPY in Scenario B, which are annual averages of
2 trillion JPY and 1.5 trillion JPY, respectively. The fuel cost reductions will be 68 trillion JPY and
69 trillion JPY, respectively. Thus, the resultant additional investment will be 12.7 trillion JPY in
Scenario A and -7 trillion JPY in Scenario B. And if actions are delayed (started in 2020), the total
additional fixed costs will increase as the learning-by-doing effects will not work to its potential andwill be 93 trillion JPY in Scenario A and 91 trillion JPY in Scenario B. The reduction of fuel costs
will be only 54 trillion JPY in both scenarios as the options will spread late. Thus, the resultant
additional investment from 2010 to 2050 will be 39 trillion and 37 trillion JPY in Scenarios A and B,

Total additional investment (2000-2050) [Tril. JPT]

respectively.
100
80
60
40
Reduction offuel cost
Additional
fixed cost
Total

20

0
-20
-40
-60
-80

Discount rate r=3%

-100
Early action

10 Yrs. Delay

Early action

Scenario A

10 Yrs. Delay

Scenario B

Figure 10 Comparison of total additional investment between early action and 10 Yrs delay
123）It takes time to construct infrastructure
Infrastructure (city infrastructure, transportation systems, energy infrastructure, buildings,etc.) generally has a long service life and cannot easily be modified once constructed. For example,
according to “Social Capital of Japan” (Cabinet Office Director-General (2007)), the average service
life4 of roads and railways is 51 and 34 years, respectively, and almost all other infrastructure has
life spans of 40 years or more5 (Table 3). Thus, if the right timing for rebuilding infrastructure ismissed, it will make it difficult to switch to a low-carbon infrastructure and to achieve the target CO2
reduction (Table 4).
Table 3 Average service life of infrastructure in each sector
Sector/Assets
Average service
life (Yr.)
Road
51
Harbor49
Airport16
Former Japan National Railways
22Japan Railway Construction Public Corporation, etc.
26
Subways, etc.
34
Former Nippon Telegraph and Telephone Public Corporation
18
Sewerage57
Waste disposal
40
Water works
39
City park
43
Education (Schools and academic facilities)
39
Education (social education, physical education and cultural facilities)
41
Flood control
85
Forestry conservation
50
Coastal protection
30
Agriculture
44
Forestry
49
Fishery
50
Postal service
18
National forest
47
Water works for industrial use
38

4

Average service life of each sector: Because infrastructure is composed of a number of component assets that differ
in service life, the mean service life is calculated by weighting and combining the service life of representative assets.
There are various calculation methods, and all involve calculating not only the mean in a single year but also themeans in two or more years and their simple average. Data shown was tentatively calculated by computing the mean
service life of each sector based on the “Ministerial ordinance on the service life of depreciation assets (Ordinance No.
15 of the Ministry of Finance on March 31, 1965)”, collecting more data, and revising the life spans.
5

The average service lives of these sectors are cited from the reference and are for information only.
These values were not assumed as prerequisites in the model analysis.
13

Table 4 Delays in infrastructure shift to low-carbon and possibility of CO2 reduction in 2050 [MtC]
Delays in infrastructure
Early action

Start in 2020

Start in 2030

Start in 2040

Start in 2050

Scenario A

85.2

86.7

109.5

134.6

161.3

(Compared to（-70%）（-69.5%）（-61.4%）（-52.6%）（-43.2%）

Scenario B

85.2

85.2

106.2

131.7

158.3

(Compared to（-70%）（-70%）（-62.6%）（-53.6%）（-44.3%）the 1990 level)the 1990 level)

Furthermore, the best possible low-carbon designs need to be incorporated into newly
constructed or rebuilt infrastructure to minimize the investment and achieve a low-carbon society
because huge and continuous investments are required to build infrastructure.
The infrastructure that is built today is likely to be in use in 2050. Thus, building of theframework of a low-carbon society has already started.

4）Uncertainties exist in the technological research, development and deployment
The development and spread of technology involves four stages: development,
demonstration, deployment and diffusion. Japan has carried out development projects for various
technologies, such as hydrogen fuel, fuel cells, and the nuclear fuel cycle, which are in their final
stage of spreading. Some of them have diffused as planned, but others are lagging and have not yet
been introduced in the market.
If we consider the fuel cell technologies, which are recently attracting attention world over,are not particularly new: Its development was started by the National Aeronautics and Space
Administration (NASA) in 1961. In Japan, the Moonlight Project to develop phosphoric acid fuelcells (PAFC) was carried out from 1981 to 1986. Later in 1990s, the basic technologies were
demonstrated and subsequent projects including the New Sunshine Project and the Fixed-type PAFC
Millennium Project of the Japan Gas Association (2000 to 2004) highlighted its potentials. However,so far, fuel cells have only been introduced on a trial basis or for demonstration purpose by electricand gas supply companies and have not yet established a solid position in the market. The status of
the nuclear fuel cycle and nuclear power technologies are similar.
The conclusions described in Section 3 are the results of an analysis is based onassumption that all technological developments and diffusion processes progress as scheduled. If thisprocess falls 10 years behind schedule, the estimated CO2 emission in 2050 will vary depending onwhether early actions are taken or not: it will be 97.7 Mt-C (-65.6% compared to the 1990 level)with early actions at a maximum but 107.0 Mt-C (-62.3% compared to the 1990 level) if actions are14

taken only after 2020 (Figure 11). If development is delayed by 30 years, the CO2 emission will be
reduced to 116.6 Mt-C (-59.0% compared to the 1990 level) with prompt actions at a maximum but
will be 125.9 Mt-C (-55.7% compared to the 1990 level) if actions start in 2020, a difference ofalmost 5% compared to the 1990 level. This is because acting early will provide opportunities toswitch to another technology if a scheduled low-carbon technology does not progress as anticipated.
Taking action now to achieve a low-carbon society will encourage active technology development,
improve efficiency, reduce costs by learning-by-doing effects, spread the portfolio of alternative
actions, prepare for future uncertainties, and thus raise the possibility of achieving a cost-efficient
low-carbon society.

Maximum CO2 reduction at 2050
(Compared to 1990 level)

80%
70%
60%
50%
40%
30%

20%
10%

Scenario A: Early action
Scenario B: Early action
Scenario A: Start in 2020
Scenario B: Start in 2020

0%
None

10Yr

20Yr

30Yr

Delay of technological R&D

Figure 11 Delay of technology development and possible CO2 reduction in 2050
Early action was shown to be necessary for achieving a low-carbon society, but thisrequires additional investment at the initial stage of the roadmap.

15

A large-scale additional investment is needed at the initial stage.
Changes in the annual additional investment were investigated for the pathways that will
incur the lowest total costs. In both Scenarios A and B, additional investment of about 5 trillion JPY
annually will be needed at the initial stage even when fuel cost reductions are considered (Figure 12).
In both scenarios, the necessary additional fixed costs in 2010 will be 2.5 trillion JPY in theresidential and commercial sector and 2.5 trillion JPY in the transportation sector. The additional
fixed costs in the transportation sector will be sharply reduced thereafter due to learning-by-doing
effects and reduced demand for transportation services by changes in the structure of cities and willbe almost zero in 2020. However, the additional fixed costs in the residential and commercial sector
will continue to be around 2 trillion JPY a year until 2030. These investments are for options thatcan largely improve efficiency and reduce costs, such as next-generation vehicles in thetransportation sector and energy efficiency appliances and highly insulated houses in the residential
and commercial sector.
The total additional investment in the entire period from 2010 to 2050 will be 12.7 trillion
JPY in Scenario A and -7 trillion JPY in Scenario B as the additional fixed costs will be reduced bylearning-by-doing effects. Particularly in Scenario B, the sum may be less than the investment inconventional types of systems. Without introducing expensive low-carbon technologies by investinglarge sums at the initial stage, no progress will be made.
Considering that the global trend is moving toward achieving a low-carbon society,Japanese industries may lose their international competitiveness if Japan does not invest in
low-carbon technologies in early because other nations will invest in such technologies andlearning-by-doing advances.
For establishing new industries, continuous investment and research and development of
low-carbon technologies are important. For example, Photovoltaic systems (PV) have recently
become a new industry in Japan, Germany and the US because R&D on PV has been conducted fora long time and industrial infrastructure has been built. On the other hand, nations that have recently
started to actively introduce PV as a matter of policy are depending on imports for the majority ofsystems. In Spain, PV usage was only 22 MW in total until 2004 but grown to 3,166 MW in 2008, a
100-fold increase in 4 years and surpassing the generation in Japan6. However, in 2008, PV producedin Spain did not account for even 10% of those introduced because domestic production could not
keep pace with the demand. The Spanish government is encouraging people to introduce PV by
setting the purchase price of PV high, which is resulting in large social costs being spent on
introducing large quantities of imported products.
The sudden introduction of low-carbon policies just before 2050 will merely result in

6

IEA PVPS (2008) “Trends in photovoltaic applications - Survey report of selected IEA countries between
1922-2007” and EPIA (2009) “Global Market Outlook for Photovoltaic until 2013”16

funds flowing to foreign firms unless continuous efforts have been made to spread technologies andto build the production infrastructure for better diffusion oftechnologies and become

internationally competitive. Delaying action increases the possibility of other nations investing
earlier and learning-by-doing low-carbon technologies, which may adversly affect the Japaneseindustry. Investing in low-carbon technologies earlier and/or imposing stricter environmental
restrictions than other nations to foster technology learning will enhance the international
competitiveness of Japanese industry.
8

Scenario A
Additional investment [Tril. JPY]

6

4
2

0
-2

Ene: Fuel saving
Ene: Additional fixed investment
Trp: Fuel saving
Trp: Additional fixed investment
Ind: Fuel saving
Ind: Additional fixed investment
Res: Fuel saving
Res: Additional fixed investment
Total

-4
-6
-8
2000

2005

2010

2015

2020

2025

2030

2035

2040

2045

2050

8

Scenario B
Additional investment [Tril. JPY]

6

4
2

0
-2

Ene: Fuel saving
Ene: Additional fixed investment
Trp: Fuel saving
Trp: Additional fixed investment
Ind: Fuel saving
Ind: Additional fixed investment
Res: Fuel saving
Res: Additional fixed investment
Total

-4
-6
-8
2000

2005

2010

2015

2020

2025

2030

2035

2040

2045

2050

Figure 12 Changes in additional investment for achieving a low-carbon society
17

5．Summary
The challenges towards a low-carbon society include not only short-to-medium-term
viewpoints, like the Kyoto Protocol and the mid-term targets of CO2 emissions, but also long-termand broad-ranging actions for achieving a 70% reduction of CO2 emission compared to the 1990
level by 2050 through the diffusion of energy efficient and environmental-friendly technologies,
shifting to low-carbon cities and other infrastructure, construction of low-carbon socioeconomic
structures by servisizing and partly returning to agriculture, forestry and fishery.
This study quantitatively analyzed when, how and at what intensities the “A Dozen
Actions” proposed in the May 2008 report should be carried out in order to achieve the 70%
reduction by 2050, which was judged to be technically feasible as described in the February 2007
report, based on technical feasibility and economy.
Some analyses conducted using energy-based models have recommended that actions will
start after (low-carbon) technologies get developed and inevitably will become cheaper. However,taking actions quickly increases the possibility of preventing global warming in view of thelearning-by-doing effects by technology diffusion, such as those shown in today‟s market for
Photovoltaic, the necessary lead time for converting energy infrastructure such as power plants,
power cables and gas pipelines, into low-carbon systems, and uncertainties regarding futuretechnological development, such as delays in deployment of fuel cell vehicles. The effects ofcumulative CO2 emissions on future temperature rises are also a strong reason for acting now, but
are not analyzed and assessed in this study.
The results show that it is worth seriously investing now in actions to improve energy
efficiency and reduce costs, such as developing and introducing energy efficient devices in homes
and offices, building highly insulated houses and developing and diffusing next-generation
automobiles in the transportation sector, to expand the market, improve efficiency and reduce costs.
The government should take the initiative by setting a target vision based on long-term perspectives
and scientific knowledge and take the first steps in the correct direction.

18

Appendix: Quantitative assessment using a backcast model
1. Overview of a backcast model
A backcast model was used to investigate what options (countermeasures and policies)
should be introduced when and at what intensity in order to achieve the future social and economic
activities portrayed in the scenarios while also satisfying the service demand today and throughout
the period until the target year based on certain criteria, and to present a Gantt chart that also
includes pathways of CO2 emission and investment and quantitative data. The activities of thesectors were determined based on the scenarios and changes in social structure and population
composition and were taken as exogenous variables in the model. The energy consumption,industrial structure, and CO2 emission composition in the standard and target years were analyzed
using the Energy Snapshot Tool (ESS) and the CGE-type model, and the values for the other years(intermediate years) were estimated endogenously by the model. Mixed integer programming wasused for formulation and the Cplex solver with General Algebraic Modeling System (GAMS) wasused to derive the optimum solution.
In this study, the base and target years were set to be 2000 and 2050, respectively.
Pathways for achieving the target society (Scenarios A and B) of 70% CO2 emission reduction
compared to the 1990 level were investigated by estimating the investment in CO2 reduction options
and energy balance every 5 years so as to minimize the total cost during the entire analysis period,
which is calculated in present value, while maintaining the activities of the scenarios under the CO2
emission restrictions in 2050.
Results ofSnapshotmodel

Historical
data

CO2
Economic
Activity

CO2
Economic
Activity

Input

Input

OptionCGE,
Transportation,
Households,
etc

Reduction
targetCO2 pathway

OptionOptionOptionCO2 pathway

Options(Countermeasuresand policies)

When, which
option andhow much
install?

Evaluation by backcast model

Base year

Target year

Figure A.1. Outline of the flow of estimation using a backcast modeli2. Overview of assessment using the backcast model
The pathways toward the 70% reduction of CO2 emission by 2050 were investigated by
the following five steps. Hereinafter, endogenous variables are expressed in uppercase, and indicesand exogenous variables are expressed in lower case.
1） Predicting future service demands
2） Listing possible options (countermeasures and policies) for achieving future scenarios3） Quantifying options
4） Adding relationships among options5） Quantitative investigation using the backcast model
2.1. Predicting future service demands
The future service demand by service j in sector i was put as an exogenous variable
depending on the social conditions assumed in Scenarios A and B. For example, the demands were
calculated from the future production of steel in the steel sector and from the number of households
and the activity per household for the residential sector.

2.2. Listing possible options for achieving future scenarios
As options (countermeasures and policies) toward a low-carbon society in 2050, the main
countermeasures listed in the “Japan Scenarios and Actions towards a Low-Carbon Society” andmain policies listed in “A Dozen Actions” were investigated. For example, actions in the residential
sector included countermeasures such as “insulated houses” and “energy efficient air conditioners”
mentioned in the former, and policies such “introducing a system for labeling environmental
performance buildings” and “revising top-runner standards” mentioned in the latter. The numbers ofmeasures and policies investigated in this analysis are summarized in Table A.1 for each action.

Table A.1. Numbers of measures and policies (Each is counted only once for the principal action.)
Action1. Comfortable and green environment
2. Anytime, anywhere appropriate appliances
3. Promoting seasonal local foods
4. Sustainable building materials
5. Environmentally enlightened business and industry
6. Swift and smooth logistics
7. Pedestrian friendly city design
8. Low-carbon electricity
9. Locally renewable resources for local demand
10. Next-generation fuels
11. Labeling to encourage smart and rational choices
12. Leadership for a low-carbon society
Total

Policy
19
45
15
13
9
11
11
28
14
32
10
12
219

Countermeasure
5
48
8
112
16
26
14
6
32
267*

*: About 400 when a countermeasure is counted separately for multiple actions.

ii

2.3. Quantifying options
For each countermeasure and policy, (1) the necessary period for introduction (the least
number of years needed for introduction) and (2) the costs for introduction were calculated.

(1) Necessary period for introduction
There are options that can be completed immediately such as establishing institutional
systems and those that may take several decades such as building insulated houses. For each option,
the least number of years necessary for accomplishing the options was estimated by studyingreferences and interviewing of experts. In principle, the period was the service life forcountermeasures and was the mean number of years from the start of discussion until enactment forpolicies. In the pathway analysis, the period was used as a limiting condition, assuming that options
cannot be introduced faster than the periods thus estimated (Figure A.2).
Least number of years
for introduction(service life, etc)

Pathways are determined so as
to minimize the total investment
during the analysis period.

Max

AmountPossible pathway
Such apathway is
not possible.

X

Possible pathway

O
O

Min

PeriodFigure A.2. Relationship between the periods needed for implementing an option and assessment of
the pathways for introducing the option(2) Costs for introduction
Fixed costs and fuel costs were considered as the costs required for introducing the options,and the differences from the conventional type were used as standards for assessing the timing for
introducing the options. Fuel costs were not considered for policies, and costs that are known to beneeded for investigating and formulating the policy were included as fixed costs.
The fixed costs for each year are endogenously determined by the model that takes
learning-by-doing effects into account. The initial value cf,init [JPY/unit] is exogenously set as thedifference between the initial fixed cost flcs,init [JPY/unit] of the measure and fconv of the conventional
alternative:c f ,init  f lcs ,init  f conv

(A.1)

The fixed costs of conventional alternatives were assumed differently depending on theiiikind of countermeasure or policy. The average stock costs of conventional devices were put as theconventional fixed costs for countermeasures that aim to improve efficiency. The costs of the devices
before fuel conversion were used as measures for fuel shifts, and the costs of thermal power plantsthat are not equipped with CCS (carbon capture and storage) were used for CCS. For policies, thecosts of conventional policies were assumed to be zero. The initial fixed cost of a options and thefixed cost of its conventional alternative were determined based on the “Energy Conservation
Catalog” (in Japanese) (2000, 2005 and 2008) of the Energy Conservation Center, “Energy
Technology Perspective (2008)” of the IEA, “Assessment and investigation of technologies forpreventing global warming (in Japanese)” of the Ministry of the Environment, references of theCentral Environment Council, “Japanese Motor Vehicles Guidebook (2007-2008)” of Japan
Automobile Manufacturers Association, Inc., and the results of market research by ourselves.

2.4. Adding relationships among options
Many options require preliminary options to have been introduced in advance. Such
preliminary options are called prerequisite options. For example, electric power supply stations musthave been established before electric vehicles can proliferate. In order to spread insulated houses thatmatch the local climate and consume the minimum energy, the present insulation standards need tobe revised to classify regions precisely to enable adjustment to the actual climatic conditions, and athird-party system for assessing the insulation performance of houses should be constructed. In thisanalysis, the relationships between the measures and policies for the “A Dozen Actions” and theirprerequisite options were investigated by classifying the relationships into “absolute connection” and“partial restriction.”
Absolute connection is a relationship in which a certain option cannot be started without
the full introduction of its prerequisite option. This relationship is widely seen between options andits prerequisite policies (Fig. A.3).
Option i is a prerequisite option of option i+1
Period t
Max1

Optimum time of implementation: s 1’
Earliest implementation time: s 1

Option 1
0
Max2

Start

Complete

Optimum time of implementation: s 2’
Earliest implementation time: s 2

Option 20

Start

Complete

Optimum time of implementation: s3

Max3

Option 30

Start

Figure A.3. Overview of absolute connectioniv

A partial restriction is a relationship in which the amount of introduction of a prerequisite
option at time t limits the amount of introduction of the target option at time t+1 (Figure A.4). For
example, if power supply stations have been established in 10% of the area by time t, electricvehicles will be able to be spread in the 10% area at time t+1.
Option i is a prerequisite option of option i+1

t t+1 t+2 …

Period t
Optimum time of implementation: s1’
Earliest implementation time: s 1

Max1

Option 10Complete

Start

Optimum time of implementation: s 2’
Earliest implementation time: s2

Max2

Option 20

Start

Complete

Max3

Optimum time of implementation: s3

Option 30
Start

Figure A.4. Evaluation of the amount of introduction of measures with partial restriction

2.5. Quantitative investigation using the backcast model(a) Preliminary conditions of the model
In this investigation of pathways, the base year t0 and the target year were set as 2000 and
2050, respectively. To eliminate the end effects of the simulation, the final year tL in the calculations
was set as 2070. The service demands in and after 2050 were determined by extrapolating the trends
up to 2050 and assuming the same CO2 emission limit as for 2050.
(b) Objective functions in optimization
The flow quantity Qf,m(t) of a option m in each time period is in principle determined byminimizing the costs during the analysis period. In this model, the flow quantity of an option is theincrement in the number of a low-carbon system introduced or in the percentage of diffusion, and theflow quantity of a policy is the progress of investigations on policies related to a low-carbon society.
Costs assessed were fixed costs and fuel costs, which were converted to present values at a
discount ratio of r = 3%:

tL


1
min TC     Fm ( )  Vm ( )  t0
  t 0  (1  r )
m


TC :

Total costs during analysis period [JPY]
v

(A.2)

Fm(t):

Total additional fixed costs for option m during period t [JPY]Vm(t):

Reduction in the total fuel costs during period t by option m (reductions are expressed as

positive values) [JPY]

The total additional fixed cost Fm(t) of each year was calculated from the flow quantity ofthe option during period t and the fixed cost Cm(t) [JPY/unit] by considering the learning-by-doing
effects during period t (Equation A.3). Learning-by-doing effects were not considered for calculating
the fixed costs of policies.

Fm (t )  Cm (t )  f conv  Q f ,m (t )(A.3)

Changes in fuel costs were calculated by multiplying the stocks Qm(h) [unit] of options
that belong to cohort h operating at time t and the reduction in energy ered,m(h,i,j,k) consumed byservice j of sector i per unit quantity of introduction in each cohort and adding the price pk(t) of thefuel k at time t. The stocks of measures are the total number of units introduced or the total
percentage of diffusion at time t, and the stocks of policies are the actual investigation states of thepolicies at time t, where, Ht is the group of cohorts that are operating at time t:






Vm (t )    pk (t )     ered ,m (h, i, j, k )  Qm (h) k 
i , j  hH t




(A.4)

(c) Evaluation of learning-by-doing effects
The cost c(t) at time t is generally expressed as:c(t )  c0  QCUM (t )

log p
log 2

(A.5)

where, c0 is the initial cost, QCUM(t) is the cumulative amount of introduction, and p is the learning
rate.
This is a non-linear relation. In this study, learning curves were used after linearization
because the backcast model constructed based on linear optimization framework. A learning curve
was divided into two or more lines, and their sum was used to express the reductions in costs bylearning. Learning effects were assumed only for countermeasures, and policies were assumed tohave no learning effects and require constant fixed costs. For example, in Figure A.5, the learningvi

curve was divided into six lines. This method calculates costs higher than the theoretical values forcumulative amounts smaller than 1. To avoid this and adjust the values also for small cumulative
amounts to the theoretical values, a line was added to the region of cumulative amounts smaller than
1.

140
L6
L5
L4
L3
L2
L1
Theoretial

Unit cost [% of initial value]

120
100
80
60
40
20
0
0

10

20

30

40

50

60

70

80

90

100

Cumulative production [unit]

Figure A.5. Linearization of a learning curve

Specifically, the fixed cost Cm(t) [JPY/unit] of option m at time t was calculated bymultiplying the initial value flcs,init [JPY/unit] and the sum of l number of lines Ll(t) that linearize thelearning curve and the line Lc(t) that has an inclination of gc and section of nc for correcting thevalues for cumulative amounts smaller than 1. Each of the lines Ll(t) was calculated from thecumulative introduction QCUMm(t) [unit] and the inclination gl and intercept nl determined by thelinearization of the learning curve. Ll(t) was assumed to be not smaller than zero.

Cm (t )  f lcs ,init    Ll (t )  Lc (t )  lLl (t ) g l  QCUM m (t )  nl (if  0), 0 (otherwise)
Lc (t ) g c  QCUMm (t )  nc (if  0), 0 (otherwise)

(A.6)
(A.7)
(A.8)

(d) Energy supply and CO2 emission
In the investigation of pathways, all exogenously given service demands had to be satisfied
regardless of implementation of options. In this model, service demands were not directly handled
but were first converted into energy consumptions and then formulated.
Changes in CO2 emission were estimated by 1) calculating the energy demand at Businessas usual (BAU) dBAU(t,i,j) [Mtoe] in service j of sector i at time t and the energy consumption (BAU)vii

eBAU(t,i,j,k) [Mtoe] for each kind of fuel by assuming that all energy was supplied by conventional
methods, 2) determining the amount of the measure to be introduced so that the total of theconventional-type energy consumption Econv(t,i,j,k) [Mtoe], the energy Elcs(t,i,j,k) [Mtoe] consumed
for introducing the measure, and the reduction Ered(t,i,j,k) [Mtoe] in energy consumption by the
introduction was equal to the energy consumption eBAU(t,i,j,k) [Mtoe], and 3) calculating the CO2
emission CO2(t) [t-CO2] after introduction of the measure.
Activity level(Exogenous)a(t,i,j)①

Energy service
demanddBAU(t,i,j)①

Energyconsumption (BAU)eBAU(t,i,j,k)②

Energy reduction
by options
Ered(t,i,j,k)Energy consumption
by options

Energy consumption by
conventional technology

Elcs(t,i,j,k)

Econv(t,i,j,k)
CO2 emission factor

emfk

CO2 emissions
CO2k(t)Figure A.6. Calculation flow of CO2 emission(1) Calculating the demands for energy supply and energy consumption (BAU)
Energy service demand (BAU) dBAU(t,i,j) [Mtoe] was calculated by multiplying the service
demand (activity level) a(t,i,j) [unit] of service type j of sector i and the energy service demandu(t,i,j) [Mtoe/unit] per unit service demand:

Energy service demand [Mtoe]

d BAU (t , i, j )  a(t , i, j )  u(t , i, j )

(A.9)

70

60
50

40
30
20

10
0

2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
OthersICT

Refrigerator

Lighting

Cooking

Hot Water

Warming

Cooling

Figure A.7. Examples of energy service demand in BAU (Residential sector, Scenario A)
viii

As an example, the energy service demand (BAU) in the residential sector in Scenario A isshown in Figure A.7. There are options that cause direct changes in energy service demand, but theireffects were also converted into changes in energy consumption in this study.
The energy consumption eBAU(t,i,j,k) [Mtoe] was calculated by assuming that all thedemanded energy was supplied by conventional methods. The calculation involved multiplying theenergy service demand dBAU(t,i,j) [Mtoe], the average efficiency of conventional appliancesηconv(t,i,j,k) [-] and the share of the fuel sconv(t,i,j,k) [-]:eBAU (t , i, j, k )  d BAU (t , i, j ) conv (t )  sconv (t , i, j, k )(A.10)(2) Balance of energy consumption
The energy consumption eBAU(t,i,j,k) [Mtoe] was assumed to be equal to the sum of
conventional-type energy consumption Econv(t,i,j,k) [Mtoe], the energy Elcs,m(t,i,j,k) [Mtoe] consumed
for introducing option m, and the reduction Ered,m(t,i,j,k) [Mtoe] in energy consumption byintroducing the option:

Econv (t , i, j, k )   Elcs , m (t , i, j, k )  Ered , m (t , i, j, k )   eBAU (t , i, j, k )(A.11)

m

Here, the reduction Ered,m(t,i,j,k) [Mtoe] in energy consumption by introducing the options
was determined by multiplying the stock of option Qm(h) [unit] of cohort h operating at time t andthe reduction ered,m(h,i,j,k) [Mtoe/unit] in energy consumption per unit introduction of the cohort:

Ered , m (t , i, j, k )   ered , m (h, i, j, k )  Qm (h)(A.12)hH t

For options that cause changes in the power consumption in the residential, commercial
and industrial sector, the energy balance and changes in CO2 emission were calculated by converting
the energy consumption into the rate of change in electricity consumption caused by implementing
the options in the entire power supply demand using the following equations and assuming that thechanges were changes in energy consumption in the electricity sector:

Ered ,m (t , Ele, j, k )  eBAU (t , Ele , j, k )  1  S red (t , Ele )

ix(A.13)     ered ,m (h, i, j , Ele )  Qm (h)   eBAU (t , i, j , Ele )  m hH
S red (t , Ele )     t
 (A.14)eBAU (t , i, j , Ele )i, j (3) CO2 emission
The CO2 emission CO2(t) [t-CO2] at time t was determined by multiplying theconventional-type energy consumption Econv(t,i,j,k) [Mtoe], the energy Elcs,m (t,i,j,k) [Mtoe]
consumed for introducing option m, and the coefficient of CO2 emission emfk [t-CO2/Mtoe]: emf    E
k



k

conv

i, j


(t , i, j, k )   Elcs , m (t , i, j, k )   CO 2(t )m(A.15)

The coefficient of CO2 emission of each fuel was assumed to be constant throughout the analysis
period.
In the investigation of pathways, the CO2 emission in and after 2050 was constrained to be
30% of the 1990 level or less:

CO2(t )|t  2050  (1  0.7)  CO21990(A.16)(e) Constraint on the amount of introduction
The stock Qm(t) [unit] of option m at time t is expressed as the sum total of the stocks incohort h operating at the time when there are no additional constraints:

Qm (t )   Q m (h)(A.17)hH t

The stock Qm(t) [unit] of option m was constrained to not exceed the maximum
introduction amount qmax,m [unit] adopted for investigating the 70% reduction scenarios:

Qm (t )  qmax,m(A.18)xWhen there is a prerequisite option mp, a constraint was imposed on the stock based on therelationship.
For prerequisite options of absolute connection, flag Pmp(t) [-] was established for showing
whether the prerequisite option is completed by time t or not. When the flag is 1 (the prerequisite
option is completed), the option at time t+1 was assumed to be able to be introduced up to the
maximum stock value qmax,m. When the flag is 0 (the prerequisite option is not completed), the stockat time t+1 was constrained to be zero. Assuming that an option may be in absolute connection with
two or more prerequisite measures, the flag of the smallest value was used as the constraint among
those of all prerequisite options in absolute connection:

Qm (t  1)  qmax,m   min Pm ' (t ) 
 m'm p
(A.19)

Pm (t )  1 (if Qm (t )  qmax,m ), 0 (otherwise)(A.20)A partial restriction is a relation by which the amount of stock of a prerequisite option attime t serves as the upper limit of the stock of the upper level option at time t+1. Here, the stock was
normalized at the maximum amount of introduction and was used as the constraint. As in absolution
connection, an option was assumed to be possibly in a partially restrictive relation with two or more
prerequisite options. The stock of the smallest value among all prerequisite options in partial
restriction was used as the constraint.

Q (t ) 
Qm (t  1)  qmax,m   min m ' m 'm p q
max, m ' (A.21)For the introduction (flow) of a option at time t, a constraint was imposed on theimplementation period sm [year] of the measure. The maximum introduction qmax,m [unit] wasdivided by the implementation period sm [year] to determine the largest possible amount of
introduction (flow), and the introduction of the option (flow) was constrained to be smaller than thevalue.

Q f , m (t ) qmax,m

(A.22)

smxi

Contact address:
Onogawa 16-2, Tsukuba, Ibaraki, 305-8506 JAPAN
National Institute for Environmental Studies
Center for Global Environmental Research
Senior Researcher,
Junichi Fujino (fuji@nies.go.jp)

Japan
Low Carbon
Society 2050

http://2050.nies.go.jp