G l o b a l

E n v i r o n m e n t

R e s e a r c h

F u n dDevelopment ofJapan Low Carbon Society Scenarios

Scenario Study TeamJapan Low Carbon Society Scenarios Toward 2050
Research Project on"Establishing of Methodology to Evaluate Middle to Long term Environmental
Policy Options toward Low Carbon Society in Japan"(FY2004-2008)
sponsored by Strategic Research Development Project,
Global Environment Research Fund,Japan
Ministry of the Environment, JapanLCS Research Booklet

No.3, June 2006

Low Carbon
Society 2050

http://2050.nies.go.jp

Scenario Study Team of “Japan Low Carbon Society Scenarios toward 2050”
Members
Mikiko Kainuma*: National Institute for Environmental Studies

Toshihiko Masui*: National Institute for Environmental Studies

Junichi Fujino*: National Institute for Environmental Studies

Tatsuya Hanaoka: National Institute for Environmental Studies

Shuichi Ashina*: National Institute for Environmental Studies

Yuzuru Matsuoka*: Kyoto University

Reina Kawase*: Kyoto University

Osamu Akashi*: Kyoto University

Koji Shimada: Ritsumeikan University

Go Hibino*: Mizuho Information & Research Institute

Kazutaka Oka: Mizuho Information & Research Institute

Maho Miyashita*: Mizuho Information & Research Institute

Tomoki Ehara*: Mizuho Information & Research InstituteRahul Pandey*: Indian Institute of Management, Lucknow

Manmohan Kapshe*: Maulana Azad National Institute of Technology, Bhopal

Pedro Piris-Cabezas*: Yale University* Editorial member of this report

Development of Japan Low Carbon Society Scenarios
Table of Contents

I. Overview ............................................................................................................. 1
1. Why do we need Low-carbon society (LCS)? ............................................................. 1
2. How to develop LCS? -Backcasting-........................................................................... 2
3. How to find trend-breaking possibilities?..................................................................... 3

II. Future scenarios and the evaluation framework -Case study in Japan-............. 4
1. Dominant trends and two socio-economic storylines .................................................. 4
1.1 Dominant trends of society and economy............................................................................ 4
1.2 Two scenarios ...................................................................................................................... 4

2. Innovative trend-breaks toward LCS........................................................................... 6
2.1 Trends in CO2 emissions and the points of trend-breaks in each sector ............................ 6
2.2 The trend-breaking options in the two scenarios ................................................................. 9
2.3 Tools for envisioning coherent future images .................................................................... 10

3. Scenarios towards LCS............................................................................................. 11

III. Opportunities for change................................................................................. 13

Appendix............................................................................................................ 14
A. Population and Household Model ........................................................................................... 14
B. Material Stock and Flow Model ............................................................................................... 16
C. Building Dynamics Model........................................................................................................ 18
D. Passenger Transportation Demand Model ............................................................................. 20
E. Freight Transportation Demand Model ................................................................................... 22
F. Household Production and Lifestyle Model............................................................................. 24
G. Energy Supply and Demand Balance Model.......................................................................... 26
H. Menoco Model......................................................................................................................... 28
I. Inter-sector and Macro Economic Model.................................................................................. 30
J. Environmental Options Database ............................................................................................ 33

I. Overview
1. Why do we need Low-carbon society (LCS)?
One important characteristic of the climate system is its inertia. Because of past and current greenhouse gas
emissions, a certain increase in global temperature is unavoidable. Such increases in temperature carryprofound risks. Even a small increase in temperature is likely to have significant impacts on ecosystems andspecies, and might lead to increased drought and extreme rainfalls, with severe consequences for oursociety. The Third Assessment Report (TAR) of the Intergovernmental Panel on Climate Change (IPCC)
and other, more recent, studies have indicated the following:i. An increase of even 1℃ in global average surface temperature compared to pre-industrial levels is
likely to have significant impacts on fragile ecosystems including coral reefs,
ii. Negative impacts on agriculture, water resources, and human health would appear on a global
scale for a temperature increase between 2 and 3℃,iii. Serious risk of large scale, irreversible system disruption, such as reversal of the land carbon sinkand destabilization of the Antarctic ice sheets, is more likely above 3℃. Such levels are well within
the range of climate change projections for the century.
The Central Environment Council (advisory committee for Ministry of the Environment, Japan)
proposed that it is important to steadily collect, consider, and examine the information on long-term
objectives of limiting the temperature rise, and 2℃ increase of temperature from the pre-industrial level255GHG-475ppm

2100

2090

2080

1990

0

年
Year

BaU

650
550
500
2070

0.0

GHG
475ppm
50% reduction
2060

GHG: Greenhouse gases

10

2050

475

15

2040

GHG475ppm

BaU

2030

2.0

20

2020

650
550
500

3.0

1.0

Global GHG emissions

BaU

2010

Temperatureraise
rise
Temperature(globalaverage)(global average)

2000

4.0

温室効果ガス排出量(二酸化炭素換算：GtC/年)
GHG
emissions(Gt-Ceq)

5.0

1990
2000
2010
2020
2030
2040
2050
2060
2070
2080
2090
2100
2110
2120
2130
2140
2150

Temperatureraise
rise
Temperature(abovethepre-industrial
level)(1990年=0.6℃)(above the気温上昇
pre-industrial
level)

will be good starting point for further discussion on this issue.

年
Year

GHG-500ppm

GHG-550ppm

GHG-650ppm

Fig. 1.1 Temperature rise and GHG emissions for BaU and different stabilization levels
According to our latest model calculations, in order that global mean temperature does not exceed 2℃
from the pre-industrial level, global greenhouse gas (GHG) reduction target needs to be about 50% of11990’s emissions level in 2050 and 75% in 2100 (Fig. 1.1). For per capita emission in 2050 to be sameacross the world, Japan will be required to reduce its emissions by about 80% compared to the 1990 level.
However these numbers include certain amount of uncertainty arising from global warming mechanisms
and climate impacts. A large amount of reduction is required considering the balance of sinks and sourcesof GHG. This implies that the reduction rate for Japan would have to be in the range of 60-80%.
Therefore, there is a need to design Low-Carbon Society (LCS).
2. How to develop LCS? -BackcastingCO2 emissions can be disaggregated based on the following equation referred to as the Kaya identity:
CO2＝（CO2／E）×（E／GDP）×GDP
CO2: CO2 emissions, E: Primary energy, GDP: Gross Domestic Production(CO2/E) is “carbon intensity” and improves if the share of renewables and nuclear increases. (E/GDP)

40

%

%
50

n
tio

ct
du
re

Japan(90–00)

60－00

ct
du
re

n
io

%
70

60－00
(90–00)FranceSwitchover

B2

n
io

–1

30 2
% 0%
re
r
du edu red
ct
ct uc
io
io tio
n
n
n

c
du
re

0

%
60

Revival%80(90–00)

60－00

ct
du
re

UK

Saving energy
Combine optionnioGermany–2(90–00)%90Japan scenario
toward 2030

n
io
ct
du
re

Changerate ofRate
of changeof Energy
Energy Intensity
Intensity ofof GDPGDP (%/year)(%/year)is “energy intensity” and improves if less energy is used for the same amount of GDP.

–3

–3

–2

–1

0

Changerate of of
CarbonIntensityof ofEnergy(%/year)Rateof change
CarbonIntensity
Energy(%/year)Fig. 1.2 Relationships between CO2 reduction targets and rate of change of aggregated energy intensity
and carbon intensity (dotted isoquant lines show the estimated CO2 reduction over 50 years assuming
annual GDP growth rate of 1% for each country; It must be noted that reduction levels shown here are
not the same as those reported by each country’s scenarios because they assume different GDP growth
rate)

2

Fig. 1.2 shows relationships between CO2 reduction targets and rate of change of aggregated energy
intensity/carbon intensity from historical trends, and existing GHG emissions scenarios for Japan, UK,
Germany, and France. When we assume GDP growth rate of 1% per year up to 2050, CO2 reduction rate
will be at most 40% in 2050 based on existing future emissions scenarios in Japan. This is not enough toachieve LCS targets such as 60-80% reductions in 2050. Results of current researches on Japan emissions
scenarios indicate that “forecasting” method, which considers future image as extension of current
countermeasures, is not likely to be suitable for LCS scenario development. We need ‘trend-breaking’interventions and investments. Thus we examine the “backcasting” method, which first develops emission
target representing favorable LCS visions and then discusses the method to achieve it (Fig. 1.3).

Reference
futureworld

Forecasting

RequiredSufficient calibrationin order to reflect
historical trends

Mitigation
Technology
development

Trend breaking
Intervention and
Investment

Backcasting
Checking
year

2000

2020

Long-term target year

Service demandchangeby changing social
behavior, lifestyles
and institutions

Calculation of required
intervention by dynamic
optimization model

Release of
AIM result

Environmental pressure

Technology development,socio-economic change
projected by wellcalibrated transition
modelsNormative
target world

2050

Fig. 1.3 Backcasting methodology to design LCS
3. How to find trend-breaking possibilities?
GHG emissions are related to various kinds of human activities. To achieve deep cut in GHG emissions,
not only mitigation technology development but also service demand changes by changing social behavior,
lifestyles, and institutions are required.
A large part of social infrastructure is likely to be replaced by 2050. It would be possible to propose
concrete policy packages including institutional change, technology development, and lifestyle change
toward LCS.
We have developed the narrative storylines, their quantitative scenarios, and trend-breaking
countermeasures in residential sector, service sector, transportation sector, industrial sector, energy supply
sector, and others. The desired Japan 2050 future images with 60-80% GHG reduction will be set and thepath considering economic impact, technological possibilities, and institutional and lifestyle changes will besimulated objectively and consistently using several numerical model analyses.

3II. Future scenarios and the evaluation framework -Case study in Japan1. Dominant trends and two socio-economic storylines
In order to examine the feasibility of the LCS in 2050, it is important to pay attention to the changes inseveral social factors. What would be the features of the society and the economy in 2050? The major
trends in 2050 are discussed below
1.1 Dominant trends of society and economy
One of the most obvious and drastic changes in 2050 would be the population structure. The change ismainly caused by the downturn in birthrate over the long term. The trends of depopulation and aging willcontinue until 2050 with high certainty. In addition, progress of globalization of the markets andmaturation of information society (informatization) would be the other major trends through 2050.
1.2 Two scenarios
In long-term scenarios, many uncertain factors exist, and also dominant trends as mentioned above.
Although we can draw the infinite pictures about our future, it is impossible to assess all of them. The
meaningful work is to represent the possibilities of drastic reductions of carbon emissions. In order to takefuture uncertainties into consideration, two different scenarios were developed by focusing on cause-andeffect relationships among the factors. The summaries of the two scenarios are presented below
Scenario A
Technical progresses in the industrial sectors are considerably high because of vigorous R&D investments by
the government and business sectors. The economic activities as a whole are so dynamic that average annual
per capita GDP growth rate is kept at the level of 2%. The other reasons for such high economic growth arehigh rates of consumption in both business and household sectors.
The employment system has been drastically changed from that in 2000 and equal opportunities for theemployment have been achieved. Since workers are employed based on their abilities or talents regardless of
their sex, nationality and age, the motivation of the worker is quite high in general.
As many women work outside, the average time spent for housekeeping has decreased. Most of thehousehold works are replaced by housekeeping robots or services provided by private companies. Instead, thetime used for personal career development has increased.
The new technologies, products, services are positively accepted in the society. Therefore, purchasing powerof the consumer is strong and upgrade cycles of the commodities are short.
Household size becomes smaller and the number of single-member households has increased.
Multidwellings are preferred over detached houses, and the urban lifestyle is more popular than the lifestyle ofcountryside.

Scenario B
Although average annual growth rate of per capita GDP is approximately 1%, people can receive adequate
social services no matter where they live. Volunteer works or community based mutual aid activities are themain provider of the services. Since the levels of medical and educational service in the countryside havedrastically improved, continuous migration of population from city to countryside has been observed.
The number of family who own detached dwellings has increased. The trend is especially prominent in the
countryside. The size of the houses and the floor area per houses has also increased with the increasing shareof detached houses.
The ways people work have also changed. The practice that husbands work outside and wifes work at home
is not common anymore. In order to avoid the excessive work of the partner, the couples help each other andsecure the income according to their life plan. Housework is shared mainly among family members, but freehousekeeping services provided by local community or social activity organisations are also available. As aresult of the changes in lifestyle, the time spent within family has increased. The time spent on hobby, sports,
cultural activities, volunteer activities, agricultural works, and social activities has also increased.

4

Table 2.1 Keywords of the two scenarios
Keywords
Mindset of people
Goal of life
Residence
FamilyAcceptance ofAdvanced technology
Population
Birth rate
Immigration of foreign
workers
Emigration
Landuse and cities
Migration
Urban area
Countryside

Life and household
Work

Scenario A

Scenario B

・Social success
・Urban orientation
・Self-dependent
・Positive

・Social contribution
・Rural orientation
・Cohabitation
・Prudent

・Downslide
・Positively accepted

・Recover・Status quo

・Increase・Status quo

・Centralization in large cities
・Concentration in city centre
・Intensive land use in urban area
・Significant population decrease・Advent of new businesses for
efficient use of land space・Decentralisation
・Population decrease・Maintain minimum city function
・Gradual population decrease・Local town development by local
communities & citizens

・Increase in “Professionals”
・High-income & over-worked

Housework

・Housekeeping robots & Services

Free time・Paid - for activity
・Improving carrier
・Skill development

Housing
Consumption

・Multi-dwellings
・Rapid replacement cycle ofcommodities

・Work sharing
・Working time reduction &equalization.
・Cooperation with family &neighbours
・With family
・Hobby・Social activity (i.e Volunteer
activity)
・Detached houses
・Long lifetime cycle of commodities(Mottainai)・Per capita GDP growth rate:2%・High・Per capita GDP growth rate:1%
・Not as high as scenario A

・Deregulation
・Declining GDP share・Dependent on import products

・Adequate regulated rules apply
・Recovery of GDP share
・Revival of public interest in
agriculture and forestry
・Declining GDP share・high-mix low-volume production
with local brand・Gradual increase in GDP share・Penetration of social activity

EconomyGrowth rate
Technological
Development
Industry
MarketPrimary Industry

Secondary Industry

Tertiary industry

・Increasing add value
・Shifting production sites tooverseas・Increase in GDP share
・Improvement of productivity

5

2. Innovative trend-breaks toward LCS
The two scenarios presented above are the possible pictures of future society and economy envisioned
from past trends and current situations. However, in order to achieve the goals of LCS, innovative trendbreaks must be added on the future images. What would be the potential trend-break options andcountermeasures in terms of reduction in CO2 emissions and energy consumption?
2.1 Trends in CO2 emissions and the points of trend-breaks in each sector
A. Industrial sector
Although change in CO2 emissions from the industrial sector has been small, it accounts for more than
40% of the national emissions. The possible countermeasures in the industrial sector include not only
energy efficient technologies but also dematerialization technologies and production technologies. In
addition to the technologies, changes in industrial structures and social structures (e.g. progress in
informatization and increasing relative importance of the service sector changes in consumption behaviour
and international relations) would also have considerable impacts on CO2 emissions from this sector.
B. Residential and Commercial sector
CO2 emissions from the residential sector have been increasing with the growing number of households
and people’s lifestyle changes. The trend is expected to continue with the spreading use of ICT appliancesand housekeeping robots.
One of the effective countermeasures in the residential and commercial sectors would be theenhancement of building insulation. The share of highly insulated residential buildings is much less thanthat of European countries. Approximately 60% of the heating demand from the residential sector can be
cut down if appropriate insulation systems are installed. It should be noted that insulation retrofit is also as
important as diffusion of highly insulated new houses since the proportion of new buildings constructed
every year is only a small percent of the total building stock.
Development and dissemination of high efficiency appliances would also have a great potential for
reducing CO2 emissions. The possible options would be the application of: high efficiency heat pumps(COP 6.0-8.0), high efficiency cooking appliances, fuel cells, solar photovoltaic, solar thermal, insulatedbath tub, home energy management system (HEMS) and so on.
Moreover, leading the people’s lifestyles towards environmental friendly ways by providing appropriate
information through school education and “Eco-life-navigation” systems is crucial to achieve LCS.
C. Transportation sector
Currently, passenger transportation and freight transportation account for approximately 16% and 10% ofnational final energy consumption respectively, and a great portion of the consumption comes from motor
vehicles.

6

The passenger transportation demands are heavily influenced by the lifestyle of the people and the local
conditions such as urban structure, population density and infrastructures. On the other hand, the freight
demands are affected by gross industrial production, gross imports, progress in dematerialization andgeographical distribution of producing/consuming areas.
In order to reduce the CO2 emissions from the transportation sector, modal shift from private motor
vehicles to mass transit systems such as railways, buses and LRTs, is essential. Appropriate urban planningis also a promising option. The urban planning towards compact cities is expected to shorten the average
trip distances and to encourage the shift of passenger transportation from cars to walk or bike.
Transportation substitution effects by the diffusion of teleworking and virtual communication systems also
have potential to reduce transportation demand.
In addition to the option of reducing the motor vehicle transportation demands, improvement of energy
efficiency and fuel switching of cars and trucks are also important since those modes are expected to havean important role in 2050 as door-to-door personal transportation systems, especially in less populatedareas, and as suitable facilities for small lot cargos. In particular, weight saving, application of regeneratingbrake, diffusion of fuel cell vehicles, electric vehicles, and bio fuel vehicles are the promising options.
D. Energy supply
More than 50% of the today’s electricity is generated from fossil fuel fired plant. In order to supply CO2
free electricity, electricity from fossil fuels would be restricted by the capacity of carbon capture andstorage (CCS). The other way is power generation from CO2 free energy sources such as nuclear power,
renewables and hydrogen. Current hydrogen production processes of on-site fossil fuel reforming need to
be changed since it would be economically difficult to capture the CO2 from widely dispersed power
generation. As for heat sources, consumption of kerosene, LPG, heavy oil, coal and natural gas need to be
replaced by renewable energy, heat from fuel cell cogeneration systems and heat pumps. As atransportation fuel, carbon-neutral biofuels would be a possible option besides CO2 free electricity andhydrogen.
E. Social system (Stock and waste management)
Since lifetimes of buildings and constructions have been long in general, some of the existing and newly
constructed infrastructures will still be used even in 2050. Therefore, considerable time is required until thedesired effects of the trend-breaking options are achieved in this sector and an appropriate planning with along-term perspective is required.
Infrastructure development and housing demand is affected by the amount of stocks in the society.
Since the stock in our society is almost saturated, substantial increase in the demand is not expectedhereafter.

7

As for durable products, the tendency of lighter and smaller products with high add-value has been
observed so far. Considering the trends, the substance and materials of the goods of the future will not be
the same as those of today.
In addition, considering population decrease and shift in the people’s mindset from material wealth tospiritual richness, the increase in demand of final consumption goods and investment goods would be
limited. It is important to accelerate the decoupling of economic growth and materialization.
On the other hand, considerable amounts of waste would be generated as the existing constructions end
thier lives. Since some of the materials contained in waste are used as resources, recycling is an important
issue.

8

2.2 The trend-breaking options in the two scenarios
The effects and feasibilities of the trend-breaking options illustrated above are heavily dependent on theconditions of the society. In this chapter, combinations of the options suitable for scenario A and B are
discussed.
In Scenario A, based on high technological development of the society, advent and maturation ofadvanced technologies such as fuel cells, electric vehicles, next-generation nuclear power, CCS and otherenergy efficient technologies would be the main options. Since economic growth in scenario A is relatively
high, the pace of technology advancement could compensate the effect of increase in driving forces
associated with economic growth and, therefore, cut-down the CO2 emissions.
In scenario B, on the other hand, relatively low economic growth and population decrease would leadto reduction in energy demand. However, the effects of technology development cannot be expected as in
scenario A. It is assumed in this scenario that the public hesitates to accept nuclear power and CCS, andfuel cells and electric/hydrogen vehicle technologies are not mature enough for market penetration.
Alternatively, changes in people’s lifestyle by appropriate education systems would be one of the majorcountermeasures in scenario B. In addition, application of renewable energy technologies such as solar
photovoltaic, biomass fuel, and wind should be introduced as much as possible.
Table 2.2 Possible trend-breaking options of the two scenarios
SectorIndustry
Residential
andCommercial

Scenario A
・Energy efficient production technology
・Insulation of the building
・Diffusion of all-electric home・Diffusion of high efficiency heat pump air
conditioner and water heater
・Development and diffusion of fuel cells
・Optimal energy control by HEMS

Scenario B
・Energy efficient production technology
・Insulation of the building
・Installing PV (especially in detached houses)
・Use of biomass fuels for cooling・Diffusion of solar water heating
・Education (Eco life navigation system)

Transportation

・Shortening trip distance for commuting byintensive land use
・Modal shift from cars to mass transit systems(buses, railways, LRTs)
・Diffusion of motor drive cars such as electricvehicles and fuel cell vehicles

Energy supply

・Expansion of nuclear power generation
・Electric load levelling and expansion of electric
storage (ex. Store the electricity generated innight time and use it for electric vehicles
・High efficient fossil fuel technologies+CCS
・Hydrogen production from fossil fuel+CCS
・Infrastructure development for hydrogenproduction, transportation, storage, application
・Less material use for production by technology
development
・Advancement of recycling technologies

・Urban structures becoming more compact
・Infrastructure development for foot and bikepassengers（sidewalk, bikeway, cycle parking）
・Diffusion of biomass hybrid cars
・Modal shift from cars to railways and to ship for
freight transportation
・Expansion of renewable energy use (wind,photovoltaic, solar thermal, biomass)・Application of Information technologies (IT) for
load adjustment

Stock andwaste
management

9・Expanding lifetime of the goods
・Decrease in final demand due to departure frommaterial wealth yardsticks
・Recycled product preference of the consumer

2.3 Tools for envisioning coherent future images
In order to evaluate the feasibility and social impacts of the trend-breaking options discussed above, thedriving forces associated with social changes in each sector need to be simulated. In the simulation, thecharacteristics and constraint conditions of each sector should be taken into account. It is also important toensure consistency among the sectors.
In the project, “Low carbon society scenarios towards 2050”, several element models are developed toensure the consistency of the envisioned future society. The following table illustrates relationships
between the items to be taken into account in each sector and the element models. Detailed descriptions ofeach model are given in Appendix A-J.
Table 2.3 Items to be considered in each sector and related models
Industrya.

Domestic andCommercial

b.
c.
d.
e.
f.

Transportation

g.
h.
i.
j.
k.
l.
m.

Energy supply

n.
o.
p.

Social system

Crosssectional

q.
r.
s.
t.
u.
v.

Items to be considered
Changes in industrial structure and technological development
on energy consumption as well as productivity
Changes in building distribution by climatic zone
Changes of the share of detached and multidwelling houses
Diffusion rate of insulated detached and multidwelling houses
Lifetime changes of the dwellings
Lifestyle changes on household consumption and allocation of
the time
Changes in population distribution and local characteristics
Changes in social environment and human activities
Changes in selectivity of the mode of passenger transportationby area
Changes in industrial structure
Dematerialization
Changes in producing/consuming area
Changes in selectivity of the mode of transportation by
distance
Function of load management and uncertainties of both energy
supply and demand
Combination of small consumer and small energy sources +Electricity/Hydrogen
Feasibility of local production for local consumption

Relationship between economic activities and stock/flow of
the materials
Amount of waste derived from the stock
Effectiveness of recycling and its impacts
Ensuring consistency among the sectors in terms of energy
demand and supply
Impacts of future technological choices on social energy
efficiency
Ensuring economical consistency of LCS

10Developed Models
・ Inter-sector and Macro
Economic Model・ Building Dynamics Model (b-e)
・ Household Production andLifestyle Model (f)・ Passenger Transportation
Demand Model (g-i)
・ Freight Transportation
Demand Model (j-m)

・ Energy Supply and Demand
Balance Model (n-p)

・ Material Stock and Flow Model(q-s)
・ Menoco Model (t)・ EDB (u)・ Inter-sector and Macro
Economic Model (v)3. Scenarios towards LCS
By using the element models mentioned above, scenarios for LCS were developed. Fig. 2.1 illustrates theframework and relationship between the element models.

EnergyTrns.
sectorEnergysupply anddemandbalancemodel

Industrial
sectorResidential energy
service model

Commercial energy
service modelService：Data flow

Investment

Stock
Production amountPassenger Trns.
demand model
Freight Trns.
demand model

Industrial
production model

Population
andhousehold
model：Model：Output of model

Preferenceof household

Population
Labor

Transportation
sectorHousehold Prd.
& lifestyle model

Efficiency

Material stock & flow model

Commercial
sectorEnvironmental option database, Bottom-up engineering model

Residential
sectorMacroeconomic

Activity

Inter-sector and macro economic model

Stock
Building dynamic model

Energy supply& demand

MenocoEnergybalancetable

Tool of making
energy balance
table

Fig. 2.1 Relationship among element models
The Menoco model estimates 2050’s CO2 emissions for scenarios A and B using the output from theelement models. Fig. 2.2 shows CO2 emissions in 2050 by sector. 2050’s CO2 emissions for both scenarios
decrease by 27% compared to 2000 and by 30% compared to 1990. This result shows that the scenarios
described in Chapter 2 can achieve LCS by 2050.

11

-

50

100

150

200

250

300

350

(MtC)

2000

A scenario

B scenario

Industrial
Transportation, Passenger

Residential
Transportation, Freight

Commercial
Other

Total

C/C'

C'/E

E/D

Fig. 2.3 shows factor analysis of 2050’s CO2
emission compared to 2000’s. CO2 emissions

0%

reduction is divided as follows:

-60%

-15%

-19%

-34%

-50%

∆C ∆D ∆(E/D) ∆(C' /E) ∆(C/C' ) Cross
=
+
+
+
+
C
D
(E/D)
(C' /E)(C/C' ) term

D: Driving Force（Service Demand)

-48%

-40%

-24%

-30%

-41%

-20%

-11%

-10%
-21%

E/D: Energy Intensity-80%
A scenario

B scenario

-72%

-70%
-73%

Reduction rate compared to 2000

D

Fig. 2.2 CO2 emissions by sector

C’/E: CO2 Intensity in end-use sector(without measures in power generation)
C/C’: Change of CO2 intensity by

Fig. 2.3 Factor analysis of CO2 emission

measures in power generation.

All the factors contribute to reduce CO2 emissions in scenarios A and B, but the reduction ratio by eachfactor significantly differs between the two scenarios. Reduction of demand for raw materials like steel,cement, paper and ethylene contributes to reduce driving force (D) in the both scenarios. Besides, thereduction of warming demand with diffusion of super-insulated buildings, reduction of transportation
volume through urban structures becoming more compact contribute to the reduction of D. The reduction
ratio of driving force in scenario B is larger than in A due to lower economic growth. Energy efficienttechnologies contribute to reduce energy intensity (E/D) in both scenarios. The reduction rate of E/D in
scenario B is larger due to diffusion of advanced technologies such as efficient heat pumps, electric vehicles
and fuel cell vehicles. The increase in the electricity’s share and diffusion of photo voltaic contribute to
reduce carbon intensity in the end-use sector (C’/E) in both scenarios. The reduction rate of C’/E in
scenario B is larger due to the large amount of biomass consumption which is equivalent in volume to oiland gas consumption. Efficient steam power plants contribute to reduce carbon intensity in the power
generation sector in both scenarios. The reduction of C/C’ in scenario A is larger because the share of
nuclear power is kept and the introduction of carbon capture and storage (CCS).

12

III. Opportunities for change
In this project, concrete images of LCS and their feasibilities are examined not only from environmental
viewpoints but also from economic and social points of view. Firstly, dominant trends and uncertain
factors of the future society were discussed and two scenarios, A and B, were developed. Secondly,
possible trend-breaking options suitable for the two scenarios were identified and detailed information of
the options was collected. Thirdly, appropriate element models were developed so that the changes indriving forces and other important factors can be quantitatively considered in the scenarios. Finally, thefeasibility of the options and the consistencies of the pictures of the future society were evaluated using theelement models.
The results of the study illustrate two different examples of LCS societies. The important implication of
the study is that LCS can be achieved regardless of the social background and the conditions if the goals of
LCS are shared by the people and appropriate countermeasures are applied. This in-depth study would
continue so that the worlds pictured in this project become more concrete and full of hope for as many
people as possible.
However, it should be kept in mind that only people’s actions could create a brighter future. Without
strong wills, efforts and actions of the people, the scenarios would end up in “pie in the sky”. Although,the challenges we are facing are obviously not easy to overcome, we should neither shift the blame on tothe others nor weep for the upcoming climate change. The best and the only things to do would be totackle and address the issue squarely and earnestly.
The future is full of uncertainty. In other words, it has unlimited possibilities. We should be aware that
our today’s actions shape the basis of the 21st century. In order to achieve the goals of sustainable
development, we might need to modify our old values and social systems. It is time to change and thechallenge has already begun.
We sincerely hope that the scenarios would encourage the people to conduct “creative destructions” andstep forward towards a sustainable and brighter future.

13

Appendix
A. Population and Household Model
A.1 Model Characteristics
・ The population and household model (PHM) simulates the future province-wise population by age
and sex, given exogenous data on national and province-wise base year's population, expectedsurvivorship rate, expected fertility rate, and expected migration rate. The number of households byfamily type is also calculated with the headship rate method as described in section A.2.
・ Moreover, it simulates population and households by climate zone and land-use classification with thedetailed assumption of population distribution.
A.2 Model flow chart
Fig. A.1 shows the flow chart used for estimating future population and households. The cohort
component method is used here. Each birth cohort is calculated with fertility rate, survivorship rate, andmigration rate. It states that the population at the next time interval ("t + 1") is the population at the
beginning time interval ("t") plus the net natural increase (or decrease) plus the net migration. Base year's
population, expected survivorship rate, expected fertility rate, and expected migration rate are given
exogenously. When there are inconsistencies between national parameters and provincial parameters, we
modify the provincial parameters in order to be consistent with the national ones.
The headship rate method is used for projecting the number of households. The headship rate in a given
category based on age, sex and family type is defined as the ratio of the number of heads of households tothe total number of members in that type. We use the historical trend of change in headship rate as thebasis for calculation and modify the parameter to reflect the assumed social change in order to estimate thenumber of households in the future.
Total population(Period T-1)
[Sex, Age]

Adjustment

Life table[Sex, Age]
Fertility rate
[Age]

Consistency

International
net-migration(Outsider)

Consistency

Province-wise
fertility rate
[Age]

Adjustment
Consistency

Headship rate
[Sex, Age, Family]

Total number ofHousehold(Period T)[Family-wise]

Province-wise
Population(Period T-1)
[Sex, Age]

Adjustment

International
Net-migration(Japanese)
Total population(Period T)[Sex, Age]

Province-wiselife table[Sex, Age]

: Data flow
: Exogenous variable: Endogenous variable

Province-wisenet-migration[Sex, Age]

Province-wiseheadship rate
[Sex, Age, Family]
Province-wiseclimatic division
share
Province-wiselanduse Cls.
share

Landuse Cls.-wise
Population(Period T)[Sex, Age]

Province-wise
Population(Period T)[Sex, Age]

Climatic zone
-wise household(Period T)[ Family]Fig. A.1 Flow chart for projecting population and number of households
14

Province-wisehousehold(Period T)[Family]Number of households by climate zone and population by land-use class are estimated with theassumption of current population distribution in a prefecture.
The indices of the PHM are listed in Table A.1.
Table A.1 Indices of Japan’s Population and Household Model (PHM)
Indices Classification
Elements
Age
19 Birth, 0-4, 5-9, 10-14, 15-19, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59,
60-64, 65-69, 70-74, 75-79, 80-84, 85Sex
2 Male, Female
Family5 One-person, Couple-only, Couple-Children, Parent-Children, Others
Province
47 (all prefectures)
Climate6 I (Hokkai), II (Aomori, Iwate, Akita), III (Miyagi, Yamagata, Fukushima, Tochigi,
Nagano, Niigata), IV (except I-III,V,VI), V (Miyazaki, Kagoshima), VI (Okinawa)Land-use
10 i. Three largest metropolitan area: i-1. Cities, i-2 Lowland farming, i-3 Intermediate &mountainous area
ii. Hub cities: ii-1.Cities, ii-2.Lowland farming, ii-3.Intermediate & mountainous areaiii. Prefectural hub cities
iv. Others: iv-1.Cities, iv-2.Lowland farming, iv-3.Intermediate & mountainous area

A.3 Example of Simulation Result
Fig. A.2 shows an example of simulation output. Japan’s future population will decrease by about 20%
compared to the current level. Population will tend to concentrate in Tokyo metropolitan area. As forhouseholds, the share of “one person” family will increase.

2050

2040

2030

2020

2010

2000

0

Chiba, Saitama, Kanagaw a

10
Couple-Children
0Fig. A.2 Example of output from the PHM(Left: Region-wise population, Right: Number of households by family type)

15

2050

Toky oCouple-Only

2045

Aichi

20

20

2040

Osaka, Ky oto, Hy ogo

2035

40

One-Person

30

2030

Miy agi, Hiroshima, Fukuoka

2025

Hokkaido, Tohoku Others

60

2020

80

Others40

2015

Kanto Others

2010

Chubu Others

100

Parent-Children
50

2005

Population in Japan (Million)

120

60

2000

Chugoku Others, Shikoku,
Ky ushu Others
Kinki Others

Number of household in Japan (Million)140

B. Material Stock and Flow Model
B.1 Model Characteristics
・ Material Stock and Flow Model (MSFM) estimates the change of material stocks and flow in thesociety. The factors considered in the model are final consumption and investments which are affectedby capital stocks, material densities of goods, physical input output coefficients of production sectors,
and recycling rate of wastes.
・ The input data of this model is the final demand calculated by an economic model (Macroeconomic
model). The outputs are material flow among sectors, the flow between economic activities and theenvironment, and material stocks accumulated as production capital, waste accumulation, and so on.
・ The MSFM analyzes the mechanism of changes in material stocks and flow, and the effect of recycling
materials in the future society, and looks for the measures towards the LCS in connection withmaterial consumption.
B.2 Model flow chart
Macroeconomic
Model
Investmentof Goods

Final Consumptionof Goods

Import / Exportof Goods
Building Dynamics
Model

Investment Shareof Material

Initial Material
StocksMaterial Densityof Goods

Material Demandfor Products

Material Stocks

Material Input for Construction
and Rehabilitation of Building
Physical Input Output
Coefficient for Production

Lifetime

Material Flow among
Domestic Sectors

Generated Waste

Material Import
RecycleExtraction fromthe Environment
Recycling Rate
Waste

Final Disposal

Fig. B.1 Flow chart for projecting future material stocks and flow

16

: Data flow
: Exogenous variable: Endogenous variable

The model was calibrated with the past 30 years’ socio-economic data, the material stocks data, andproduction data. As for the socio-economic data, population, the number of households, the amount ofinvestment, GDP, and import and export were considered. As for material stocks and productions, floorarea of dwellings, the number of car ownership, steel and cement production, etc, and other factors weretaken into account. Using these relational expressions and the future estimates of socio-economic indices,demands for future stocks of durable goods in each year are estimated. From the difference between the
demand for stocks and the existing stocks, the required investment is calculated. The material demand for
products, the driving force of the MSFM, is calculated by multiplying the volume of goods production by
their material density. Here, “Material Density” is defined as the weight of material per unit (ton per unitmonetary amounts, ton per vehicle, ton per m2, etc). Material input for construction and rehabilitation ofbuildings which are the output from the Building Dynamics Model, is also the driving force for the MSFM.
The amount of stocks is determined by adding new investments in the previous period and subtracting
the depleted portion. Initial material stocks are estimated by accumulating material inputs over the past 100
years.
The material flow among domestic sectors and the flows related to the extraction from the environment
and the disposal into the environment are estimated based on the concept of material balance of goods andsectors while considering parameters such as changes in the material density of the goods, changes inphysical input-output coefficients of production sectors by technology innovation, and changes in therecycling rate of materials and lifetimes of durable goods. Flows related to the extraction from theenvironment and the disposal into the environment are also estimated.
Table B.1 Classification of Materials, Goods, Sectors
Material

Iron, Cement, Woods, Aluminum

Goods

Building, Civil engineering structure, Machinery, Others

Sectors

Production sectors, final demand sectors

Production

Primary process (Pig iron), Secondary process (Crude steel),
Tertiary production (Final steel products), Final goods production*( ) Example of iron case

Final demandHouseholds, Government, Investment, Import and Export

17

C. Building Dynamics Model
C.1 Model characteristics
・ Building Dynamics Model (BDM) estimates the future dwelling stock by climate zone, type of building,construction material, insulation level and construction year by using cohort method.
・ The inputs of the model are the dwelling stock in the base year, the survival rate of dwelling stock andthe number of new dwellings which satisfy the future demand from the households.
C.2 Model flow chart
Number of dwelling stock(year T-1)

Unoccupied rate

[Region, types of buildings,
construction material, insulation level,
construction year]

Number ofhouseholds

Population andHousehold Model

Number of new dwellings(year T)

Share by type ofbuilding

Survival rat

Share by region
Share byconstruction material

Survived number of dwellings(year T)
[Region, types of buildings,
construction material,
insulation level, construction year]

Share byinsulation level

Number of new dwellings(year T)
[Region, types of buildings,
construction material, insulation level]

Renovation rate
Number of dwelling stock(year T)

Householdproduction andlifestyle model[Region, types of buildings,
construction material,
insulation level, construction year]

Bottom-up
engineeringmodel：endogenous variable：exogenous variable

Fig. C.1 BDM Flow Chart
Table C.1 Climate zone, Types of buildings, Construction material, Insulation level
Indices
Climate zone
Type of building
Construction material
Insulation level

Elements
I (Sapporo)
II (Morioka)
III (Sendai)
IV (Tokyo)
V(Kagoshima) VI(Naha)
Detached, Apartment
Wooden, Non-wooden
Without insulation, Standard of 1980,
Standard of 1992, Standard of 1999

18

The dwelling stock in the base year (2003) is from the 2003 Housing Survey of the Ministry of Internal
Affairs and Communications. As for the survival rate of dwelling stock, we estimated it with the Weibull
distribution from the Housing Surveys of 1978, 1983, 1988, 1993, 1998 and 2003.
The dwelling stock in a future year is calculated by adding the new number to the surviving stock fromthe previous year. First, the demand for dwelling stock is determined in proportion to the number ofhouseholds determined by the Population and Household Model. Next the total number of new dwellings
is determined based on the demand for dwelling stock and the surviving stock from the previous year.
Then the number of new dwelling by region, type of building, construction material, insulation level iscalculated by multiplying the total number of new dwelling with the proportion by region, type of building,construction material, and insulation level. Finally, the dwelling stock is calculated by adding the newdwellings to the surviving dwellings from the previous year.
Renovation of insulation and room expansions could also be taken into account.
C.3 Example of Simulation Results
Examples of simulation outputs are shown below. Fig C.2 shows the estimated future dwelling stock bytype of building and construction material. Reflecting the decrease in the future number of households, thedwelling stock would also decrease after peaking in 2015.
Fig C.3 shows the estimated future dwelling stock by insulation level. “Standard of 1999” is the latest
insulation level and almost comparable to the strict European insulation levels. Growth of the “Standard of1999” since 2015 results from renovation of insulation for existing dwellings. About a quarter of theexisting dwelling stock is still present in 2050, therefore not only an enhancement of insulation for new
dwellings but also renovation of insulation for existing dwellings is essential to make all the dwelling stock

Wooden detached
Wooden apartment

Without insulation
Standard of 1992

Fig. C.2 Estimated number of dwelling stock(Million)

2050

2045

2040

2035

2050

Non-wooden detachedNon-wooden apartment

2030

0
2025

0

2020

10

2015

10

2005

20

2045

20

2040

30

2035

30

2030

40

2025

40

2020

50

2015

50

2010

60

2005

60

2010

meet the “Standard of 1999” in 2050.

Standard of 1980
Standard of 1999

Fig. C.3 Estimated number of dwelling stockby insulation level (Million)

19

D. Passenger Transportation Demand Model
D.1 Model characteristics
・ Passenger Transportation Demand Model (PTDM) can simulate transportation demand associatedwith changes in population distribution, social environment, people’s activity pattern, modal share andaverage trip distance.
・ The model is based on the transportation model developed by the Ministry of Land Infrastructure andTransport (MLIT) in Japan. In this model, the transportation demand within the daily living area
(intra-region transportation) and between the daily living areas (inter-region transportation) arecalculated separately.
D.2 Model flow chart
Macro Economic Model

Population Dynamic Model

Population[Attribute, Area]
Employment[Attribute]
License[Attribute]
Population[Attribute, Area]

Population[Attribute]

Trip Generation Coefficient[Attribute, Day, Area, Objective]

Trip Generation Coefficient[Attribute, Objective, Mode]

Modal Share(%)[Day, Area, Mode]

Modal Share(%)[Attribute, Objective, Mode]

Average Trip Distance(km/Trip)[Day, Area, Mode]

Average Trip Distance(km/Trip)[Mode]
Intra-Area Transportation(person-km)[Mode]

Inter-Area Transportation(person-km)[Mode]：Data Flow

Net-Total
Conversion ratio
Passenger Transportation[Persons-km]

Intra-Area Transportation：Endogenous Variables：Exogenous Variables

Inter-Area Transportation

Fig. D.1 Transportation Model Flow Chart
The logic of calculation of intra and inter regional transportation demands are identical. Firstly, classified
population data by attribute shown in Table D.1 is calculated by license holding ratio, employment rate andoutputs from Population and Household Model. Secondly, trip generation coefficient, modal share, andaverage trip distances are multiplied to obtain the transportation demands. Exogenous variables used in themodels include: trip generation coefficient, modal share, and average trip distance. The following tableshows attributes of variables used for the simulation.
20

Table D.1 Attributes of variables in PTDM
Intra-Area Transportation
Passenger cars, Buses, Railways, Walk&Bike

Mode
Objective
Attributes
-Age
-Area

Commute to work, Commute to school,
Return, Business, Private & Shopping,
Sighteseeing & LeisureSex, Age（3）Licence, Employment
Under 14, 15-64, over 65
Metropolitan area (TMUC1, LCU2, LCC3,
Others) ×Three land types (Urban,
Agricultural, Mountainous)Inter-Area Transportation
Passenger cars, Buses, Railways, Aviation,
Maritime
Business, Sightseeing, Private, Others
Sex, Age（6）Under 19, 20’s, 30’s, 40’s, 50’s, Over 60―

1 TMUC: Three Major Urban Commuting Area, which includes Tokyo, Osaka and Nagoya urban area
2 LCU: Local Central Uraban Area
3 LCC: Local Core City

D.3 Example of Simulation Results
The following figures show sample outputs of the simulation. As shown in Fig D.4 and Fig. D.5, though
total transportation demand is decreasing, per capita transportation demand stays constant. The reasonsfor such a low growth rate in per capita transportation demand are explained by the effect of urban
structures becoming increasingly compact and decrease in average trip distance. In addition, the share ofrailways transportation will increase rapidly due to the promotion of modal shift from car to train.

450,000

1,200,000

400,000
1,000,000

350,000
800,000

300,000
250,000

600,000

200,000
400,000

150,000
100,000

200,000

50,000
0

0
2000

2005

2010

2015

2020

2025

2030

2035

2040

2045

2000

2050

2005

2010

2015

2020

2025

2030

2035

2040

2045

2050

Fig. D.2 Inter-region transportation demand by mode Fig. D.3 Intra-region transportation demand by modeof transportation (mil. person-km)
of transportation (mil. person-km)
1,600,000

1.40
1.20
1.00
0.80
0.60
0.40
0.20
0.00
1970

1,400,000
1,200,000
1,000,000
800,000
600,000
400,000
200,000

1990

2010

2030

2050

0
2000

2005

2010

2015

2020

2025

2030

2035

2040

2045

2050

Fig. D.4 Total transportation demand by mode of Fig. D.5 Total transportation demand per capita
transportation (mil. person-km)(mil.person-km/cap)
Buses

Aviation

Pass.cars

21

Maritime

Railways

Walk&Bike

E. Freight Transportation Demand Model
E.1 Model characteristics
・ Freight Transportation Demand Model (FTDM) simulates freight transportation volume associatedwith changes in industrial structure, material density, transportation distance, and modal share.
・ The inputs of the model are production and imports calculated in the Inter-sector and Macroeconomic
Model.
・ The outputs are freight transportation volumes in terms of tonne-km by mode.
E.2 Model flow chart
Inter-sector andmacro economic model“Air transport”“Surface transport”

Production and import ofhigh-value added products[JPY]

Production and import[JPY](product)Transport generation ratio[tonne/JPY](product)
Modal share [%](product, mode,
distance)

Distribution of transport distance[%](product, distance)

Representative transport distance[km](distance)

Transportation volume[tonne-km](product, mode)

Transportation volume[tonne-km]

Transportation volume[tonne-km](mode)：Data Flow
：Endogenous Variables：Exogenous Variables

Fig. E.1 FTDM flow chart
Different flows are used for surface transport and air transport. For surface transport, firstly,transportation volume in terms of tonne is calculated by multiplying gross production (domestic
production + imports) by transport generation ratios. Secondly, transportation volume in tonnes in eachtransport distance is calculated by using distribution of transport distance. Thirdly, transportation volumeof each mode of transport in tonnes is calculated by using modal share in each transport distance band.
Finally, transportation volume in terms of tonne-km is calculated by using the representative transportdistance of each distance band. Transport generation ratios are estimated endogenously using regression
equations identified from historical data. Distribution of transport distance, modal share, andrepresentative transport distance of each distance band are given exogenously.
For air transport, transportation volume in tonne-km is directly calculated with production and importsof high-value added products.
22

Table E.1 Indices of FTDM
Indices
Mode
Product

Elements
Small Freight Vehicle, Large Freight Vehicle, Railway, Maritime, Aviation
Agricultural Products(AG), Minerals(MI), Metals and Machineries(ME),
Chemicals(CH), Light Industries Products(IL), Miscellaneous Industries
Products(IM), Specialty Products(SP), Others(OT)
Transport distance ~100km, 100~300km, 300~500km, 500~750km, 750~1000km, 1000km~
band
E.3 Example of Simulation Results
The following figures show sample outputs of the simulation. It is assumed that the sum of production andimports for the primary and secondary sectors in 2050 in scenarios A and B is 1.43 and 1.10 times that ofyear 2000 respectively. It is estimated that total transportation volume in tonne-km in A and B scenario is
only 0.91 and 0.79 times that of year 2000 (Fig. E.5). This is partly because the proportion of fundamental
materials in production and imports decreases. Moreover, reduction of weight of each unit of product plays
a role. Those effects can be seen in the weight reduction of transportation volume(Fig. E.2). As for thetransportation volume by transport distance, short distance transport maintains a large share (Fig. E.4).
This results in the large modal share of freight vehicle (Fig. E.3) because of the advantage of freight vehicle
over other modes for short distance transportation.
8,000,000

8,000,000

7,000,000

7,000,000

6,000,000

6,000,000

5,000,000

5,000,000

4,000,000

4,000,000

3,000,000

3,000,000

2,000,000

2,000,000

1,000,000

1,000,000
0

0
1980

1990

2000

A

1980

B

1990

2000

A

2050
AG

MI

ME

CH

IL

IM

B
2050

SP

OT

Freight Vehicle

Railway

Maritime

Fig. E.2 Transportation volume in tonnes by product Fig. E.3 Transportation volume in tonnes by mode
(1000 tonne)(1000 tonne)
8,000,000

700,000

7,000,000

600,000

6,000,000

500,000

5,000,000

400,000

4,000,000

300,000

3,000,000

200,000

2,000,000

100,000

1,000,000

0

0
1980

1990

2000

A

1980

B

～100km100～300km

2050
300～500km

500～750km

750～1000km

1000km～

1990

2000

A

B
2050

Small Freight Vehicle
Railway

Large Freight Vehicle
Maritime

Fig. E.4 Transportion volume in tonnes by transport Fig. E.5 Transportion volume in tonne-km by mode
distance (1000 tonne)(mil. tonne-km)

23

F. Household Production and Lifestyle Model
F.1 Model characteristics
・ The Household Production and Lifestyle Model (HPLM) simulates energy service demand, waste
generation, and water consumption for household production by four household types, underprescribed scenarios of household composition, age composition, income budget, and time budget
in the future.
・ The model can consider demographic and socioeconomic trends with consistency, together with
Population and Household Dynamics Model, Building Dynamics Model, and Inter-sector andMacro Economic Model.
F.2 Model flow chart
Time use
Historical trend
scenario

Disposalincome:Data flow
:Exogenous variable

Liner Expenditure System:Endogenous variable

Household
Stock Capital

Amusement

Goods and service preference module

Clothing

Food

Education

Housework

HealthResidence

Sleep

Other

Nesting structure of household production
Material and energy balance module

Revealed
Energy service
demandRevealedWater
demandRevealed
Material
demandFig. F.1 Flow chart of HPLM
Fig. F.1 shows the flow chart of HPLM. This model consists of two modules; “goods and service
preference module” and “material and energy balance module”. “Goods and service preference module”
estimates the final consumption expenditure from disposal income, household stock, and time use; italso estimates production of household service with the final consumption expenditure. “Material andenergy balance module” estimates environmental load generations by the household service production.
In the model, disposal income is divided between final consumption expenditure and saving. Finalconsumption expenditures are spent on goods and services which are required for the householdproduction. Preferences of goods are estimated with the historical household survey. The same is done

24

for the time use budget. Produced household benefits are classified into nine categories, and about 400
kinds of goods, time use, and household capital stock are modeled as the inputs for the production in anested structure.
Furthermore, required energy service demand, water demand, and material demand for householdproduction are summed up, considering time delay, conversion waste, and other factors, in order tocalculate the generation of environmental loads.

Population and household
Dynamic model

Building
Dynamic model

Macroeconomic model

Building
characteristics

Demographic information

Disposal income
Interest rate, Good priceHousehold production and lifestylemodel
Revealed demands:Data flow

Endusemodel

CO2emission

Energycomsumption:Endogenous variable:Model

Solid
Waste

Water
pollutantsAir
pollutants

Fig. F.2 Frameworks of HPLM and other models
F.3 Example of Simulation Result
Fig. F.3 shows the growth of the household consumption expenditure by 2030. The growth of “oneperson” household consumption expenditure is larger than that of “couple-only, couple-children” or“parent-children” household consumption expenditure. In Fig. F.4, CO2 emissions by residential energy

45

one-person (R)

80000

40

couple-only,
couple-children (R)parent-children (R)

70000

35
30
25

others (R)20

one-person (E)155couple-only,
couple-children (E)parent-children (E)0others (E)101995 2000 2005 2010 2015 2020 2025 2030
Year(R) Reported value(E) Estimated value

Fig. F.3 Household consumption expenditure

CO2 emission (103 t-C)

Household consumption expenditure(104 yen/household・month)consumption and by transport activity for household production is shown.

Taxi service
Bus service

60000

Rail service

50000

LPG

40000

Tow n gas

30000

Electricity
Kerosene

20000

Diesel oil

10000

Gasoline01995 2000 2005 2010 2015 2020 2025 2030
Year

Fig. F.4 CO2 emission from household activities
25

G. Energy Supply and Demand Balance Model
G.1 Model characteristics
・ Energy Supply and Demand Balance Model (ESDBM) seeks optimum configurations of the energy
system based on the energy balance between supply and demand, in order to take full advantage of
renewables in specific regions.
・ The model requires the following information as exogenous variables: wind synopsis, amount of solar
insolation, and stocks of woody materials in regions.
・ Major outputs of the model are: optimum capacity of each system, energy supply, total energy systemcost, and CO2 emissions.
G.2 Model flow chart

Fig. G.1 ESDBM flow chart
Fig. G.1 shows a scheme of ESDBM. The model configures regional low-carbon energy systems on thebasis of regional characteristics of natural resources and energy demands. In the model, Japan is divided
into several regions depending on the agricultural structure and climatic conditions. The optimum energy
system is configured based on the regional characteristics. The energy demands are determined by othermodel elements, i.e., Population and Household Model, Building Dynamics Model, Household Production

26

and Lifestyle Model, and so on. Data of natural resources, such as wind synopsis and amount of solar
insolation are based on actual observation.
In order to explore future LCS, the model employs two types of energy supply systems: (1) distributed
energy system, such as micro-grid and/or 1kW-scale fuel cell combined heat and power system, and (2)centralized energy system, such as grid electricity and district heating system. The model can shed somelight on the roles between distributed systems and centralized systems. Renewable energy component
includes wind turbine generation, photovoltaic generation, woody biomass cogeneration, and solar heating.
In order to analyze the effectiveness of the renewable system, the model considers energy storing capacities,
such as batteries or hydrogen storage. Furthermore, the following technological mitigation options aretaken into account:

CO2 reduction options in the electricity sector, advancing nuclear power, andinstallation of carbon capture and storage (CCS).

27

H. Menoco Model
H.1 Model Characteristics
・ In Japanese, “Menoco” means “back of the envelope” type calculation. The menoco model isdeveloped on the spreadsheet as shown in Fig. H.1. Given the data on service demand, share of energy.
and energy improvement by type of service and energy in 2000/2050, the model calculates the energybalance table and the CO2 emission table while keeping consistency among sectors.
・ Since users can conduct sensitivity analysis with different parameters promptly, the model is suitablefor communication among stakeholders to design LCS. Besides, the model can be used as a simple
assessment tool of output from various models.

Residential

Þ Energy service demandHousehold
Cool
Warm
Hot Water
Cooking(S)Cooking(E)Lighting
Refrigerator
ICT
ApplianceÞ Energy consumption / CO2 Emission

Unit

2000

Mil.
Mtoe
Mtoe
Mtoe
Mtoe
Mtoe
Mtoe
Mtoe
Mtoe
Mtoe

46.8
3
17
11
1
1
3
4
2
10

2050 BaU
CM
A
B
A
B
43.2 42.1
5
3 90% 90%
15
13 67% 67%
7
12 80% 80%
1
1 100% 100%
1
1 100% 100%5
3 80% 80%
33 100% 100%
32 100% 100%
13
10 100% 100%

COL

OIL

GAS

59%
55%
44%

15%
33%
56%

OIL

GAS

BMS

0.90
0.75
0.45

0.90
0.75
0.45

0.90
0.75
0.45

2050 CMA
B
43.2 42.1
4
3
10
9
6
9
1
1
1
1
4
3
3
3
3
2
13
10

UMit
EnergyConsumption

2000
2050 A-scenario
2050 A-scenario
2000
2050 A-scenario
2050 B-scenario
2000
2050 A-scenario
2050 A-scenario

Emission
Factor
CO2 Emission

COL OIL GAS BMS S/W Heat
0
21
9
0
1
0
0
1
1
0
8
0
0
2
2
7
18
0
1.00 0.80 0.55 0.00 0.00
1.00 0.80 0.55 0.00 0.00
1.00 0.80 0.55 0.00 0.00
0
16
5
0
0
0
1
0
0
0
0
1
1
0
0
-

H2
0
4
0
0.00
0.53
0.00
0
2
0

ELE Total
23
54
14
28
8
37
1.19
0.10
0.65
27
49
1
5
5
7

Þ Service Share

Cool
Warm
Hot Water
Cooking(S)Cooking(E)Lighting
Refrigerator
ICT
Appliance

BMS

2000
S/W HeatH2

7%2050 A-scenario
BMS S/W Heat

ELE Total COL
100% 100%26% 100%
5% 100%0% 100%100% 100%
100% 100%
100% 100%
100% 100%
100% 100%

OIL

GAS

5%
5%
5%

5%
5%
5%

ELE Total
2.84
3.69
0.62
0.70
1.00
1.00
1.00
1.00
1.00
-

OIL

GAS

2050 A-scenario
BMS S/W Heat

0.90
0.95
0.55

0.90
0.95
0.55

0.90
0.95
0.55

10%

H2

10%
10%

ELE Total COL
100% 100%80% 100%
70% 100%
90% 100%
100% 100%
100% 100%
100% 100%
100% 100%
100% 100%

OIL

GAS

2050 B-scenario
BMS S/W Heat

5%
10%
15%

5%
10%
20%

50%
20%
35%

ELE Total
8.00
8.00
6.00
0.80
1.11
1.50
1.50
2.00
1.50
-

OIL

GAS

0.90
0.95
0.55

0.90
0.95
0.55

H2

ELE Total
100% 100%40% 100%
30% 100%
30% 100%
100% 100%
100% 100%
100% 100%
100% 100%
100% 100%

2050 B-scenario
BMS S/W Heat

H2

0.90
0.95
0.55

0.55

ELE Total
8.00
8.00
6.00
0.80
1.11
1.50
1.50
2.00
1.50
-

30%

Þ Energy efficiency
COL
Cool
Warm
Hot Water
Cooking(S)Cooking(E)Lighting
Refrigerator
ICT
Appliance

2000
S/W Heat

1.00
0.45

H2

COL

0.95
0.55

1.00
1.00

H2

COL

0.95
0.55

1.00
1.00

Fig. H.1 Menoco (only a portion for the Residential sector is shown here)
H.2 Model flow chart
Fig. H.2 shows the flow of estimating energy balance table and CO2 emission table. The model calculates
energy consumption of the end-use sectors, e.g. industrial, residential, commercial, and transportation
sector, by energy type and service type, using exogenous data for service demand, mixture of energy, andenergy improvement. Then it calculates energy consumption in power generation from the electricity
demand in the end-use sector. Energy balance table is created with the energy consumption in end-use
sector and power generation sector. Finally CO2 emission table is created with CO2 emission factor given
exogenously.
Sector, service, energy as indices of Japan’s Menoco include the elements shown in Table H.1.
28

Service Demand[Service]

Share of Energy[Service, Energy]

EnergyImprovement[Service, Energy]

Energy consumption
in Industrial sector[Service, Energy]
Energy consumption
in Residential sector[Service, Energy]
Energy consumption
in Commercial sector[Service, Energy]
Energy consumptionin Transportation sector[Service, Energy]
Electricity
demandEnergy consumptionin Transformation andPower Generation[Energy]

Energy Balance
Table[Sector, Energy]
CO2 Emission
Factor[Energy]

CO2 Emission
Table[Sector, Energy]: Data flow
: Exogenous variable: Endogenous variable

Fig. H.2 Flow chart for Menoco
Table H.1 Indices of Japan’s Menoco
Indices Classification
Elements
Sector
19 Industrial, Residential, Commercial, Transportation
Service
32 Industrial : Crude steel production, Cement production, Ethylene production, Paper
production, other production by industrial category
Residential : Warming, Cooling, Hot water, Cooking, Lighting, ICT, Other services
Commercial : Warming, Cooling, Hot water, Cooking, Lighting, Motor others
Transportation, Passenger: Cars(Mini/Small/Medium), Buses, Railways, Maritime, Aviation
Transportation, Freight : Cars(Mini/Small/Medium), Railways, Maritime, Aviation
Power generation
Energy10 Coal, Oil, Gas, Biomass, Hydro, Nuclear, Solar/Wind, Hydrogen, Heat, Electricity

H.3 Example of simulation result
See Chapter 2.3

29

I. Inter-sector and Macro Economic Model
I.1 Model characteristics
・ Inter-sector and Macro Economic Model (IMEM) consists of a static general equilibrium module for asingle country and a macroeconomic module.
・ One of the purposes of this model is to present the consistent pictures to achieve the drastic CO2
emission reduction in 2050 taking into account the countermeasures assessed in the individual models
explained above.
I.2 Model flow chart
Household production
and lifestyle model

Narrative scenario

Other models

Macroeconomic
modulePreference

Expected economic
growthand labor force

Technology(input-output coefficient,energy efficiency, ...)

International price,trade andgovernment activity

Production, consumption(IO table & energy balance table)
and CO2 emissions: Data flow
: Exogenous variable: Endogenous variable

Fig. I.1 IMEM flow chart
The parameters in the CGE module are calibrated from the input-output data in 2000. The input data are
updated to assess the future economic activities from the narrative scenario, the models explained in thispaper, and the macroeconomic module representing the future values related to the account of macro
economy.
Input data include expected economic growth, labour endowment, capital endowment, technology
improvement (input-output coefficient and energy efficiency), preference change, ratio of imported goods
to domestic goods, international price, etc.
Based on the above input data, the commodity prices and activities are calculated to balance the demandand supply of each commodity. The outputs include the input-output table, energy balance table, and CO2
emissions in the target year.

30Macroeconomic module

energyoutput coefficient
on non-energy

input

data in
2000

commodity demand

import&export

SNA

input coefficient
on non-energy
IO tablein 2000⑩②⑥
production and income⑪
outputext. trnsact.
account⑥

output matrix

assumption⑧①

V matrixin 2000

tech

account ofinc. & expnd.

tech&preference

input matrix

energy balance table(output)

nonenergy①

CO2⑥
production and cost

output coefficient ①
on energy
tech

⑫

⑦

balance① production function

fixed capitalformation
matrix in 2000
tech②

capital profile
in sector⑨
productionsocialfixed capital stock
matrix⑪
capital stock⑩

compensationof cap. goods

③

⑫

②

commodity supply

capitalintermediatedemand

cap.
goods

④

labor

final demand sector

energy balance table(input)value nonadded energy

energy balancetable in 2000⑤

investment

energytech⑦

economic
growthproduction sector①

final cons

Input coefficienton energy

CGE module

condition⑦ expenditure and income in household andgovernment② commodity market⑧ assumption of import and export③ capital market⑨ fixed capital stock matrix④ labour market⑩ investment goods market⑤ calculation of GDE⑪ capital stock⑥ expenditure and income in production sector⑫ CO2 emissions
Fig. I.2 Structure of IMEM
Although the number of classifications in the basic data is more than 100, the commodities and sectors areaggregated in order to meet the research objectives. The table I.1 shows the examples of aggregatedactivities and commodities.

31

Table I.1 Examples of aggregated activities and commodities in the CGE module
Activities
Commodities
Primary industry
MiningManufacturing

Agriculture / Forestry / FishingPrimary energy

Coal mining / Crude oil and natural
gas mining / Other mining
Food products and beverages /Textiles / Pulp, paper and paper
products / Publishing and printing /Chemical materials / Chemical
products / Petroleum products /Coal products / Non-metallic mineralproducts / Pig iron and crude steel /Other steel products / Non-ferrous
metal / Fabricated metal products /Machinery / Electrical machinery,equipment and supplies / Transport
equipment / Precision instruments /Other manufacturing

Secondary energy

Primary industry
Other mining

Construction

manufacturing

Nuclear power plant / Thermal
power plant / Hydro power plant /Geothermal plant / Photovoltaic
generation / Wind power plant /Waste power plant / Biomass power
plantPower plant

Town gas
Water supply
Service

Transport andcommunications

Coal / Crude oil / Natural gas /Nuclear / Hydro / Geothermal /Photovoltaic / Wind / Waste /Biomass
Coaks / Other coal products /Gasoline / Naphtha / Jet fuel /Kerosene / Light oil / Heavy oil /LPG / Other petrorium products /Town gas / Electricity / Hydrogen/ HeatAgriculture / Forestry / Fishing
Food products and beverages /Textiles / Pulp, paper and paper
products / Publishing and printing/ Chemical materials / Chemical
products / Non-metallic mineralproducts / Pig iron and crude steel/ Other steel products / Nonferrous metal / Fabricated metal
products / Machinery / Electricalmachinery ,equipment and supplies/ Transport equipment / Precision
instruments / Other manufacturing

Construction
Water supply

Wholesale and retail trade / Financeand insurance / Real estate / Public
service activities / Other service
activities
Railway transport / Road transport /Water transport / Air transport /Other transport / Communications

Service
Transport andcommunications

Wholesale and retail trade /Finance and insurance / Real estate/ Public service activities / Other
service activities
Railway transport / Road transport/ Water transport / Air transport /Other transport / Communications

I.3 Example of Simulation Results
350

Tri. yen at 2000 price

300

2000

2050 A

2050 B

250
200
150
100

0

Agriculture
Forestry
Fishing
Coal mining
Crude oil & NG mining
Other mining
Food products & beverages
TextilesPulp, paper & paper products
Publishing & printing
Chemical materials
Chemical products
Petroleum products
Coal products
Non-metallic mineral
Pig iron & crude steel
Other steel products
Non-ferrous metal
Fabricated metal products
MachineryElec.machine, equip. & supplies
Transport equipment
Precision instruments
Other manufacturing
Construction
Thermal power plant
Non-thermal power plant
Town gas
Water supply
Wholesale & retail trade
Finance & insurance
Real estate
Railway transport
Road transport
Water transport
Air transport
Other transport
Communications
Public service activities
Other service activities

50<Sectors>

Fig. I.3 Example of the CGE module simulation

32

J. Environmental Options Database
J.1 Model Characteristics
・ Environmental Options Database (EDB) is a database system that stores information of activities
which accompany or reduce GHGs emission. Activity includes energy technology, institution,
infrastructure, lifestyle, and other aspects. As shown in Fig. J.1, narrative description andquantitative value are entered in the form. All the items in the activity form are shown in Table J1.
・ The EDB has a module for estimating
future GHGs emission. Given the data on
future driving force and penetration ofactivities, the module calculates energy
consumption, GHGs emission, stock ofactivity, and other outputs. User cananalyze the calculated projection using
pivot table/graph as shown in Fig. K.2.

Fig. J.1 Form of Activity in EDB

Table J.1 Items of Activity
Item
Activity type
Description
SectorActivity unit
FigureMemo
Lifetime
Fixed cost
O&M cost
Input
OutputAffected activity

Content
Type of the activity
Description of the activity
Sector activity belongs
Unit of activity amount
Picture or illustration of the activity.
Data source, estimation method, reference etc.
Lifetime.
Fixed cost per activity.
Operating and maintenance (O&M) cost per activity. O&M cost here does
not include the cost which accompanies inputs.
Input per activity.
Output per activity.
(This item is available when the activity type is “Activity to influence other activity”)

Increase (+)/decrease (-) rate of affected item of other activity

Affected flow
Environmental load
Penetration
Reference(This item is available when the activity type is “Activity to influence flow”)

Increase (+)/decrease (-) rate / quantity of affected flow.
Time-wise value of environmental burden emission per activity. The emission
includes the direct emission, not include the emission with input.
Penetration rate of the activity
Reference33

Fig. J.2 An example of simulation output of EDB using pivot function

J.2 Model flow chart
The members of LCS2050 project provide the information on the activities. The database plays the roleof information exchange platform for the teams of LCS2050. The estimation module in the EDB
calculates future projections under the scenarios of alternative policy. If the share of the activity (among
the competing activities) is estimated based on the objective of cost minimization, the bottom-up
engineering model is used. Menoco Model and Inter-sector and Macro Economic Model use energy
improvement and countermeasure cost from the simulation output of EBD and/or Bottom-up
engineering model.
(Team of LCS2050)

Data

Data

EDB

(Energy improvement. etc.)

MenocoData(Cost, In/Out etc.): Data flow
: Exogenous variable: Endogenous variable

Inter-sector andmacro economic
model

Bottom-up
Engineering
Model

Data

(Energy Improve-.
ment and Cost etc.)

Fig. J.3 Data linkage of models via EDB

34

"Research Project on Establishing of Methodology to Evaluate Middle to Long
term Environmental Policy Options toward Low Carbon Society in Japan (Japan
Low Carbon Society Scenarios toward 2050)" (FY2004-2008)
The first great step to prevent global warming was taken by Kyoto Protocol which came into effect on
Feb.16, 2005. But it is necessary to reduce GHG (Greenhouse gases) emissions drastically to stabilize
climate change. Japan is also required to assess its long-term global warming policy. A large part ofsocial infrastructure is likely to be replaced by 2050. It would be possible to propose concrete policy
packages including institutional change, technology development, and lifestyle change toward low
carbon society.
This project focuses on the following issues: 1) long-term scenario development study to integrate
environmental options consistently using simulation models, 2) long-term GHG reduction target
setting considering effectiveness and validity, and 3) assessment of environmental options
considering future socio-economic conditions in a) urban system, b) information technology (IT)society, c) transportation system, and d) industrial change. We have the above 6 sub projects
consisting of research experts in those areas and develop social and technically consistent middle
and long-term global warming policy. To show probable paths toward a low carbon society in Japan
which is compatible with economic development, would enhance public interest and lead to social
and lifestyle changes. We propose to offer the latest research findings.
We have simulated the required GHG reduction for Japan. We have investigated the scenarios toward2050 with back-casting method. The desired Japan 2050 future images with 60-80% GHG reduction
will be set and the path considering economic impact, technological possibility, institutional andlifestyle change will be simulated objectively and consistently.
[Researchers]
Project Leader: Shuzo Nishioka (NIES)Team Leader: Mikiko Kainuma(NIES) for scenario study, Norichika Kanie(TITech) for target setting,Keisuke Hanaki (University of Tokyo: UT) for urban system, Jun Fujimoto(UT) for ICT (Information andCommunication Technology) based society, Yuichi Moriguchi(NIES) for transportation system,
Yoshifumi Fujii(Bunkyo University) for industrial change, and about 50 other researchers.

Further information: http://2050.nies.go.jp/
Contact person: Junichi Fujino (NIES), fuji@nies.go.jp
http://2050.nies.go.jp