ALPS International Symposium
Toranomon Hills Forum
February 19, 2019

Innovations of products, services andsocial systems, and their impacts on
climate change mitigation measures
Keigo AkimotoGroup Leader, Systems Analysis Group
Research Institute of Innovative Technology for the Earth (RITE)

Contents
1. Empirical analyses for decoupling
2. Toward Green Growth
3. Potential drastic changes of energy demands
induced by progresses of AI, IoT etc.
4. Scenario analyses for the Long-term target of theParis Agreement: Impacts of car- & ride-sharing
5. Conclusions and challenges for the future2

1. Empirical analyses for decouplingGlobal GDP growth vs CO2 emission
Energy-related CO2 emissions (GtCO2/yr)

34

Factors of emission reduction in 2015 from thelong-term trend2015

1.09 GtCO2
32

30

2010

Linear regression (2000-2015)
y = 0.5211x + 2.2712
R² = 0.9793

28

26

24
2000

22
40

45

50

55

60

4

65

GDP (Trillion US$/yr)

Factor2015 reduction
effects

Explanation

Reductions in
global ironproduction

about 250
MtCO2/yr

Decrease in global ironproduction of about 100 Mt(particularly in China)

Reductions in
global cement
production

about 50
MtCO2/yr

Decrease in global cement
production of about 170 Mt(particularly in China)

Increase in shale
gas in the U.S.

about 220
MtCO2/yr

Shifts from coal to shale gas
economically in the U.S.

Expansion ofrenewableenergies

about 160
MtCO2/yr

Higher expansions by
1.2%/yr point compared withthe average annual
expansion rate

Reduction in
CO2 emissionsof Japanabout 40
MtCO2/yr

Due to Fukushima-daiichi
nuclear power accident, theemission in Japan increased
as a trend.

GDP (Trillion US2005$/yr)
Source) IEANote) GDP: Real, MER, in 2005 priceSource) http://www.rite.or.jp/system/events/akimoto_ALPSII_2017.pdf- Fundamentally, a strong positive correlation between global GDP and CO2 emissions isobserved. Although global emissions were almost flat between 2013 and 2016, this can be
regarded as adjustments of larger emissions growth in 2009-13 to the long term trend.
- Major contributions of this leveling were production adjustment of steel or cement
sectors mainly in China, and a shift to shale gas in U.S. Impact of increase in renewableenergy diffusion seemingly is relatively small.
- Global CO2 emission is in upward trend again in 2017 and 2018.
- Prospect for decreasing global CO2 emission is not so optimistic.

Global GDP v.s. electricity consumption
25000

Y2015
2015年
Y2010
2010年
Y2009
2009年

Electricity (TWh/yr)

20000

Linear regression
1971-2015年の間の(1971-2015)
実績値の線形回帰式

15000

y = 340.64x - 1308
R² = 0.9984

10000

5000

0
20

30

40

50

60

70

80

GDP (Trillion US2010$/yr)
Source) IEA, 2017

Note) GDP: Real, MER, in 2010 priceThe relationship between the global GDP and electricity consumption shows a strong linearity.
Energy particularly electricity play an important role for economic activities after the industry
revolutions.

5GDP vs CO2 emissions in major countries: overview
61971～2015 (From 1990 for EU28)

25

CO2 per capita [tCO2/yr]

20

2007

1995

15
Korea

0

World

India

Japan

2008
UK

1982
Spain

Italy

EU28
France• On the other hand, CO2
emissions per capita vary
widely among countries withsimilar GDP per capita, due toheterogeneity in their land area
and industrial structure.

• Switzerland, Sweden andFrance are thought to be on theleading edge of decoupling
trend because of their small
CO2 emissions despite theirrelatively high GDP. But theiremission levels have
Desirable trend of decoupling
conventionally been low due to2000high ratios of hydro and2011
nuclear.

2013

2000

5

Australia

US
CanadaGermany

10
Malaysia
China

2008

• Several developed countries
seem to follow decoupling
trend, i.e. GDP increases while
CO2 emission decreases.

SwedenSwitzerland• Increase of historical CO2
emissions by China is muchsteeper than forerunners.

• Detailed investigation is
0
10
20
30
40
50
60
70
80 required to conclude whether
these trends are truly
Source) IEA
GDP per capita [thousand US$/yr] (in 2010 exchange rate)
contributing to global
decoupling, considering
Although several developed countries appear to be following decoupling trend international sharing of industry
as a whole, it is hard to reach a clear conclusion as various and complicatedand domestic industrial
factors are entangled.
structure.

1.2
1.1
1.0
0.9
0.8
0.7
0.6
0.5

7

Source: estimated by RITE

US
Japan
UK
Sweden- In terms of the productionbased CO2 emissions per GDP,the degrees of improvement of
the four countries differs greatly.

2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014

1.3
1.2
1.1
1.0
0.9
0.8
0.7
0.6
0.5

Production-based CO2 per GDP

Consumption-based CO2 per GDP

US
Japan
UK
Sweden

2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014

[Year
2005=1.0][2005年=1.0][Year
2005=1.0][2005年=1.0]

Per-GDP CO2 Emission in US, UK, Sweden and Japan:
Production-base v.s. Consumption-baseNote: 2010 local currency base

- However, concerning theconsumption-based emissions,the improvement rate of the four
countries does not differ thatmuch when excluding the impact
of Japan’s emission increase
due to the shutdown of nuclear
power generation after the
Fukushima Daiichi nuclear
power accident during the Great
East Japan Earthquake.

Energy cost share (2014) vs Economic growth (2000-14)
of industrial sectors in UK

8

Source: K. Nomura, https://www.dbj.jp/ricf/pdf/research/DBJ_RCGW_DP60.pdf (in Japanese)

The industrial sectors having high share of energy costs in the total costs showed relatively
small growth rate between 2000 and 2014. These sectors shifted to outside the UK according to
the analyses of consumption-based CO2 emissions.

2. Toward Green GrowthOverview of Shared Socioeconomic Pathways (SSPs)
Fossil fuel price: low;
Fossil fuel resources: high;GDP: very highTech. improve: low;
Population: low;
GDP: low

MiddleTech. improve.：high;Public acceptability oflarge-scale tech.: low;
Population: low;
GDP: highGovernance: low;
Price distributionof fossil fuelenergy prices: big

10

Categorization of deep emission reduction scenarios
for below 1.5 °C

11Source) IPCC SR15

Much lower energy
demand scenarios
than those of SSP1
- Low energy demand is
induced autonomously on
economic principle throughtechnological and social
innovations- Low carbon prices (businessbased measures even without
strong climate polices)

SSP1
Low

SSP2
(Middle scenario)
High

Final energy demands

 The total risk management is important, andvarious kinds of technologies play their own roles.
 On the other hand, opportunities of innovations in
end-use technologies, induction of low energy
demands, and their impacts on total climatechange mitigation should be more focused. (P1)

SSP5

- High carbon prices(harmonization among
nations are required to
avoid carbon leakage)- Large-scale deploymentsof CDR, e.g., CCS, BECCS,
DACS, are required.

CO2 marginal abatement costs (carbon price)for 1.5 °C and 2 °C targets

12

P1
P2
P3
P4

P2,3,4：about 1000-2000 $/tCO2
P1：about 300 $/tCO2

Source) IPCC SR15

P1 (Low energy demand scenario):
about 150 $/tCO2

P2, P3, P4：over 400 $/tCO2

 The carbon price under the P1 (Low energy demand) scenario is much lower than others (P2-4).
Note) The carbon prices for 1.5 °C consistent pathways are estimated to be about 3-4 times higher thanthose for 2 °C pathways according to the IPCC SR15. Some models do not obtain the feasible results for 1.5°C pathways.

Range of baseline emission pathways based on different
socioeconomic scenarios and pathways for 2 °C target

13

SSP: Shared Socioeconomic Pathway(used for IPCC scenario analyses etc.)
Baseline emission
range of SSPs

2 °C goal(>66% & >50%)- There are large uncertainties in baseline emissions due to uncertainties insocioeconomic development. The uncertainty range is much larger than that fordifferent target levels of temperature (e.g., ±0.5 °C, that is, 1.5 to 2.5 °C target).
- It is significant to achieve such low emissions with net negative costs throughtechnological and social innovations.

Image of standard scenarios by models andscenarios required for deep cuts in a real world
Model world:
Ordinary technology progress

Realistic world requirement:
Innovations stimulated & implemented

Baseline scenario

Baseline
scenario
CO2emission

CO2
emission

14

Intervention scenario

Intervention scenario

By technology
and social
innovations(additional costs)(additional costs)

Carbonprice

Carbon price/
Marginal abatement cost

Carbonprice

Implicit or explicit carbon price/
Marginal abatement cost

High carbon prices of over 100$/tCO2 in real price are unlikely to be accepted globally in areal world. Technology and social innovations which will bring low (implicit or explicit)
carbon prices (including coordination of secondary energy prices) are key to achieve
deep emission cuts.

Opportunities for Green Growth15

Opportunity for Green Growth

Explanation[I] Long-term
green
growthworldwide

Internalization of damage cost by
climate change impact currently
regarded as external costs by GHG
emissionse.g. Governmental regulations, standards, labelling or implicit carbon
pricing, voluntary action or internal carbon price by firms, ESG
investment by financial organizations. Internalizing environmental
external costs may lead to larger growth than not internalizing them inlong-term perspective, although causing short-term slowing of
economic growth. 【Presuming worldwide emissions reduction】[II] Middle-to
long-term
green
growthworldwide

Achievement of innovation thatinduces GHG reduction

Achievement of innovation that promotes economically independent
emissions reduction (e.g. progress of sharing economy with IT or AI),as well as measures to support the innovation (e.g. policies forpromoting private investment such as corporate tax break, realizationof good economic circumstances, or elimination of less necessary
regulations) 【Effect of realizing innovation may expand worldwide
even without global emissions reduction effort】[III] Middle-to
long-term
green
growth in
specific
countries

Income growth of the countries by
increase in export of superior
technologies or products for
mitigating global warming tooverseas【Presuming worldwide emissions reduction. Green growth
opportunities for countries with higher capability ofenvironmentally-friendly technology development】

Income growth of the countries bydecrease in import through lower
fossil fuel price caused by CO2emissions reduction effort【Presuming worldwide emissions reduction. Green growth
opportunities for countries with higher dependence on imported
fossil fuel】

Economic growth opportunities by
replacing fossil fuel resource importwith capital-intensive technologies

Opportunities for improving total factor productivity by enhancing
resource productivity, albeit potentially decreasing capital productivity
or energy productivity 【Green growth opportunities for countries
with higher dependence on imported fossil fuel and higher
capability of environmentally-friendly technology development】

Image of CO2 emissions ofGreen Growth Scenarios
CO2
Emissions16

Frozen-technology scenario(Hypothetical scenario):
Assuming economic growth withno advancements from current
technologies
Usual energy saving (with increase in TFP
along with increase in energy productivity)
Baseline scenario (Medium)
Energy saving and fuel conversion by disruptive
innovation (with increase in TFP along withincrease in energy productivity)【Green growth
factor II】

Innovation baseline scenario: Businessdriven innovation (with change ineconomy and society by advancement of
IoT, AI etc.)
Short-term economic decline, but ultra longterm economic growth 【Green growth factor I】
Realization of long-term economic growthby internalizing environmental externalcosts (Green investing by the financial
sector, climate/energy policies, etc.)

Climate policy scenario (well below 2
degrees Celsius scenario etc.)
Further reduction can be achieved depending on the degree of globalwarming risks. However, economic growth, depending on the country,
will be expected through increase in investment for adaptation,
increase in exports of environmentally conscious technologies, anddecrease in the value of imports for importing countries due to the fallin fossil fuel prices. 【Green growth factor III】

Image of shift of emission reduction cost curves
Unit emission reduction costs($/tCO2)

17

Emission reduction cost
curves under current andgradual progress oftechnologies[Measures with over 100 $/tCO2]- For wide deployment, technology
developments for cost reductions are
required.
- Such high cost measures may be
prepared for risk management for highdamages of climate change.

Baseline emission reductions

0[Measures with below or around 50 $/tCO2]- Mitigation costs may impair short-term
economy but if we consider external costsof climate change damages, theirinternalization might be economically
acceptable.

Progress of IT, AI etc. andthe induced social change[Negative cost-measures]- The measures will be able to induce economic growth- Currently all of the negative-cost measures have been
almost realized if we consider all implicit costs.
- However, progresses of IoT, AI etc. will be able tochange several products and services, and provide
emission reduction potentials with net negative costs.

Emission reduction
potential (tCO2/yr)

Relationship between cumulative CO2 emissions andtemperature rise and uncertainties in climate sensitivity

18

Temperature response to emissions in 2010; the responses are normalized
by the amount of contribution of CO2 emission after 100 years past

Uncertainties in Climate sensitivity (IPCC)
Equilibrium climate sensitivity
Likely range (“best estimate”)

Source) Synthesis report of IPCC AR5

Before IPCC WG1 AR4

1.5−4.5°C (2.5°C)

IPCC WG1 AR4 (2007)

2.0−4.5°C (3.0°C)

IPCC WG1 AR5 (2013)

1.5−4.5°C (no consensus)

IPCC WG3 AR5(MAGICC) (2014)

2.0ｰ4.5°C（3.0°C）[Based on the AR4]

- [Long-term] Approximately linear relationship between cumulative CO2 emissions andtemperature rise can be observed. Nearly net zero CO2 emissions are necessary for thestabilization of global temperature at any level.
- [Near- & mid-term] Emission pathways for the long-term zero emissions have wide
ranges due to uncertainties in climate sensitivity.

Implications in relation to the Paris Agreement
19♦ The Paris Agreement (PA) specifies long-term targets such as keeping

temperature rises to well below 2℃ relative to pre-industrial levels, pursuing
1.5℃, and achieving net zero GHG emissions in the latter half of the 21st
century.
♦ Global Stocktake every 5 years (the same process continues.)♦ At the COP24 held in December 2018, a detailed rule book of PA was agreed.

Differentiated reviewing between developed and developing countries was
not framed, whereas pushing for raising ambition of the NDCs was notframed either.
♦ There are few measures of filling the gap between the long-term targets andactual emissions. A strong emission abatement measure by a single country
or a few countries without wider international cooperation would not work
well, because of industrial transferring to overseas and carbon leakage.
♦ Green growth (generating multiple synergies with the SDGs) is essential inorder to reach the PA’s long-term targets while properly managing total risks.
For this purpose, technological progress and social transformation on theenergy demand side, induced by innovations such as IT and AI, can offer
significantly important opportunities.

3. Potential drastic changes ofenergy demands induced byprogresses of AI, IoT etc.

Disruptive Innovations of End-use Technologies
21

Source: C. Wilson (IIASA)

Disruptive innovations of end-use
technologies such as IoT, AI, will be able toinduce:1) Shift from atomized to connected
2) Shift from ownership to user-ship3) Sharing economy & circular economy

Human society will be able tocontinue economic growth andresolve many social issues
through building highly
integrated systems of
Cyberspace (virtual) andPhysical space (real)
Source: Government of Japan (Cabinet Office)Innovations in end-use technologies through IT and AI,
and the induced social changes
449 Watts
5 Watts
2.5 Watts

Power72 Watts
Stand-byenergy use

22• Energy consumption is not our
purpose, but is just a phenomenon
accompanying with consumption of
goods and services, which is
conducted for our welfare increase.
Energy embodied in goods andservices must be taken into account.
• The end-use products and services
will usually diffuse rapidly, and theembodied energy and CO2 may
decrease rapidly.

75 kWh
0.1 kgSource：IIASA

1706 kWh
Embodied energy
26 kg
WeightOperation ratio of
automobiles is about 5%.
The large room for the
improvement exists by
the achievement of fullyautonomous cars.

Source: http://gendai.ismedia.jp/articles/-/50859

There are large opportunities to achievesocial changes and to increase energy
efficiency through fully autonomous
cars, food systems, etc. which can be
induced by innovations of IoT, AI etc.

Demand projection using AI and big data

Optimization
Production

Disposal loss reduction
Inventory

Delivery

Order

Discount

Reduction of food/disposal loss by optimization of supply & demand via AI

Source: Japan Weather Association https://www.jwa.or.jp/news/2018/02/post-000984.html

Taxi anddemographics

Data Platform

Mobility Service Platform

23







Food
Taxi’s dispatch
Clothing
Books
Office sharing, etc.
There are many opportunities of
efficiency improvement

Distribution
Mobile/tablet

Source： http://www.atmarkit.co.jp/ait/articles/1803/12/news043.html

 The key point is that large CO2

emission reductions are not achieved
if consumer welfare is thereby largely
decreased, but are achieved
autonomously on economic principle
by innovations of AI etc.
 They do not directly aim at energy
saving or CO2 emission reduction, but
contribute to the large reductions
indirectly throughout lifecycle.
⇒ Big opportunities for green growth4. Scenario analyses for the Longterm target of the Paris Agreement:
Impacts of car- & ride-sharingEnergy Assessment Model: DNE21+

25♦ Linear programming model (minimizing world energy system cost)♦ Evaluation time period: 2000-2100
Representative time points: 2000, 2005, 2010, 2015, 2020, 2025, 2030, 2040, 2050,
2070, 2100

♦ World divided into 54 regions
Large area countries are further divided into 3-8 regions, and the world is divided
into 77 regions.

♦ Bottom-up modeling for technologies both in energy supply and demandsides (about 300 specific technologies are modeled.)♦ Primary energy: coal, oil, natural gas, hydro&geothermal, wind,photovoltaics, biomass and nuclear power♦ Electricity demand and supply are formulated for 4 time periods:
instantaneous peak, peak, intermediate and off-peak periods♦ Interregional trade: coal, crude oil, natural gas, syn. oil, ethanol,hydrogen, electricity and CO2♦ Existing facility vintages are explicitly modeled.
- The model has regional and technological information detailed enough to analyze sectoral
measures. Consistent analyses among regions and sectors are obtained.

Image of scenario analyses on the impacts of fully autonomous cars andthe induced car- and ride-share based on the innovations of IT, AI etc.
Progress of IT, AI, big data etc.
Responses to social aspects(Legal issues etc.)

26

Inter-mode
competition withpublic transportationachievement of fullyautonomous cars

(future work)

Inducing ride-share

Inducing car-share
Rebound effects(future work)

Increase in operation ratio of cars

Reduction in owned cars

Relatively more economicaleven for high price of vehicle

Reduction in consumptionsof steel, plastic etc.
Decrease in energy
Consumptions forsteel productions

Production increaseof shale gas

Decrease in energy consumptions
for automobiles per person-km

EVs etc. will become
relatively economical.

Reductionin Naphtha

Autonomous CO2 emission
reductions in transport, iron &steel, and chemical sectors.

Shift from heavy oils to others (e.g.,gas) in industrial sectors will become
relatively economical.

Reductions in gasoline anddiesel demands
Response to
IMO’s regulations(future work)

Co-production
balances in oil
refinery will change.

Decrease in marginal abatement
cost (carbon price) for long-term
deep emission reduction
targets, e.g., below 2 °C

Impacts on power
sectorImpacts on othersectorsGlobal emission pathways and CO2 marginal
abatement costs for 2 °C goal
90

SSP2

80
CO2 emission [GtCO2/yr]

27

70

SSP1

60
50

SSP1 & car- and ride-share

40
30

2 °C consistentscenario (>50%)

20
10
0
2010

CO2 marginal abatement
costs for 2 °C pathways
Unit: $/tCO2 (real price)

2030

2050

2070

2090

In case of uniform carbon price(the least cost case)

Baseline：SSP2
Baseline：SSP1
Baseline：SSP1 & car- and ride-share

Consistentscenario with
IPCC SR15

2050

2100

P3

154

269

P2

165

187

P1

126

185

2℃ pathways (>50%)：40% reduction in 2050 relative to 2010 SSP2

(Middlescenario)
Source) estimated by using a global energy
SSP1
and climate change mitigation model,
DNE21+, developed by RITE

The marginal abatement costs for the scenario
of SSP1 & car- and ride-share are much lower
than those for the SSP2 scenario.

SSP1 &
car- andride-share

Global automobiles owned: impacts of sharing
induced by fully autonomous cars
2050

2500

FCV

2000

EV

1500

PHEV

1000

HEV

500
0

ICEV
SSP2

SSP1
Baseline

SSP1 & carand ride-share

SSP2

SSP1

SSP1 & carand ride-share

SSP2 ⇒ SSP1
Cost reductions in environmental
friendly cars, e.g., hybrid vehicle, PHEV,
EV, FCV for SSP1 induce that these cars
are more economically.
SSP1 ⇒ SSP1 & car- and ride-share
The sharing brings higher operation
ratios and induces economic travels
even with high price cars, and EVs
diffuse relatively more widely even in
Baseline scenario.

2℃ pathways (>50%)
3000

Passenger car in use [million vehicle]

Passenger car in use [million vehicle]

3000

28

Shared autonomous car: FCV

2500

Shared autonomous car: EV

Effects of car& ride-sharing

2000

Shared autonomous car: PHEV
Shared autonomous car: HEV

1500

Shared autonomous car: ICEV
FCV

1000

EV
PHEV

500
0

HEV
ICEV
2010

2015

2020

2025

2030

2035

2040

2045

2050

Global energy consumption in transport sector

29

3000

Electricity

2050

Gaseous fuel：Hydrogen

Final energy consumption [Mtoe/yr]

2500

Gaseous fuel：Gas
2000

Liquid fuel：Bio fuelLiquid fuel：Fuel oil

1500

Liquid fuel：Diesel oil
1000

Liquid fuel：Jet fuelLiquid fuel：Gasoline

500

Solid fuel：Biomass
0
2010

SSP2

SSP1

Baseline

SSP1 & carand rideshare

SSP2

SSP1

SSP1 & carand rideshare

Solid fuel：Coal

2℃ pathways (>50%)- Future price reductions of cars induce energy consumption decrease in transportation
sector (including all mobilities in this figure) in baseline scenarios. (SSP2⇒SSP1)
・Much larger impacts of car- & ride-sharing induced by fully autonomous cars can beobserved in transportation sectors. (SSP2, SSP1⇒SSP1 & car- and ride-sharing)Global crude steel production by technology

30

2500

2000

Type VI: EAF, Middle efficiency
Type V: EAF, Low efficiency

1500

Type X: DRI-based EAF (Hydrogen)
1000

Type VIII + IX: DRI-based EAF (Gas)
500

Type IV+CCS: BF-BOF, High efficiency+ Next-generation coke ovenBaseline

2℃ pathways(>50%)
2030

Baseline

2℃ pathways(>50%)
2050

Baseline

SSP1 & car- and ride-share

SSP1

SSP2

SSP1 & car- and ride-share

SSP1

SSP2

SSP1 & car- and ride-share

SSP1

SSP2

SSP1 & car- and ride-share

SSP1

SSP2

SSP1 & car- and ride-share

SSP1

SSP2

SSP1 & car- and ride-share

SSP1

SSP2

0
2010

Crude steel production（Mt-CS/yr）

Type VII: EAF, High efficiency

2℃ pathways(>50%)

Type III+CCS: BF-BOF, High efficiency
Type IV: BF-BOF, High efficiency +Next-generation coke ovenType III: BF-BOF, High efficiency
Type II: BF-BOF, Middle efficiency
Type I: BF-BOF, Low efficiency

2100- Global crude steel production is expected to be increased from 2010 to 2050 in all
scenarios. On the other hand, the crude steel production of SSP1 and SSP1 & car- andride-share in 2100 will be considerably smaller than that of SSP2.
- Automotive steel sheet demand is estimated to be decreased due to car- and ride- share.
However, that amount is not so much large compared to the total crude steel production.

Global ethylene and propylene production by technology
31450

Ethane cracker, high efficiency

350

Ethane cracker, middle efficiency

300
250

Ethane cracker, low efficiency

200

Naphtha cracker, high efficiency
150

Naphtha cracker, middle efficiency

100
50

Naphtha cracker, low efficiency

Baseline

2℃ pathways(>50%)

Baseline

2℃ pathways(>50%)

Baseline

SSP1 & car- and ride-share

SSP1

SSP2

SSP1 & car- and ride-share

SSP1

SSP2

SSP1 & car- and ride-share

SSP1

SSP2

SSP1 & car- and ride-share

SSP1

SSP2

SSP1 & car- and ride-share

SSP1

SSP2

SSP1 & car- and ride-share

SSP1

SSP2

0
2010

Ethylene and propylene production（Mt/yr）

400

2℃ pathways(>50%)- In all scenarios, ethane crackers are evaluated to dominate over naphtha crackers for thelong run.
- In the SSP1 & car- and ride-share, ethylene and propylene production slightly drops thanthe SSP1 due to car- and ride-share.

Global electricity generation mix
Hydrogen (Co-firing)

100000Hydrogen(Single-fuel firing)
Solar thermal

90000

Electricity generation [TWh/yr]

32

80000
Solar PV
70000
Wind power
60000

Nuclear power

50000

Hydro + Geothermal

40000

Biomass (Co-firing)
w/ CCS
Biomass (Single-fuel firing) w/
CCS
Biomass (Co-firing)
w/o CCS
Biomass (Single-fuel firing) w/o
CCS
Gas w/ CCS

30000
20000
10000

2050

Baseline

SSP1

SSP2

SSP1 &
car- and ride-share

SSP1

SSP2

SSP1 &
car- and ride-share

SSP1

SSP2

2℃ pathways (>50%)

SSP1 &
car- and ride-share

Baseline

SSP1 &
car- and ride-share

SSP1

SSP2

2010

0

Oil w/o CCS

2℃ pathways (>50%)

Coal w/ CCS

2100

Gas CGSGas w/o CCS
Oil w/ CCS

Coal w/o CCS- Large decline in CO2 emission in transportation sector occur in SSP1 & car- and ride-share with lower
marginal abatement cost, so this induce lower emission reduction levels required for other sectors toachieve -40% compared with 2010, as well as a small amount of introduction of coal power plants
without CCS which cannot be allowed under stringent emission reductions.

5. Conclusions and challenges
for the futureOutline of IPCC WG3 AR6 (Co-vice chair: Diana Urge-Vorsatz)
34“New” chapterof AR6

Estimation of Passenger Travel Demand in Japan fromTime Use Point of View: Consideration of Rebound Effects

35•

Existing Integrated Assessment Models use activity (service, goods) scenarios which
are mainly based on historical trend.

•

On the other hand, a bottom-up way of demand scenario construction, starting frompeople’s daily life behavior from time budget and time use point of view has apotential to capture service demand change including rebound effects induced bysocioeconomic and technological changes transparently, comprehensively andconsistently.

•

We are considering self-consistent
low energy demand scenarios byidentifying and quantifying service
demand from time use and timebudget approach.

 Contrasting passenger travel
demand scenarios in Japan can beconsidered, i.e., Low Demand (travel
distance by motorized private
decreases by 47%) and High
Accessibility (travel distance by
motorized private increases by 30%).

1200

Motorized public
Non-motorized transportation

Motorized private
Average travel time per capita

269

1000

120

100

432

800

60
849

651
454

494

40

344

200

0

80

372

61

600

400

68

363

67

79

77

411

20
50

39

44

117

Population Socioeconomic Low Demand
Change Only Change Only

29

0

High
Accessibility

2015-162050 Autonomous Driving(Source) Gi et al. (2019)

Vehicles Scenarios

Average travel time per capita [min/day]

Everything (e.g., society, economy, technology) will change drastically until 2100.

Travel demand within daily lifea area [billion p-km/yr]•

Conclusions
36♦ The total risk management is important understanding several kinds of related

uncertainties. We should consider the role of each climate change response
measure understanding its costs and potentials. Energy supply sidetechnologies with low- and zero-emissions are surely important.
♦ On the other hand, innovations will be achieved through “new connections” ofa number of technologies etc. Technologies not directly related to global
warming mitigation will form “new connections”, induce social change, andthen help achieve deep CO2 emission reductions. Big progresses in AI, IoT, bigdata etc. will reduce energy demands without decrease in welfare. (It is
important not to directly aim at too much energy saving and global warming
mitigation but to endeavor for the innovations of products and services.)♦ This study conveyed a preliminary analysis of impacts of car- & ride-sharing
induced by fully autonomous cars on energy supply and demands and CO2

emission reductions also considering the induced effects on other sectors.
♦ Progresses of AI etc. may bring low energy demands across many sectors but

most of current integrated assessment models (IAMs) have not treated them
explicitly and quantitatively. Appropriate treatment of innovations in end-use
sectors, and consistent analyses including rebound effects are very importantscientific agenda for modelers. RITE would like to develop a better IAM
treating these innovations and to evaluate the scenarios in collaboration withIIASA and other institutes and researchers.

Appendix

Energy consumption and GHG emissions
throughout food lifecycle

38

Energy consumption; an example of the estimations for U.S.

♦ 10.8 EJ/yr in 1990s: it accounts for approx. 12% of the total energy consumption
Source：https://www.oregon.gov/deq/FilterDocs/PEF-FoodTransportation-FullReport.pdf

GHG emissions; an example of the estimations for the world
Energy consumption and GHG
emissions in food lifecycle issubstantial. If we can reduce foodloss, i.e. unfruitful foodproduction, a great ripple effect is
expected, since it can contributeto reduce not only direct energy
consumption in food processing,transportation and retails but also
indirect energy consumption
embodied on related products
and activities.
Source：GRAIN (2011) Food and climate change: the forgotten link

Major assumptions of car- and ride-sharing[Major assumptions] (mainly following Fulton et al. (2017))♦ Fully autonomous car can be realized in 2030
♦ Additional costs for fully autonomous cars:+10,000$ in 2030, +5,000$ in 2050, +2,800$ in 2100
♦ Operation ratio of cars: depending on travel service demands of cars per area
♦ Life times of cars: 13-20 years for conventional cars, 6-20 years for share cars
♦ Number of riding per car:1.1-1.5 people in 2050 and 1.1-1.3 people in 2100 for conventional cars
1.75 people in 2050 and 2 people in 2100 for shared cars

Annual travel distanceper car [km/yr]

A relationship between ‘demand of passenger transport service by cars per country area’and ‘annual travel distance per car’
40000
35000
30000
25000
20000
15000
10000
5000
0

0

500

1000

1500

2000

2500

3000

Demand of passenger transport service by cars per country area [thousand p km/ km2]

39

Major assumptions of car- and ride-sharing andthe estimated impacts

40[Major assumptions] (mainly following Fulton et al. (2017))♦ Fully autonomous car can be realized in 2030
♦ Additional costs for fully autonomous cars:+10,000$ in 2030, +5,000$ in 2050, +2,800$ in 2100
♦ Operation ratio of cars: depending on travel service demands of cars per area
♦ Life times of cars: 13-20 years for conventional cars, 6-20 years for share cars
♦ Number of riding per car:1.1-1.5 people in 2050 and 1.1-1.3 people in 2100 for conventional cars
1.75 people in 2050 and 2 people in 2100 for shared cars

[Estimated impacts]♦♦

Number of shared car owned in 2050: 60% compared to that of conventional car owned
Number of shared car sales in 2050: 70% compared to that of conventional car sales[Impacts on iron and steel productions]♦♦

Ton of steel for shred cars: 78% compared to that for conventional cars
Total iron and steal productions in the SSP1 and car- & ride-sharing scenario: 98% of
those in the SSP1 without consideration in car- & ride-sharing[Impacts on productions of ethylene and propylene]♦♦♦

Share of productions of ethylene and propylene in productions of plastics:85%
The share for cars in the productions of ethylene and propylene: 8%
Total productions of ethylene and propylene: 99% (accordingly reductions in naphtha
and ethane)Average Transport Costs per Passenger-km by mode
1.4

41

Waste
Water Pollution

Transport costs (US$2007 per passenger-km)

1.2

Land Use Impacts
Resource Externalities
GHG

1

Air Pollution
Noise

0.8

Barrier EffectTransport Diversity
Land Value

0.6

External Health Ben.
Internal Health Ben.

0.4

Congestion
Traffic Services
Road Facilities

0.2

External Parking
Internal Parking

0

External Crash
Internal Crash
Travel Time

-0.2

Operating Subsidy-0.4

Vehicle Operation
Average Car Compact Electric Car
Car

Van orPickupRideshare Diesel Bus
Passenger

Electric Motor-cycle
Trolley

BicycleWalk

Vehicle Ownership
Total costs（Data Source）Litman, Transportation Cost and Benefit Analysis (2009, 2015, 2017, 2018)

・Costs other than vehicle costs account for large fraction.
・We have included travel time and safety costs to DNE21+ at first to consider opportunity
benefits/costs of autonomous driving vehicles and car-sharing.

Assumptions on Opportunity Benefits/Costs forAutonomous Driving Vehicles and Car-Sharing*
Travel Time

SafetySafety Improvement
benefit ($/year) = 0.1($/v-km)×(1 – Rate ofpenetration ofautonomous driving
vehicles)×Travel
distance (v-km/year)

Wage ($/hour) = Per capita GDP ($/year)÷2000
Autonomous (hour/year; Annual working time)
Travel time (hour/year) = Travel distance (p-km/year)
Driving
Vehicles÷30 (v-km/hour; Travel speed)
Only
Driving Free benefit ($/year)＝Wage ($/hour)×0.15(Benefit rate)×Travel time
Car-Sharing
Only

42

Car-Sharing cost ($/year)＝Wage×0.35 (Costrate)×Travel time×0.1 (Increase rate of total traveltime) ×(1 – Car-Sharing utility)

Autonomous
Driving Free benefit ($/year)
Driving
Vehicles and
Car-Sharing cost ($/year)
Car-Sharing

Safety Improvement
benefit ($/year)*Relative benefits/costs compared with conventional auto-mobiles

1
0.8

Assume two type of benefits/costs

0.6

・Travel Time Benefit/Cost: Driving Free benefit and
Car-Sharing cost
Assumptions on car-sharing

0.4

・Safety Improvement Benefit

シェアカー利便性
Car-sharing
utility
Penetrationof autonomous driving vehicles
完全自動運転普及率

0.2

utility and rate of penetration 0of autonomous driving vehicles 2000

2020

2040

2060

2080

2100

Relative benefits of autonomous driving
vehicles and car-sharing [$/p-km]
完全自動運転・シェアリング便益[$/p-km]

An example of estimation of opportunity benefits/costs byautonomous driving vehicles and car-sharing: the United States 43
0.3
0.25
0.2

安全性向上便益
Safety Improvement benefit

0.15

シェアカー利用コスト
Car-Sharing cost

0.1

運転フリー便益
Driving Free benefit
合計
Total

0.05
0
-0.05

2010

2030

2050

2070

2100

・Driving Free Benefit increases gradually due to increase of wage
・Car-Sharing cost saturates because the increase of car-sharing caused by increase of wage
is cancelled by increase of the utility of car-sharing.
・Safety Improvement benefit is initially high but decreases rapidly due to penetration ofautonomous driving vehicles.

The estimated impacts of car- and ride-sharing on
number of owned car and the other sectors

44[Estimated impacts]♦ Number of shared car owned in 2050: 60% compared to that of conventional

car owned♦ Number of shared car sales in 2050: 70% compared to that of conventional
car sales[Impacts on iron and steel productions]♦ Ton of steel for shred cars: 78% compared to that for conventional cars
♦ Total iron and steal productions in the SSP1 and car- & ride-sharing

scenario: 98% of those in the SSP1 without consideration in car- & ridesharing[Impacts on productions of ethylene and propylene]♦ Share of productions of ethylene and propylene in productions ofplastics:85%♦ The share for cars in the productions of ethylene and propylene: 8%♦ Total productions of ethylene and propylene: 99% (accordingly reductions in
naphtha and ethane)Global oil refinery by product
4500

45

2050

4000

Petroleum coke

Production [Mtoe/yr]

3500

Fuel oil
3000

Diesel oil

2500
2000

Kerosene/Jet fuel

1500

Gasoline

1000

Naphtha

500
0
2010

SSP2

SSP1

Baseline

SSP1 & carand rideshare

SSP2

SSP1

SSP1 & carand rideshare

LPG

2℃ pathways (>50%)- Gasoline and diesel oil for automobiles are reduced in SSP1, and SSP1 & car- and rideshare compared to SSP2. (SSP2⇒SSP1, SSP1 & car- and ride-share)

Global final energy consumption by sector

46

7000

Heat：CGS
5000

Gaseous fuel：Hydrogen
4000

Gaseous fuel：Gas
3000

Liquid fuel：Bio fuel2000

Liquid fuel：Oil
1000

Solid fuel：Biomass
0
2010

SSP2

SSP1

SSP1 & carand rideshare

Baseline

SSP2

SSP1

SSP1 & carand rideshare

Solid fuel：Coal

Residential andcommercial sector

2℃ pathways (>50%)
2050- Gas and electricity demand in theindustrial sector and residential andcommercial sector is expected to be
increased in 2010 through 2050 inall scenarios.
- As shown in the global ethylene
and propylene production, fuelswitching from oil to gas is
occurred for achieving CO2
emission reduction. But as a whole,gas demand in the 2℃ pathways issmaller than that in the baseline.

6000

2050

Electricity

5000
Final energy consumption [Mtoe/yr]

Final energy consumption [Mtoe/yr]

Industrial sector

Electricity

2050

6000

Heat：CGS
4000

Gaseous fuel：Hydrogen
3000

Gaseous fuel：Gas
2000

Liquid fuel：Bio fuel1000

Liquid fuel：Oil

0
2010

SSP2

SSP1

SSP1 & carand rideshare

Baseline

SSP2

SSP1

SSP1 & carand rideshare

2℃ pathways (>50%)
2050

Solid fuel：Biomass

Solid fuel：CoalFramework for assessment of passenger travel demand
47

・・・

Time
for
TravelTime fortravelwithindaily lifearea

Time forcommuteTime forbusinessTime
for
LeisureTime forprivate
trip

Time
for
Unpaid
work
Time
for
Paid
work
Time
for
SelfcareTime fortraveloutsidedaily lifearea

Time formotorized
publicDistance by
motorized
publicTime formotorized
privateDistance by
motorized
privateTime for nonmotorizedtransportation

Distance by
non-motorizedtransportation

Time formotorized
publicDistance by
motorized
publicTime formotorized
privateDistance by
motorized
privateTime for nonmotorizedtransportation

Distance by
non-motorizedtransportation

Time formotorized
publicDistance by
motorized
publicTime formotorized
privateDistance by
motorized
privateTime for nonmotorizedtransportation

Distance by
non-motorizedtransportation

DistanceforcommuteTotal distanceby motorized
publicTotal
distance
for travel
withindaily lifearea

Distanceforbusiness
Total distanceby motorized
privateDistancefor private
trip
Total distanceby nonmotorizedtransportation

・Travel distance bypurpose and mode is
directly connected to time
for travel by purpose andmode for each
representative actor.
Total
distance
for travel
outsidedaily lifearea

Representatives and Procedure for
Bottom-up Estimation of Passenger Travel Demand
Sex

Age
15-29

Male

30-49
50-64
65+
15-29

Female

30-49
50-64
65+

Employment

Representative

Occupation

Representative

Working
Non-working
Working
Non-working
Working
Non-working
Working
Non-working
Working
Non-working
Working
Non-working
Working
Non-working
Working
Non-working

M1
M2
M3
M4
M5
M6
M7
M8
F1
F2
F3
F4
F5
F6
F7
F8

Administrative, managerial
Professional, technical, clerical,
sales, service, security
Agricultural, forestry, fishery
Manufacturing process

A
B
C
D

×
RegionRepresentative

Three metropolitan areas
Major cities
CitiesVillages

M
U
C
V

48

・Set 160
representatives tosimulate personal
heterogeneity oftravel demand

Total travel demand Japan (p-km/yr) = Total travel demand within daily life area Japan (p-km/yr)+ Total travel demand outside daily life area Japan (p-km/yr)
Travel demand within daily life area R, P, M (km/yr)= Trip rate R, P, M (trip/yr) ×Trip distance R, P, M (km/trip)= Trip rate R, P, M (trip/yr) ×Trip time R, P, M (h/trip) ×Travel speed R,M (km/h)= Travel time R, P, M (h/yr) ×Travel speed R,M (km/h)
R: personal attributes (region, sex, age, employment, occupation), P: trip purpose (commuting, work,
private trip), M: travel mode (non-motorized transportation, motorized private, motorized public)

Total passenger travel demand in 2015-16 in Japan
49

Total travel demand in Japan [billion p-km/yr]* Non-motorized transportation: walk, bicycle, etc., Motorized private: private cars, motorcycles, etc.,
Motorized public: buses, railways, etc.

1600

International

1400

Domestic public

1200

Domestic private

1000

RegionV

800

RegionC

600

Region
U

400
200
0

65+
Female50-64

Non-working
Private trip

Occupation C

Male

Occupation B

Motorized private
CommutingOccupation A

Sex

Age

Employment

Travelwithindaily
life area

Work

15-29

RegionMotorized public

Occupation D

30-49
RegionM

Traveloutsidedaily
life area

PurposeNon-motorizedtransportation

Mode・Energy consumption for passenger travel can be calculated by using energy systems models
based on passenger travel demand by mode.

Passenger travel demand scenarios in Japan
50

Future(2050)

Current state(2015-16)
SocioeconomicchangeMega-trend I(Population
aging)
Mega-trend II(Gender equality)
Mega-trend III(Tertiary
industrialization)

Change oftransportation

Mega-trend IV(Autonomousvehicles)Mega-trend IV-A
(Low demand)
Mega-trend IV-B
(High accessibility)

・We consider mega-trends in terms of socioeconomic change (Mega-trend I-III)and transportation technology innovation (Mega-trend IV).

A narrative of four mega-trends in Japanfor future mobility demand scenarios
Mega-trend
I Population Aging
II Gender Equality
III Economic Growth(Tertiary Industrialization)
CommonIV
Transport
Technology
Innovation

A
Low
Demand51

StorylineaChange of population structure by region, sex and age.

b Increase of employment rate for people those aged 65 and over.
a Increase of employment rate and administrative positions for women.
b Convergence of gender difference of trip purpose by mode.
Shift from blue-collar workers to white-collar workers.

a Increase of preference for motorized private due to distribution of autonomous driving vehicles.

Increase of travel speed by motorized private thanks to efficiency improvement of traffic flow byb distribution of autonomous driving vehicles.
a Decrease of effective travel speed by motorized private because of waiting time for sharing vehicles.
b Decrease of private trip demand due to increase of utility of online shopping and leisure at home.
cDecrease of commuting trip demand by employed people due to increase of teleworking for white-collar
workers.
Decrease of commuting trip demand by students due to e-Education.

de Increase of trip demand by non-motorized transportation in cities for health improvement.
f
a

B
b
High
Accessibility cdIncrease of trip demand by motorized public and decrease of trip distance in cities thanks to compact
urban design.
Increase of private vehicles ownership because of increase of preference for on-demand trips and shift
from trips by motorized public to those by motorized private.
Increase of travel speed thanks to intelligent transport systems. On the other hand, travel speed by
motorized private decreases in cities center because of traffic congestion caused by increase of private
vehicles.
Expansion of the urban area thanks to the improvement of utility of motorized private and motorized
public on the move.
Shift of access/egress traffic from non-motorized transportation to motorized private.