Opening remarks and the background of the Energy
Demand changes Induced by Technological and Social
innovations (EDITS) network
Keigo Akimoto, Research Institute of Innovative Technology for the Earth (RITE)
The side event at the Japanese Pavilion of COP27
Please consider the environment before printing this slide deck
Icon from all-free-download.com, Environmental icons 310835, by BSGstudio, license CC-BY

This presentation is licensed undera Creative Commons Attribution 4.0 International LicenseG20 Karuizawa Innovation Action Plan on
Energy Transitions and Global Environmentfor Sustainable Growth (June 2019)“We recognize the importance of quantitative analysis on better understanding futureenergy demand and supply and the role of innovation of both sides driven bydigitalization, Artificial Intelligence (AI), the Internet of Things (IoT), and the sharing
economy. We encourage efforts made by the global scientific community andinternational organizations and frameworks to further refine and develop the fullspectrum of economy-wide scenarios for energy and climate models.”Note) This is also an annex document of the G20 Osaka Leader’s declaration.
2EDITS: Energy Demand changes Inducedby Technological and Social innovations

The EDITS project supported by Ministry of Economy, Trade, and Industry(METI), JapanThe terms: FY2020- (expectation: for five years and more)
Participating research institutes or researchers:
IIASA, AIT, LBNL, OECD/ITF, CMCC, Central European Univ., Univ. of Wisconsin,UCSB, UFRJ/COPPETEC, The Korean Society of Climate Change Research, The
Univ. of Tokyo, Osaka Univ., RITE, and others
Nearly 100 researchers including many IPCC lead authors have been involved.

[Objectives]
 To create a research community with a focus on end-use, demand-side perspectives that furthers dialogue
and cross-fertilization of research and policy analysis through the sharing of novel data, novel concepts,methodologies and policy analyses.
 To improve the state-of-art of demand modeling in environmental and climate policy analysis, via methods
and model intercomparisons and assisting the transfer of conceptual and methodological improvements
across disciplines, sectors, and environmental domains.
 To better inform policy via structured model experiments and simulations that assess potential impacts,
barriers, as well as synergies and tradeoffs to other SDG objectives of demand-side policy interventions,particularly in novel fields and service provision models such as digitalization, sharing economy, or the3 integration of SDG and climate objectives in synergistic policy designs.

Response measures to climate change
Human Needs

Real human needs?
(Innovations willalso change them.)

Human Activities
Energy・Material・Information／Human Activities

Technology and social innovations including digitalization
have opportunities inducing to change this.

EDITS focus

Change of Society andBehavior
Energy Efficiency ↑Material Efficiency ↑ICT replace Ene./Mat.

CO2 Emissions／Energy・Material・InformationLow Carbon Energy
Low Carbon Material

Mitigation (Emissions reduction)＋low carbon information

CO2 Concentration／CO2 Emissions
Emission of Non CO2 GHG

Temp. Rise／GHG Concentration

Historical analyses and measures
mainly focus on:

CCS, CCU
Afforestation、DAC
Climate Control（SRM etc.）

Climate Engineering

Impact of Climate Change／Temp. Rise
Adaptation to Climate Impact

Adaptation

Residual Damage by Climate Change
4Source：Kenji Yamaji, 2006：「Theory of 3E Systems Analysis」, Iwanami-Shoten (in Japanese) + modifications

Low Energy Demand (LED) scenarios
IPCC Special Report on 1.5 C (SR15)

IPCC 6th Assessment
Report (AR6)LDLED
The IPCC reports show some scenarios of low energy
demands, but more comprehensive and quantitative
scenarios will be needed.
5

SDGs and a low energy demand society
Achieving Goal 12 is well coordinated with achieving other eleven Goals
Responsible Consumption & Production:
End poverty, reduce overconsumption, minimize waste and environmental impacts

LEDSource: IIASA, LED scenario

6

Deep emission reductions at affordable costs will be a key to achieving
multiple SDGs, and digitalization, and the related other innovations willcontribute to the achievement.

Digitalization impacts (IPCC AR5 Ch.5)

7IPCC AR5 Ch.5 – Knowledge Gaps
Authors: Felix Creutzig, Joyashree Roy, Arnulf Grubler, Eric Masanet, and others

1. Better metric to measure actual human well-being
2. Evaluation of climate implication of the digital economy
3. Scenario modelling of services
4. Dynamic interaction between individual, social, and structural driversof change
These gaps will also be tackled in the EDITS project.

8‘High-with-Low’ Narrative Scenario
high wellbeing with low resource use

Source: EDITS WG3 Narratives group (Arnulf Grubler, Greg Nemet,Shonali Pachauri, Charlie Wilson), The 'High-with-Low' Scenario
Narrative: Key Themes, Cross-Cutting Linkages, and Implications forModelling

9How can we approach this scenario
from a modelling side?

Overview of DNE21+ model♦ Linear programming model (minimizing world energy system cost; with 10mil. variables and 10mil.

constrained conditions)♦ Evaluation period: 2000-2100
Representative time points: 2005, 2010, 2015, 2020, 2025, 2030, 2040, 2050, 2070 and 2100
♦ World divided into 54 regions
Large area countries, e.g., US and China, are further disaggregated, totaling 77 world regions.
♦ Interregional trade: coal, crude oil/oil products, natural gas/syn. methane, electricity, ethanol,hydrogen, CO2 (provided that external transfer of CO2 is not assumed in the baseline)♦ Bottom-up modeling for technologies on the energy supply side (e.g., power sector) and CCUS
♦ For the energy demand side, bottom-up modeling conducted for the industry sector including steel,cement, paper, chemicals and aluminum, the transport sector, and a part of the residential &commercial sector, considering CGS for other industry and residential & commercial sectors.
♦ Bottom-up modeling for international marine bunker and aviation.
♦ Around 500 specific technologies are modeled, with a lifetime of equipment considered.
♦ Top-down modeling for others (energy saving effect is estimated using long-term price elasticity.)
10

Scenario assumptions

Digitalization and innovations, and induced social changes − Demand reductions (1/2)
Changes due todigitalization

Direct impacts

Indirect impacts

1) Ride and carsharing associatedwith fullyautonomous cars

- Energy consumption reductions
due to ride-sharing- Reductions in consumption of basic
materials, e.g., iron and steel, plastics, tire,glass, and concrete, due to reductions in number of cars associated with car-sharing - Reductions in freight shipping => 8)2) Virtual meetingand teleworking- Reductions in person-km travel by- Reductions in travel service- Potential reductions in numbers ofpassenger cars, buses, and aircraft bydemand and the associatedcommercial building, and the resulting
10%
reductions in energy
reductions in iron and steel, concrete, andconsumptions in transport sector
others [Not yet considered]3) E-publication etc.

- Reductions in paper
consumptions due to large
deployment e-publications etc.

4) Recycling and- Reductions in energy
reductions in
consumptions for apparelapparels due to eproductionscommerce and otherdigitalization
11

Model assumptions (tentative)
Iron and steel production: -4%
compared with standard scenarios
Plastic production: -1%
Tire production (for cars): -28%
Glass production (for cars): -28%
- Cement production: -1% (only for multistorey car park)- Potential reductions in freight services for - Reductions in paper/pulp by 20%
papers. [Not yet considered]- Reduction in new productions of- Potential reductions in energy
consumption at shopping centers etc. [Not apparels by 20%. No explicit modelingfor apparels in DNE21+, andyet considered]corresponding reductions in energy
consumption in textile and leather
sector by 20%

Red: residential sector, Green: commercial sector, Blue: transport sector, Purple: industry sector , Brown: Non-CO2 GHGs etc.

Scenario assumptions

Digitalization and innovations, and induced social changes − Demand reductions (2/2)
Changes due to digitalization

Direct impacts5) Longer life time of buildings
due to improv. in city planning- Potential Redductions incement and steel due tolonger life time ofbuildings- Longer lifetime of building: +40%; therelated reductions in cement (-3%) andsteel (-3%) productions

6) Reductions in food losses
due to better demand
projection- Reductions in nitrogen - Reductions in freight shipping services => 8)
fertilizer, plastics, etc.
- Pot. red. in construction for supermarkets
and the resulting energy
etc., and the resulting reductions in steel,consumption reductions
concrete, and others [Not yet considered]- Potential reductions in - Pot. increases in afforestation due toenergy consumption atincrease in rooms of land area [Not yetsupermarkets etc.
considered]- Red. in CH4 and N2O- Reduction in petrochemical products
including ammonia by 1%- Reduction in plastics by 1%- Reduction in paper and pulp by 0.5%
- Reduction in transport services by 1%
and others(according to I/O analysis results)- Reduction in CH4 and N2O emissions:-493 MtCO2eq/yr in 2050

7) AM (3D-printing) for applying - Reduction in aluminum
aircraftand steel production- Reduction in electricity
for productions
8) Red. in freight shipping
services due to reductions inbasic materials and products
12- Energy consumption
reductions in freightshippingIndirect impacts

Model assumptions (tentative)- Energy efficiency improvements of aircraft - Red. in aluminum and steel prod. by
1% and 0.02%, respectively
and the energy consumption reductions- Energy efficiency improvements of cars and - Reduction in elec. consumption by 1%
the energy consumption reductions [Not yet - Increase in energy efficiency of aircraftby about 10%
considered]- Reduction in freight shipping demandby 1%

Red: residential sector, Green: commercial sector, Blue: transport sector, Purple: industry sector , Brown: Non-CO2 GHGs etc.

Scenario assumptions
Emissions
reduction

BL-StdBL-Mobil
BL-Resid
BL-Build

Baseline(nonspecificclimatepolicies)

Energy demand reductions due to mainly digitalization
Transport1)

Residential
2, 3, 4)

Building5)

Food6)――――

Industry Spill over7)
8)――

Rapid cost red.
in granulartech’s, e.g., PV,Wind, EV

Demandflexibilities inelectricity(EV, HP, CGS)

――X
X
X

BL-FoodX

BL-IndX

BL-All_CEX

X

X

X

X

XBL-All_CE+FL

X

X

X

X

X

X

X

X

――――――――X

X

B2DS-Std
B2DS-Mobil
B2DS-Resid
B2DS-Build
B2DS-Food

B2DS(well below
2C; NDCs
in 2030;CN by
2050 in G7countries)

X
X
X
X

B2DS-IndX

B2DS-All_CE

X

X

X

X

X

X

13
B2DS-All_CE+FL

X

X

X

X

X

X

Final energy consumption (preliminary)Baseline (without additional climate policies)
Final energy consumption [Mtoe/yr]

2050

2040

2030

-1400
BL-Mobil
BL-Resid
BL-Build
BL-Food
BL-IndBL-All_CE
BL-All_CE+FL
BL-Mobil
BL-Resid
BL-BuildBL-Food
BL-IndBL-All_CE
BL-All_CE+FL
BL-Mobil
BL-Resid
BL-Build
BL-Food
BL-IndBL-All_CE
BL-All_CE+FL

14-1200-1000-800-600-400-200

0200

Industry
Building (Residential & commercial)
Transport

While this preliminary
study assumes only
limited impacts ofcircular/sharing
economies due todigitalization mainly,significant reductions(by around 10%) in
final energy
consumptions areestimated.

Ref.) Global final energy consumption in 2019: 10 Gtoe/yr; baseline final energy consumption in 2050: 14 Gtoe/yr

GHG emissions (preliminary)Baseline (without additional climate policies)
GHG emissions [MtCO2eq/yr]-14000BL-Mobil
BL-Resid

2030

BL-Build
BL-Food
BL-IndBL-All_CE
BL-All_CE+FL
BL-Mobil
BL-Resid

2040

BL-Build
BL-Food
BL-IndBL-All_CE
BL-All_CE+FL
BL-Mobil
BL-Resid

2050

BL-Build
BL-Food
BL-IndBL-All_CE-12000-10000

-8000

-6000

-4000

-2000

The assumed
02000
digitalization impacts
in end-use sectors on
Iron & steel
entire GHG emissions
Cementare estimated toPulp & paper
reduce the emissions
Chemicalby around 7%, andOther industries
those plus theRoad transport
assumptions on rapid
Domestic aviation
cost reductions inOther domestic transport
Largergranular technologies
deployments
Intn'laviation
bunkersof EVand demand
Intn'l marine bunkers
Residential & commercial flexibilities could
reduce the emissions
Power generation
Other energy coversion by around 20%, even
if limited factors are
LULUCF CO2
considered.
Process CO2
Non-CO2 GHG

BL-All_CE+FL

15Ref) Global GHG emissions in 2019: 59 GtCO2eq./yr; Baseline GHG emissions in 2050: 73 GtCO2eq/yr

Final energy consumption (preliminary)
B2DS (well below 2 °C)

Final energy consumption [Mtoe/yr]-3000-2500-2000-1500-1000-500

0500

2030

B2DS-Std
B2DS-All_CE

Energy savings induced by carbon prices

BL-All_CE+FL

Energy savings induced by carbon prices

2040

B2DS-Std
B2DS-All_CE

Energy savings induced by carbon prices

BL-All_CE+FL

Energy savings induced by carbon prices

2050

B2DS-Std
B2DS-All_CE

Energy savings induced by carbon prices

BL-All_CE+FL

Energy savings induced by carbon prices

Industry
16

Building (residential & commercial)

The reboundeffects due toreductions incarbon prices

Final energy
consumptions will
be reduced byaround 25% in
B2DS withcheaper carbon
prices, thanks tothe impacts ofend-use
technological andsocial innovations.

TransportRef.) Global final energy consumption in 2019: 10 Gtoe/yr; baseline final energy consumption in 2050: 14 Gtoe/yr

GHG emissions (preliminary)

B2DS (well below 2 °C)

-60

-50

-40

GHG emissions [GtCO2eq/yr]-30-20

The reductions independency on CDR options-10010Iron & steel

B2DS-StdCement

2040

Pulp & paper
Chemical
B2DS-All_CE

Other industries
Road transport
Domestic aviation

BL-All_CE+FL

Other domestic transport
Intn'l aviation bunkers

B2DS-Std

Intn'l marine bunkers
Residential & commercial

2050

Power generation
B2DS-All_CE

Other energy coversion
LULUCF CO2
Process CO2

BL-All_CE+FL

Non-CO2 GHG
DACCS

17

The dependency on CDR options (e.g., BECCS and DACCS) will be reduced.

2015

18

OECD
ASIA

2030

MAF
LAM
REF

2040

World total
2050

B2DS-All_CE+FL

B2DS-All_CE

B2DS-Std

BL-All_CE+FL

BL-All_CE

BL-StdB2DS-All_CE+FL

B2DS-All_CE

B2DS-Std

BL-All_CE+FL

BL-All_CE

BL-StdB2DS-All_CE+FL

B2DS-All_CE

B2DS-Std

BL-All_CE+FL

BL-All_CE

BL-StdFInal energy consumption per capita [toe/capita]

Final energy consumption per capita by region(preliminary)

4.0

3.5

3.0

2.5

2.0

1.5

1.0

The ‘High-with-Low’
Scenario induces
smaller differencesin final energy
consumption acrossregions.

0.5

0.0Changes in emissions reduction costs (preliminary)
Total energy systems costs (unit: Billion USD/yr)
The annual average in 2030-2040

The annual average in 2040-2050

Mobil

Resid

Build

Food

Ind

All_CE

All_CE
Mobil
+FL

Resid

Build

Food

Ind

All_CE

Baseline ▲547▲339▲1▲57▲4▲894▲963 ▲1601 ▲459▲1▲74▲7▲1971 ▲2085▲556▲352▲0▲64▲5▲926 ▲1038 ▲1635 ▲477▲6▲90▲14▲2037 ▲2266

B2DSAll_CE+FLNote) The changes in the costs compared those in the standard scenarios for each emissions reduction targets.

CO2 marginal abatement costs (carbon prices) in B2DS (unit: USD/tCO2eq)
2040

Carbon prices
for B2DS

2050

B2DS-Std

B2DS_AllCE

B2DS_AllCE+FL

B2DS-Std

B2DS_AllCE

B2DS_AllCE+FL

68−31057−23850−195146−739

123−52460−364

Note) The cost ranges are those due to the differences in emissions targets across countries.
19The emissions reduction costs canbe reduced greatly through theassumed innovations in end-use
sectors.
The affordable cost measures will
also help increases in feasibility
and achieve multiple SDGs.

Conclusions

20



EDITS project is developing comprehensive and quantitative scenarios ofnarrative ‘High-with-Low’ scenario (high well-being with low resourceuse) with DNE21+ model and other several models.

Digitalization and the related innovations could induce rapid changes inour society, reduce several resources and the embedded energyconsumptions through circular and sharing economy, and achieve cheaper
cost for deep emissions reduction.

Our EDITS project members believe that better understandings on thepotential changes and great contributions to emissions reduction as wellas achievements of multiple SDGs through comprehensive analyses will
induce the movements in all people including consumers, industrialpeople, and policymakers.

Thank you very much for your attention!

EDITS is an initiative coordinated by the Research Institute of
Innovative Technology for the Earth (RITE) and International Institutefor Applied Systems Analysis (IIASA), and funded by Ministry ofEconomy, Trade, and Industry (METI), Japan.

Keigo AkimotoGroup Leader of Systems Analysis Group
Research Institute of Innovative Technology for the Earth (RITE)
E-mail: aki@rite.or.jp

https://iiasa.ac.at/web/home/research/researchPrograms/Energy/Research/EDITS/EDITS.html
This presentation is licensed undera Creative Commons Attribution 4.0 International License