The Japan Pavilion, COP26
November 11, 2021

Background and Overview of EDITS:
Energy Demand changes Induced byTechnological and Social innovations
Keigo Akimoto
Systems Analysis Group
Research Institute of Innovative Technology for the Earth (RITE)

Categorization of deep emission reduction scenarios
for below 1.5 C (IPCC SR15)

2Source) IPCC SR15

Much lower energy
demand scenarios thanthose of SSP1

SSP1
Low

- Low energy demand is induced
autonomously on economic principle ✓through technological and socialinnovations✓- Low carbon prices (business-basedmeasures even without strictclimate policies)

SSP2(Middle scenario)
High

Final energy demands

Comprehensive risk management is essential, and various
kinds of technologies play their respective roles.
On the other hand, opportunities for end-use technology
innovation, inducing low energy demands, and their impacts
on overall climate change mitigation should be more
focused. (P1)

SSP5

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

G20 Karuizawa Innovation Action Plan on Energy Transitionsand Global Environment for Sustainable Growth (June 2019)“We recognize the importance of quantitative analysis on better
understanding future energy demand and supply and the role ofinnovation of both sides driven by digitalization, 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 anddevelop the full spectrum of economy-wide scenarios for energy andclimate models.”Note) This is also an annex document of the G20 Osaka Leader’s declaration.

3

Objectives of the EDITS Project4• To create a research community focusing on end-use, demand-side
perspectives that further dialogue and cross-fertilization of research andpolicy analysis through the sharing of novel data, novel concepts,methodologies, and policy analyses.
• To improve the state-of-art of demand modeling in environmental andclimate policy analysis via methods and model intercomparisons andassist the transfer of conceptual and methodological improvements
across disciplines, sectors, and environmental domains.
• To inform better 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 the SDG integration and climate objectives in synergistic
policy designs.
The EDITS project starts from year of 2020.

The Organization and the Research Topics of EDITS
5EDITS Organization
Sectors

Buildings
Transport
Industry

Model comparison

Data• Data repository
Metadata, data owner, how to access• Use existing structures (e.g. zenodo)• Initially internal

Model survey / classification• Revise model survey• Involve broader community• Utility of approaches for demand side transformation• Papers on model classification by sector••••

Harmonize narrative (e.g. LED) and basic drivers
Joint scenario development
Show value added of diversity
Additional insights from granularity

15Digitalization
EquityLifestyles / Behavior
Business models
Theory development
Source IIASA, EDITS Quarterly Meeting (Mar. 2021)

Narratives
Complementarity ProtocolSynthesis

Official Members of the EDITS Project6InstituteResearchers (limited lists)

RITEKeigo Akimoto, Miyuki Nagashima, Ayami Hayashi

IIASA

Keywan Riahi, Arnulf Grübler, Bas van Ruijven, Shonali Pachauri, Benigna BozaKiss, Paul Kishimoto

OECD/ITF

Luis Martinez, Guineng Chen

Stanford University
University of Tokyo

John Weyant
Masahiro Sugiyama, Yiyi Ju

Lawrence Berkeley National Labs (LBNL)

Nan Zhou, Tianzhen Hong, Wei Feng, Nina Z.Khanna, Hongyou Lu

Utrecht University
CMCC
Tsinghua University

Oreane Edelenbosch, Ernst Worrell
Massimo Tavoni, Elena Verdolini, Giacomo Marangoni
Ou Xunmin

UFRJ/COPPETEC

Roberto Schaeffer, Talita Borges Cruz, Matheus Poggio, Alberto Santos

Asian Institute of Technology (AIT)Osaka University
University of Wisconsin
University of California, Santa Barbara (UCSB)
Yonsei University/Korea Environment InstituteCentral European University (CEU)
University of Natural Resources and Life Sciences,Vienna (BOKU)
University of Freiburg
Mercator Research Institute on Global Commons andClimate Change (MCC)
Tyndall Centre for Climate Change Research
University of Groningen

Joyashree Roy, Pal Indrajit, Shreya Some, Nandini Das, J. Chatterjee, SinghYoshikuki Shimoda, Yohei Yamaguchi
Greg Nemet
Eric Masanet
Tae Yong Jung, Yong-Gun Kim
Diana Urge-Vorsatz, Souran Chatterjee

Other major contributors

Dominik Wiedenhofer, Jan Streeck
Stefan Pauliuk
Leila Niamir, Felix Creutzig
Charlie Wilson
Linda Steg
Julia Steinberger, Bianka Shoai-Tehrani, Joni Jupesta, Poornima Kumar, Marta
Baltruszewicz

Global Energy Consumptions and Losses by Sector
7

Primary energy production = 100Source) A. Grubler (IIASA), ALPS International Symposium (2016)

Required services need only 4-5% ofprimary energy consumption.

There is ample room to improve energy productivity in end-use sectors. However, currently there
are significant barriers to enjoying the possible productivity improvement because of hidden costs.
Nevertheless, IT, AI and other related technologies may overcome the barriers at affordable prices.

Energy Demands Estimated from Decent Living Standard (DLS)
8Service-driven energy accounting model

Final energy requirement in India(DLS_ACCEL) [GJ per capita]
Construction energy

Operational energySource) Rao et al., “Energy requirements for decent living in India, Brazil and South Africa”,Nature Energy (2019)••

Some studies tried to estimate final energy demands according to a bottom-up measure fromrequired service demands for DLS.
Much larger energies compared with those for DLS are consumed, particularly in developed countries.

Learning Rates of Energy Technologies:
Supply vs End-use

9Wilson et al., Nature Climate Change, 2012

The learning rates of the “granular” end-use technologies are greater than those of the“lumpy” energy supply technologies in general.

Transport: CASE
10Connected; Service & Shared

Autonomous; Electric

The operation ratio ofautomobiles is about 5%. The
large room for improvement
exists by the achievement of fullyautonomous cars.

EV ”e-Palette” only for Autono-MaaSSource) ToyotaPossibility of integration of cars
and near distance airplaneSource) Airbus, AudiSource) Nissan

Changing the shape of cars

The sharing may reduce the number ofcars and the consumption of materials,and change the form of cities.

Source) Jari Kauppila, ALPS International Symposium (2019)

Estimated Number of Vehicles in Sharing Car Systems: Dublin
11◼

Build a model based on actual data
(population distribution, road/public
transport network, weekday trip
demand (time zone, OD), trip
preferences, etc.)◼

In the case of Dublin, Ireland, replacing
all private cars with shared cars enables
to supply the current mobility with justunder 2% of the number of cars.

◼

Replacing 20% of private cars (evenwithout EV) reduces CO2 emissions to▲ 22%Source) OECD/ITF, Shared Mobility Simulations for Dublin (2018)

Scenario#Bus1

100% Replacement

2

Keep

4

100% Replacement
Keep trips where Bus
with headway < 5min6

Simulation results: changes relative to Baseline [%]Car
100% of trips
replaced
20% of trips
replaced

Passengerkm

Vehiclekm

CO2emissions

Congestion

TraveltimeEquivalentprivate carvehicles+51▲38▲31▲37+72▲98+32▲42▲31▲43+134▲99+16▲23▲22▲7+6▲18+13▲25▲23▲9+6▲18

Other Impacts of Technological and Behavior Changes
on Several Sectors (Examples)[Food]◼ It is regarded that approx. 30% of GHG emission (even more depending on boundaries) comesfrom the food system. Also, food wastes and losses are about one-third of total production
globally.
◼ More accurate food demand forecast through AI / ICT could lead to a decrease in foodwastes and losses and energy consumption, and GHG emission accordingly.
◼ Consequently, reducing plastic containers, store spaces in supermarkets, energy forrefrigerators/freezers and transport energy could be triggered.

[Apparel]◼
◼
◼
◼

It is said that 50% of clothes are unused and disposed of it
Preference changes, especially among young generations, e.g. wearing suits is not widespreadamong young people, and progress of E-commerce, e.g. anything is available without traveling.
Just-in-time system using AI / ICT, enabling accurate demand forecast and not dependent on
mass production.
Large spaces for display and considerable energy for construction and air conditioning arenecessary for extensive commercial facilities; however, it is possible to reduce them..

[3D printing]◼
◼
◼

3D printers (Additive Manufacturing: AM) are evolving
Compared to injection molding, it is possible to create a complicated shape and a light productwith the same strength in many cases.
It enables small batch custom manufacturing instead of mass production and avoids mass
production and mass disposal.

12Rebound Effects on Energy Consumptions throughDigitalization (Literature Reviews)
SectorEstimate d energy
consumption;[Representative number]

E-publication-90% to +3000%;[-70%]

E-news-1400% to +550%;[-70%]

E-business-91% to +179%;(Insufficient evidence)

E-music-87% to +235%;[-60%]

E-videos and e-games-70% to +450%;[0%]Teleworking-15% to -0.01%

Vehicle distance travelled -20% to +3.9%

Person distance travelled -19% to -11.9%
Source) Court et al., “Digitalization of goods: a systematic review of thedeterminants and magnitude of the impacts on energy consumption”,
Environmental Research Letters (2020); Hook et al., “A systematic review of theenergy and climate impacts of teleworking”, Environmental Research Letters (2020)13✓ There can be several kinds ofrebound effects on energy
consumptions.
✓ Basically, reductions in energy
consumptions due toimprovements in digitalization
technologies are estimated.
✓ However, the estimated energy
consumptions vary widely
according to the boundaries ofrebound effects, etc.