The COP28 Side-event at the Japan Pavilion
on December 11, 2023 in Dubai− Demand response to disruptions −Importance of demand side solutions for thetransformation towards net-zero society

Keigo Akimoto, Research Institute of Innovative Technology for the Earth (RITE)
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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 full
spectrum 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 Induced
by 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., ISCTE, Univ. of
Wisconsin, UCSB, UFRJ/COPPETEC, The Korean Society of Climate Change
Research, The Univ. of Tokyo, Osaka Univ., RITE, and others
ゴマーク（下段は、左から産業、建築、運輸、データ、定性的シナリオの作業部会）
Nearly 100 researchers including many IPCC lead authors are involved in.
[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 the
3 integration of SDG and climate objectives in synergistic policy designs.

Low Energy Demand (LED) scenarios
IPCC Special Report on 1.5 C (SR15)
IPCC 6th Assessment
Report (AR6)LD
LEDThe IPCC reports show some scenarios of low energy
demands, but more comprehensive and quantitative
scenarios will be needed.
4

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

LED

Source: IIASA, LED scenario

5

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

Digitalization impacts (IPCC AR6 Ch.5)

6

Global emission reduction costs
and potentials in 2030 by sector
and technology
The bottom-up studies of IPCC AR6
WG3, Fig. SPM.7
SPM C12.1
Based on a detailed sectoral
assessment of mitigation options,✓ It is estimated that mitigation
options costing USD100 tCO2-eq-1 orless could reduce global GHG
emissions by at least half of the2019 level by 2030.
✓ Options costing less than USD20
tCO2-eq-1 are estimated to make up
more than half of this potential.

✓ Large amounts of emissions reduction potential
with negative costs are estimated when typical
discount rates, e.g., 5%/yr, are employed.

Costs and potentials evaluation: 2030 global emissions−Sectoral bottom-up studies vs. IAMs−
Bottom-up studies(IPCC Fig. SPM7)

IAMs(IPCC Fig.3.33)

CO2(30.3 GtCO2/yr)

35～44 GtCO2/yr

GHGs

44.3 GtCO2eq/yr(49～58
GtCO2eq/yr)

CO2(15.5 GtCO2/yr)

23～34 GtCO2/yr

29.5 GtCO2eq/yr(37～48
GtCO2eq/yr)

IAM estimates in the IPCC AR6

Below 20
USD/tCO2eq

100 $/tCO2
20 $/tCO2

8

Below 100
USD/tCO2eq
GHGs[compared to baseline：
38 GtCO2eq/yr (32～44 GtCO2eq/yr)]Note) The numbers in parentheses for CO2 and GHG emissions are the values taken fromthe report and simply converted to CO2 or GHG using the actual values of the difference inglobal emissions in 2019 (14 GtCO2/yr).

✓ There is a big difference in costs and potentials estimated by technology
bottom-up studies and IAMs.

Example of factors affecting the discount rates
for investment (hidden costs)[Technology-specific factors]• High investment returns are required when the technology is not yet sufficiently mature (e.g., CCS) or when there are challenges interms of social acceptance (e.g., nuclear power), due to the high risk of the technology.
• Product value depreciation: The faster the change to a new product or service, the faster the value of the product will be
depreciated, and the higher the discount rate will be. If the opposite is true, the discount rate will be lower. (Energy supply such as
electricity < material supply such as iron, cement, etc. (mainly energy-intensive industries) < hot water, air conditioning, etc. (therelationship with material supply is not always clear) < lighting, refrigerators, etc. < TVs, automobiles, etc.)• If price reductions are expected in the future, it becomes rational to wait to invest, and the discount rate will be higher.
[Preferences of investors and consumers, etc.]• Funds constraints: If another investment has a higher return, its expected rate of return is referenced.
• Hidden costs (e.g. opportunity costs)• Consumer preference: Purchase of environmentally friendly products (e.g., early adopters), a strong perception of co-benefits (inhousing, etc.)• Discount rates are higher when the life expectancy of the residents is considered shorter than the lifetime of the house.
• Landlord-tenant problem etc. (Commercial sector)• Bounded rationality (limitations of people's ability to process information and make decisions, etc.)[Factors related to the trading market, investment environment, and surrounding institutions, etc.]• Greater returns are required for climate change investments when there is greater uncertainty about energy and climate policy.
• Higher volatility in the carbon price market will demand greater returns on low-carbon and decarbonized investments.
• Under electricity liberalization, greater volatility in price indexes will demand greater returns on investments in power sources withlarge capital costs (low-carbon and decarbonized power sources).
• Short-term investment payback tends to be preferred under quarterly financial statements.

9

User cost of capital10

The user cost of capital, formulated in Jorgenson's neoclassical investment theory to account for the
optimizing behavior of firms, is defined as the cost that an investor must pay to obtain services from
capital goods and is expressed by the following equation (partially simplified). It also corresponds to therental price of the equipment.
𝐾𝐴
𝑃𝑘𝑗𝑡= (𝑟𝑘𝑗𝑡 + 𝛿𝑘𝑗 − 𝜋𝑘𝑡 ) ⋅ 𝑃𝑘𝑡

User cost of capital (PtK), real market price of the capital (PtA), real interest rate at time t (rt), depreciation rate (),cost associated with changes in the price of capital goods (capital gain or loss) (), type of assets (k), country or
economic agent ( j )The coefficient (rkjt＋kj－kt) on the real market price of the capital (PtA) is usually called the annualization factor.
✓ In a consistent manner with economic indicators, user costs of various capitals have been measured.
✓ While “implicit discount rates” explain high discount rates focusing mainly on bounded rational behaviors
including heterogeneous consumers, “user cost of capital” explains high discount rates focusing mainly on
rational discount rates for using capitals or on rational rental prices of capital.
✓ In the estimation of the “user cost of capital”, the bounded rational factors discussed in the “implicit
discount rate” are considered to be included in the values of the parameters r, ,  , respectively.

Scenario assumptions
Emissions
reduction

BL-StdBL-Mobil
BL-Resid
BL-Build

Baseline(non
specificclimatepolicies)

Energy demand reductions due to mainly digitalization
Transport
1)

Residential
2, 3, 4)

Building
5)

Food
6)

―

―

―

―

Industry Spill over
7)
8)
―

―

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

Demandflexibilities inelectricity(EV, HP, CGS)

―

―

X
X
X

BL-Food

X

BL-IndX

BL-All_CE

X

X

X

X

X

X

BL-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 G7
countries)

X
X
X
X

B2DS-IndX

B2DS-All_CE

X

X

X

X

X

X

11
B2DS-All_CE+FL

X

X

X

X

X

X

Final energy consumption (preliminary)Baseline (without additional climate policies); relative to the BL-Std scenario

12

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-Build
BL-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

12

-1200

-1000

-800

-600

-400

-200

0

200

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 are
estimated.

Ref.) Global final energy consumption in 2019: 10 Gtoe/yr; baseline final energy consumption in 2050: 14 Gtoe/yrFinal energy consumption (preliminary)
B2DS (well below 2 C)
最終エネルギー消費量[Mtoe/yr]Finalenergy consumption[Mtoe/yr]
-3000

-2500

-2000

-1500

-1000

-500

0

500

2030

B2DS-Std

B2DS-All_CE

Energy savings induced by carbon prices

B2DS-All_CE+FL+GR

Energy savings induced by carbon prices

2040

B2DS-Std
B2DS-All_CE

Additional emissions reduction
contributions (“avoided emissions”)
due to DX

Energy savings induced by carbon prices

B2DS-All_CE+FL+GR

Energy savings induced by carbon prices

2050

B2DS-Std
B2DS-All_CE

Energy savings induced by carbon prices

B2DS-All_CE+FL+GR

Energy savings induced by carbon prices

Industry
産業部門
13

民生部門
Building

The reboundeffects due toreductions incarbon prices

DX solutions including
circular and sharing
economy could
induce around 6%
reduction of total
final energy
consumption, whichcorresponds to asimilar level of energy
savings due to carbon
prices for the B2DS.

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

GHG emissions reduction (preliminary)In 2040

14

GHG emissions [GtCO
2eq/yr][GtCO(relative
to thebaseline(BL-Std) emissions)
GHG排出量2eq/yr]（ベースライン（BL-Std）排出量からの変化）
GHG emissions[GtCO2eq/yr]-60

Baseline
Baseline

B2DS-Std
BL-All_CE

-40

-50
-35

-30-40

-25

-30

-20
-15

-20

-10-10-50Additional emissions reduction
contributions due to DX

B2DS-All_CE
BL-All_CE+FL+GR

L-All_CE+FL
B2DS-Std

B2DS
B2DS

B2DS-Std
B2DS-All_CE

GHG emissions reduction induced by carbon prices

B2DS-All_CE
B2DS-All_CE+FL+GRGHG emissions reduction induced by carbon prices

L-All_CE+FL

0

105
鉄鋼部門
Iron
& steel✓ Large potentials inemissions reduction
紙パルプ部門Pulp& paperin end-use sectors
化学部門
Chemical
その他産業部門
including products
Other industries
道路交通部門and service solutions
Road transport
国内航空部門Domestic aviation
also exist with lower
その他国内運輸部門
Other domestic transport costs.
国際航空部門Intn'l aviation bunkers
国際海運部門✓ The digitalization
Intn'lmarinebunkers
民生部門
impacts in end-use
Residential
発電部門 & commercial
sectors on GHG
Powergeneration
その他エネ転部門
emissions reduction,
Otherenergy coversion
土地利用起源CO2
which will also reduce
LULUCF
CO2
プロセス起源CO2
CO2以外のGHG
ProcessCO2‘hidden costs,’ could
DACCS GHG
Non-CO2
be also large.
セメント部門Cement

DACCS

Additional emissions reduction
contributions due to DX

14

Larger deployments of high efficient
end-use productions including heatpump technologies due to carbon prices

Larger deployments of EVs including
PHEV due to carbon prices

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

IPCC AR6 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 should also be tackled in our EDITS project, and it is desirable for the EDITS
project to contribute to the next IPCC report and other opportunities.

15

Topics the EDITS and this event focusing on:
16◼

There are large opportunities in end-use sectors to reduce GHG emissions with lower costs.

◼

In addition, many of the end-use measures will help achieve the multiple SDGs, and there are great
opportunities even in a world of disruption. We should focus more on end-use measures for effective
global emissions reductions.

◼

Relatively large hidden costs (large barriers of diffusions) are observed in end-use sectors in the realworld, due to high depreciation rates, high expected price reductions, and bounded rationalities.

◼

Digitalization technologies could contribute to reducing the hidden costs as well as inducing circularand sharing economies. In addition, changes to lifestyle preferences for long-term cycles for productswith good qualities will be one of the potential opportunities for decreasing depreciation rates etc.

◼

Better evaluations of the reduction effects of the Scope 3 emissions, particularly of the downstream,with policy supports, will be significant in order to offer the incentive in firms. The Government ofJapan is supporting the scheme of “avoided emissions.” Firms will have the opportunities to reduce
the emissions for the products and services belonging to consumers.

◼

In order to induce the demand-side policies, visualizations of end-use measures which contribute tohigh well-being with high research credibility will be important.
16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 of
Economy, Trade, and Industry (METI), Japan.

17

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
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This presentation is licensed undera Creative Commons Attribution 4.0 International License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.)

19

Global costs and potentials by sector and technology in 2030

ーStandard technology scenario & Standard discount rateー20

CO2 marginal abatement cost [$/tCO2]

Power: effic. improv./
shift among fossil fuels

Power: CCUS
Power: hydro power &geoth.
Power: wind power
Power: solar thermal
Power: e-methane
Other energy conv. sector
Industry: CN fuels
Transport: CN fuels
Building: CN fuels
DACCS, mineralization

Power: biomass
Power: nuclear power
Power: solar PV
Power: hydrogen & ammonia
Industry: CCUS
Industry: effic. improv./
shift among fossil fuels
Transport: effic. improv./
shift among fossil fuels
Building: effic. improv./
shift among fossil fuels

LULUCF

biofuels
BECCS

CO2 absorption in concrete
products for roadHEV

biofuels
HEV

BECCS

energy savings of appliances etc.
Shift from coal to gas in chemical; energy
savings in less energy intensive sectors etc.

CO2 absorption in ready-mixed
concrete/curing promotion

Solar PV,
Wind power

BECCS

CCS for iron & steel, chemical, and cement productions
co-firing biomass of coal
power; gas power with CCS
high effic. coal power;
shift from coal to gas
power

HEV

CCS (including
BECCS)

shift from coal
to biomass

Nuclear power+13% relative to 2015(45.7 GtCO2/yr)

CO2 emissions in 2015(40.5 GtCO2/yr)

CO2 reduction compared with Baseline [GtCO2/yr]

IPCC IAMs estimates below 20 $/tCO2:
44～35 GtCO2/yr in 2030

Estimates by using DNE21+ model

IPCC IAMs estimates below 100 $/tCO2:
34～23 GtCO2/yr in 2030

The reduction potentials by the IPCC
bottom-up studies below 100 $/tCO2:
29 GtCO2/yr (24−36 GtCO2/yr) relative
to baseline emissionsNote) The potentials in CO2 are simply
converted using the emission gap in 2019
between CO2 and GHG (76%).

Global costs and potentials by sector and technology in 2030

ーStandard technology scenario & low discount rate of 5%/yr*ー21Power: effic. improv./
shift among fossil fuels

CO2 marginal abatement cost [$/tCO2]

Power: biomass
Power: nuclear power
Power: solar PV
Power: hydrogen & ammonia
Other energy conv. sector

Industry: effic. improv./
shift among fossil fuels
Transport: effic. improv./
shift among fossil fuels
Building: effic. improv./
shift among fossil fuels

LULUCF

Power: CCUS
Power: hydro power & geoth.
Power: wind power
Power: solar thermal
Power: e-methane
Industry: CCUS
Industry: CN fuels
Transport: CN fuels
Building: CN fuels
DACCS, mineralization

BECCS

BECCS

biofuels

Estimated by using
DNE21+ model* 5% discount rate in
all countries, sectors
and technologies

biofuels

BECCS

energy savings of appliances etc.
PHEV, BEV
Shift from coal to gas in chemical; energy
savings in less energy intensive sectors etc.
CCS for iron & steel, chemical,
and cement productions
co-firing biomass of coal
power; gas power with CCS

CO2 absorption in
concrete products
for road
BECCS

Solar PV,
Wind power

CO2 absorption in ready-mixed
concrete/curing promotion

shift from coal to biomass

CCS (including
BECCS)

high effic. coal power; shift
from coal to gas power

The baseline emission
in 2030 in the standard
discount rate case:45.7 GtCO2/yr

Nuclear power

CO2 reduction compared with Baseline [GtCO2/yr]
The emissions reduction potentials withnegative costs compared with those in thestandard discount rate case: 3.8 GtCO2/yr,
which include:- hybrid vehicles- electrification of heating in buildings

The reduction potentials by the IPCC bottom-up studies below 100$/tCO2: 29 GtCO2/yr (24−36 GtCO2/yr) relative to baseline emissionsNote) The potentials in CO2 are simply converted using the emission gap in 2019
between CO2 and GHG (76%). The allow of the reduction potentials are shown
from the baseline emission in the standard discount rate case.

Power tech’s installations in 2030: Comparison with the IPCC
<100$/tCO2:
80EJ/yr

0$/tCO2

20$/tCO2

50$/tCO2

100$/tCO2

200$/tCO2

40

IPCC report: Below 100 USD/tCO2eq

Power generation [EJ/yr]

30

Hydro

20

10

Geothermal

IPCC

Std. tech.
scenario
Solar PV

Rapid cost red. inren. etc.

IPCC

Std. tech.
scenarioWind

Rapid cost red. inren. etc.

IPCC

Std. tech.
scenario

Rapid cost red. inren. etc.

Hydro & geotthermal

IPCC

Std. tech.
scenario
Nuclear

Rapid cost red. inren. etc.

IPCC

Std. tech.
scenario

DR5%,w/o IC

DR: 5%

DR: std.

DR: 5%

DR: std.

IAM

sectoral

DR5%,w/o IC

DR: 5%

DR: std.

DR: 5%

DR: std.

IAM

sectoral

DR5%,w/o IC

DR: 5%

DR: std.

DR: 5%

DR: std.

IAM

sectoral

DR5%,w/o IC

DR: 5%

DR: std.

DR: 5%

DR: std.

IAM

sectoral

DR5%,w/o IC

DR: 5%

DR: std.

DR: 5%

DR: std.

IAM

sectoral0

Rapid cost red. inren. etc.

Biomass✓ Large impacts of considerations of grid integration costs on the estimated costs and potentials of electricity generation can be observed
as well as the discount rate for investments. Demand flexibilities using digitalization technologies with lower costs will be important.

‘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 for
Modelling

23

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
24

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 impacts

5) Longer life time of buildings
due to improv. in city planning- Potential Redductions in
cement 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 yet
supermarkets 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 products25- 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.