COP24 side-event: Mitigation Policy Choices and Levels of Effort

December 13, 2018

Evaluations on emission reduction efforts of
NDCs and the Implications of Global
Effectiveness on Climate Change Mitigation
Keigo Akimoto,Takashi Homma, Fuminori Sano
Systems Analysis Group

Research Institute of Innovative Technology for the Earth(RITE)

Global CO2 Emissions Trajectory

Source) Global Carbon Project2- Global CO2 emissions increased more rapidly
since 2000.
- The emissions were almost constant from 2013
to 2016 while the global GDP increased.
- According to our analysis, the largest
contribution was due to production adjustmentsof iron & steel etc. mostly in China (since 2010,
the productions were too large), and the second
largest contribution was due to shale gas in the
US.
- The global CO2 emissions after 2016 areincreasing again due to mainly mitigations of
the production adjustments in China.

2000

Impact of shale gas?

6,500

1800

6,000

－8%

5,500

－8%

5,000

1600
1400
1200
1000

4,500

800

4,000

600

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

0
2004

2,500
2003

200
2002

3,000
2001

400

2000

3,500

400

Differences of CO2 emissions (MtCO2)

100
2014

2013

2012

0
2000

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

7,000

CO2 emissions (MtCO2)

US

2001

0
2000

1,500

600

2011

2,000

200

2010

500

300

800

2009

2,500

600

400

2008

1000

1,000

2007

－17%

3,000

700

500

2006

1500

3,500

+4%

1,200

2005

－11%

2004

4,000

800
－1%

1,400

2003

CO2 emissions (MtCO2)

2000

3

Financial crisis Impact of nuclear
shutdown
aftermath

2002

4,500

Japan 1,600

2001

2500

Financial crisis aftermath?

CO2 emissions (MtCO2)

5,000

Differences of CO2 emissions (MtCO2)

EU28

Differences of CO2 emissions (MtCO2)

Production-based & Consumption-based CO2 emissions

Production‐based CO2 emissions
Consumption‐based CO2 emissions
Differences (=[Consumption‐based] ‐ [Production‐based]) [Right axis]

Source: estimated by RITE

- The embodied emissions in trade (difference
between Consumption-based CO2 and Productionbased CO2) increased in EU, almost constant in
the US, and slightly reduced in Japan between
2000 and 2014.
- Climate policies and other kinds of policies affectdomestic emissions and also global emissions
through international trade.

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

4

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 outside the UK
according to the analyses ofconsumption-based CO2
emissions.

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

5

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

Employment activities andinformation service sectors
whose share of energy cost
are relatively small achieved
relatively high economic
growth between 2000 and2014 in Germany. Relatively
cheap Euro compared with theindustry competitiveness ofGermany helped motor vehicle
sectors to achieve relatively
high growth.

Note: 2010 local currency base

6

Consumption-based CO2 per GDP

1.2
1.1
1.0
0.9
0.8
0.7
0.6
0.5

US
Japan
UK

2014

2011
2012
2013

2009
2010

2007
2008

2005
2006

2003
2004

2001
2002

Sweden
2000

2014

2012
2013

2010
2011

2008
2009

2006
2007

2004
2005

[2005年=1.0][Year 2005=1.0]

Production-based CO2 per GDP

2002
2003

1.3
1.2
1.1
1.0
0.9
0.8
0.7
0.6
0.5

2000
2001

[Year
2005=1.0][2005年=1.0]

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

Source: estimated by RITE

- In terms of the production-based CO2 emissions per GDP, the degrees of improvement of the four countries
differs greatly.
- However, concerning the consumption-based emissions, the improvement rates of the four countries do notdiffer that much 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.
- Focusing only on production-based emissions may lead to wrong interpretation of emission reduction efforts ofindividual nation.

How to measure the comparability of efforts of NDCs
The Paris Agreement allows pledges of various type emission reduction targets and adopts areview process for them.
The submitted Nationally Determined Contributions (NDCs) include the targets of emissions
reduction from different base years, CO2 intensity, and CO2 emission reductions from baseline(w./w.o. clear definition of baseline).
We need to interpret them through comparable metrics to measure the efforts:
Simple metrics (easily measurable and replicable)
etc.
- Emissions reduction ratios from the same base year Advanced metrics (more comprehensive, but require forecasts)- Emission reduction ratios from baseline emissionsetc.
- Emissions per unit of GDP
 More advanced metrics (most comprehensive, but require modeling)- Final energy prices- Marginal abatement cost (per ton of CO2)etc.
- Abatement costs as a share of GDP

and the effects on international competitiveness of the NDCs are significant for sustainablemeasures.

7

Emissions reduction ratio from base year of NDCs for major countries
Emissions reduction ratio from base year
Japan：in 2030, -26% from 2013
levels
US： in 2025, about -26 to-28% from 2005 levels
EU28： in 2030, -40% from 1990
levelsRussia： in 2030, -25% to-30% from 1990 levels
China： in 2030, CO2 intensity of 60% to -65% from 2005 levels

From 1990

From 2005

From 2013-17.8%-24.3%-26.0%-15 to -17%

-26 to -28%

-19 to -21%

-40%

-35%

-24%

-25 to -30%

+13 to +6%+7 to 0%+406 to +343%+96 to +72%

+17 to +2%

Underlined: official NDCs, Others: estimated by RITE

Emission reduction ratios vary depending on the base year. The emission reduction ratios of
NDCs cannot be used directly for comparison of emission reduction efforts, mainly because
the base years are different across the nations.

8CO2 marginal abatement costs of the NDCs
9

Average of 2025-2030

2030 (2025 for the U.S.)【World GDP loss due to mitigation】 NDCs:0.38%; the global least cost：0.06%
The least cost (equal marginal abatement costs)：6$/tCO2
Switzerland

380

Japan

378

EU28

210

Canada

166

Korea

144

New Zealand95

United States

85

Norway70

East Europe (Non-EU member)

58

Thailand54

Australia

33

Mexico27

Kazakhstan

14

Belarus12

Russia4South Africa

1

Turkey

0

India

0

Ukraine

0

China

0

0

Source: J. Aldy et al., Nature Climate Change, 2016

50

100

150

200

250

300

350

CO2 marginal abatement cost ($/tCO2)

Model: RITE DNE21+
Source: K. Akimoto et al., Evol. Inst. Econ. Rev., 2016

The estimated marginal abatement costs of NDCs are largely different among countries, and therefore theworld total mitigation costs are much larger than those for achieving the aggregated emission reductions
under the least cost measures, i.e., under globally uniform MAC.

400

CO2 marginal abatement cost for the U.S, EU and Japanconsidering several kinds of policy constraints
I-aI-b
UnitedStates

I-c
I-d

EU28

II-a
II-b
II-c

10

Assumptions
26% reductions relative to 2005 with least cost measures
28% reductions relative to 2005 with least cost measures
26% reductions relative to 2005. The amount of emission reductions in power sector proceeds
according to the estimates for the Clean Power Plan by EPA.
28% reductions relative to 2005. The amount of emission reductions in power sector proceeds
according to the estimates for the Clean Power Plan by EPA.
40% reductions relative to 1990 with least cost measures
40% reductions relative to 1990 for both the UK and non-UK EU nations
The emission reductions for EU-ETS sectors are determined by the planned emission allowances,and the non-ETS sectors fill the rest of reductions to meet the 40% reductions relative to 1990.

26% reductions relative to 2013 with least cost measures. Maximum share of nuclear power in
III-a electricity generation is assumed to be 20%.
Japan

III-b 26% reductions relative to 2013 with least cost measures. Maximum share of nuclear power inelectricity generation is assumed to be 15%.
26% reductions relative to 2013. Electricity share assumed to be same as the energy mix ofIII-c Japanese governmental plan.
III-d 26% reductions relative to 2013. Electricity share assumed to be nuclear: 15%, renewables: 29%,
others: same as the energy mix of Japanese governmental plan.

CO2 marginal abatement cost for the U.S, EU and Japanconsidering several kinds of policy constraints
CO2 marginal abatement cost ($/CO2)

500

400

The annual total emission reduction cost ;

II-c
I-d

III-d
III-c

I-c

300
II-b
II-a

200

11

III-b

US: the cost for I-c is about 5.1 times larger thanthat for I-a.
EU: the cost for II-c is about 1.5 times larger thanthat for II-a.
Japan: the cost for III-c is about 2.2 times larger
than that for III-a.

III-a

100

0

I-b
I-a

I. US
I-a: -26%; the least costI-b: -28%; the least cost
I-c: -26%; power sector
according to CPP
I-d: -28%; power sector
according to CPP* CPP: Clean Power Plan

Source: estimated by RITE DNE21+

II. EU
II-a: the least costII-b: Brexit (-40% for UK)
II-c: splitting into ETS andnon-ETS sectors

III. Japan
III-a: the least cost undernuclear of maximum 20%
III-b: the least cost undernuclear of maximum 15%
III-c: following the NDC
including the energy mix(nuclear of 20%)
III-d: following the NDC
including the energy mix
but nuclear of 15%- It is not easy to achieve the least
cost measures because there arevarious kinds of social andpolitical constraints in each nation.
- The mitigation costs constrainedby other policies can be muchhigher than those under the least
cost measures.

Analyzed three cases for evaluating economic impacts of NDCs
for major nations/regions
Case 1: NDCs

Case 2: Equal MACs
among sectors withineach nation (Autarky)

Case 3: Equal MACs
among nations andsectors (Global trade)

Individual achievement of
national emission reduction
targets without CO2 trading

Global achievement of
aggregated emission
reduction targets

National emission reduction
targets in 2025/2030 without
CO2 emission trading

Other related policies

U.S.

26% GHG emission reduction in
2025 relative to 2005

EU

40% GHG reduction relative to 1990

Same emission reduction target
CO2 intensity of power generation:
462[gCO2/kWh], & 27% renewables as those in Case 1 without CO2
in TPESemission trading
20% renewables in TPES

Japan 26% GHG reduction relative to 2013 Electricity share same as the energy(energy-related CO2 emissions:
927MtCO2)

China
India
Brazil

65% reduction of CO2/GDP relative
to 2005
35% reduction of GHG/GDP relative
to 2005
43% GHG reduction relative to 2005

mix of Japanese governmental
plan.(22% renewables, 26% coal,
20% nuclear)
20% renewable in TPES
40% non-fossil in power generation
45% renewables in TPESSouth 398-614 [MtCO2eq.] GHG emissions 
Africa
Russia 27.5% GHG reduction relative to
1990

12

National emission reduction
targets in Case 1 areaggregated globally, with
global CO2 emissions trading(%, relative to the baseline)

Changes in GDP

Impacts of the NDCs on GDP for the major countries in 2030
1.0%
0.5%
0.0%

Case1

-0.5%

Case2

-1.0%

Case3

-1.5%
-2.0%
-2.5%
US

EU

Japan

Source: estimated by a CGE type DEARS model developed by RITE

China

India

BrazilSouth
AfricaRussiaWorld
average- For the U.S., the decreases in GDP in Cases 1 and 2 are 1.6% and 0.8% relative to the baseline, respectively.
The estimated GDP loss in Case 1 is much higher than that in Case 2, mainly due to the constraints on carbon
intensities of the power sector assumed in the proposed CPP.
- For EU, the decreases in GDP in Cases 1 and 2 are almost the same, because the renewable target is cost
efficient for the 40% emission reduction target.
- For Japan, the decreases in GDP in Cases 1 and 2 are 1.9% and 0.7%, respectively. The energy mix of theJapanese governmental plan results in larger decreases in GDP and sectoral productions (the energy mix isdetermined not only by cost efficient emission reductions but also by energy security issues etc.).

13

Impacts on Trade in Chemical and Iron & steel sector in 2030
14

2.0%
Changes in(Net Import)/Production(%, relative to the baseline)

ChemicalIncrease in
Net Imports

1.0%
0.0%

Increase in
Net Exports

‐1.0%
‐2.0%

Case1

Case2

Case3

‐3.0%

US
Changes in(Net Import)/Production(%, relative to the baseline)

Iron & steel

EU

Japan

Rest of the regions

5.0%
4.0%

Case1

Case2

Case3

3.0%
2.0%

Increase in
Net Imports

1.0%
0.0%

Increase in
Net Exports

‐1.0%
‐2.0%

US

EU

Japan

Other regions

Source: estimated by a
CGE type DEARS model
developed by RITE

- The NDCs will make a large impacts on the potential international trade balances in Chemical sector in the US, EU andJapan, and in Iron & steel sector in Japan and EU. (Cases 1 and 2)- Under the global emission trade case (equal MACs), the impacts will be relatively small. (Case 3)

Image of standard scenario by models and real world scenarios
for deep cuts
Realistic world requirement:
Innovations stimulated & implemented

Model world:
Ordinary technology progress
Carbonprice

Carbon price/
Marginal abatement cost

Carbonprice

Baseline scenario
CO2emission

Implicit carbon price/
Marginal abatement cost

Baseline
scenario
CO2emissionIntervention scenario

15Intervention scenario

By technology andsocial innovations

Explicit high carbon prices of such as over 100$/tCO2 in real price are unlikely to be accepted in a real world.
Technology and social innovations resulting in low (implicit or explicit) carbon prices (including coordination of
secondary energy prices) are key for deep emission cuts to be implemented.

Innovations of end-use technologies and the induced social changes
16- Wide range of technological innovations andtheir integrations are required for improving ourwelfare and sustainable development.
- AI, IoT, big data etc. will be able to stimulate
such innovations.

Source: Gendai Business

Operation ratio of
automobiles is about 4%,
for example. The large
room for the improvement
exists by the achievementof complete autonomous
cars.
Source: IIASA

Source: FinlandCO2 marginal abatement cost in 2050 for 2 C goal under the middle energy
demand scenario vs low energy demand scenario with car- & ride-sharing
The global emissions in 2050: -40% compared to 2010
(corresponding to below 2 C in 2100 with 50% probability)

CO2 marginal abatement cost (MAC) in 2050
Middle energy demand
scenario: SSP2

Low energy demand
scenario: SSP1
with car- & ride-sharing

180 $/tCO2

100 $/tCO2

Unit: $/tCO2 (real price);
Equal marginal abatement costs among all nations are assumed.
SSP: Shared Socioeconomic Pathway
Source: estimated by RITE DNE21+ model

MAC in SSP1 with car- and ride-sharing assumptions is much smaller than in the standard scenario,
SSP2, due to achievement of low energy demands even in non-climate policies through the achievementof an economically efficient society induced by energy demand side innovations, such as IT, IoT and AI.

17

Conclusions

18

 Increasing trend of global CO2 emissions continues.
 In some developed nations, a relatively long decreasing trend of the emission can be observed,
but it was induced mainly by industrial structure change, and the consumption-based CO2

emissions were not reduced in most of the nations. High energy cost burden induced theleakage of industries. The international competitiveness issue is very important.
 The marginal abatement costs for the currently submitted NDCs are greatly different among
nations. Such large differences will hinder global efficiency of emission reductions andsustainable efforts of participating nation.
 Several social and political conditions hindering the least cost mitigation measures exist ineach nation. Cheaper emission reduction measures should be pursued, but some of therealistically unavoidable constraints should also be considered.
 According to the assessments for the macro economic impacts, some developing
nations/regions with almost zero marginal abatement costs will have positive impacts on GDP
and on outputs of some energy-intensive sectors as carbon leakages take place throughinternational trade. The coordination of the NDCs through the review process will be important.
 On the other hand, the coordination based on high carbon prices is unrealistic in the real world.
The opportunities for decreasing energy demand, particularly through further improvements in
IT, IoT or AI, will be expected for deep reductions with much lower mitigation costs.

Appendix

Introduction: International Competitiveness andthe Global Effectiveness of Climate Change Mitigation
 The historical trends in emissions in some developed nations where their emissions

have greatly reduced have been induced mainly by industrial structure change underglobalization.
 The Paris long-term goals, such as 2 C goal, require large global emission reductions.

According to many research studies, the emission gap between the aggregated NDCs
and the pathways for the goal is large.
 For sustainable measures of climate change mitigation, harmonized efforts of emission

reductions among all nations are the key.
 This presentation includes:

1) historical CO2 emission trends including consumption-based CO2 emissions andthe factor analyses2) measuring emission reduction efforts of Nationally Determined Contributions (NDCs)
and the assessments on international competitiveness3) In order to avoid the global ineffectiveness of climate change mitigation measures,the possible pathways of end-use innovations with green growth will be provided.

20

Employed indicators for measuring emissions
reduction efforts

More advanced metrics

Advanced metrics

Simple metrics

Indicators for emissions reduction efforts
Emissions reduction ratio
from base year (only forOECD countries or Annex I
countries)

Compared to 2005

Compared to 2012(or 2010)

21

Framework

Notes

When baseline emissions are expected to stagnate, it
is more relevant to simply compare the projected
reduction rates (all the more since there areuncertainties regarding the BAU). This is why we use
the reduction ratio compared to BAU for OECD
countries only - on the other hand, such an approach
would be irrelevant for countries where emissions are
expected to grow substantially.

Most countries use 2005 as their base year (as amatter of fact, 1990 seems too far in the past to
be used as a base year to evaluate the emissions
reduction effort for upcoming emissions)
Adopting a recent base year may enableappropriate comparison of future efforts.

Emissions per capita (onlyfor non-OECD countries or
non-Annex I countries)

Absolute value

For non-OECD countries, we adopt the absolute valueof emissions per capita instead of the reduction ratio
from base year.

As this indicator (absolute value) is very
dependent on country’s situations such as
economic development stage, industry structure,climate etc., not appropriate to measure
reduction efforts.

CO2 intensity (GHG
emissions per GDP)

Absolute value

Reveals what level of CO2 emissions corresponds towhat degree of economic activity

It can easily reach bad values for countries with alow GDP; it is also highly dependent on the
country’s industry structure.

Improvement rate(compared to 2012or 2010)It will be better to measure emission reduction efforts
because the bias due to differences in economic
growth rate can be removed compared with theindicator of emission reduction ratio from base year.

The value may change greatly for low GDP
countries with high GDP growth rate.

Emissions reduction ratio
compared to BAU

The differences in economic growth etc. can becancelled.

Efforts already made in the past for energy
saving etc. are neglected and future abatement
potential as well.

CO2 marginal abatement
cost (carbon price)

This is a particularly relevant indicator to assess
reduction efforts as it contains countries’ differences interms of economic growth, energy savings efforts,
abatement potential of renewables.

Past efforts made for energy saving etc. may leadto high marginal abatement costs for additional
reduction efforts.

While marginal abatement costs reflect the frontiereffort, this indicator corresponds to the efforts made in
the baseline as a whole.

Market data is available for ex-post evaluation,
but for ex-ante evaluation, only model-based
estimates are available which makes
uncertainties rather high.

This indicator corresponds to the economy’s capability
to bear efforts for the whole reduction.

Uncertainties are high as this is a model-based
estimation.

Retail prices of energy(electricity, city gas,
gasoline, diesel)
Emission reduction costs
per GDP

Employing historical
data of 2012 or 2010
for weighted
average

Region divisions of DNE21+

22

Population prospects (millions)

23

2010

2020

2030

Japan

127

124

118

United States

312

340

364

EU28

507

515

515

Switzerland

8

8

8

Norway566Australia

22

25

27

New Zealand

4

5

5

Canada34

37

40

Russia

144

139

132

China

1367

1445

1477

Korea

48

49

49

Mexico

118

128

135

Ukraine

46

44

41

Belarus998

Kazakhstan

16

17

17

East Europe (Non-EU countries)

23

23

22

Thailand

66

70

72

India

1206

1357

1474

Turkey728086South Africa

51

54

56

The World Total

6916

7679

8308

Source) RITE estimates based on the 2008 UN population prospects in the medium variants. For statistical values up to 2010, The UN World Population Prospects 2012 are used.

GDP Prospects (MER, %/yr)

24

2010―2020

2020－2030

Japan

1.4

1.9

United States

2.6

2.0

EU28

1.2

1.3

Switzerland

1.4

1.2

Norway1.8

1.6

Australia

2.7

1.8

New Zealand

2.4

1.6

Canada2.1

1.7

Russia

4.3

6.3

China

7.7

5.6

Korea

3.0

1.9

Mexico3.2

3.0

Ukraine

3.2

5.3

Belarus3.2

3.4

Kazakhstan

5.4

5.0

East Europe (Non-EU countries)

2.2

3.8

Thailand

4.3

4.0

India

6.5

5.9

Turkey4.0

2.8South Africa

2.5

3.4

The World Average

3.0

2.9

Source) RITE estimates. Our estimates are not so different form USDOE/EIA International Energy Outlook and IEA World Energy Outlook. (In consideration of the differences between PPP and MER)Evaluated INDCs (1/2)
The 119 INDCs submitted as of October 1st, 2015 were evaluated.
As of October 1st, 2015, 119 INDCs had been submitted, and representing
about 88 per cent of global emissions in 2010.
Comprehensive evaluations of emission reduction efforts were only for 20
countries (see below) due to the limited regional resolution of the model.
2020 (Cancun Agreements)

Post-2020 (INDCs)

United States-17% compared to 2005-26% to -28% by 2025 compared to 2005

Canada-17% compared to 2005-30% by 2030 compared to 2005

EU28

-20% compared to 1990-40% by 2030 compared to 1990

Switzerland-20% compared to 1990

-50% by 2030 compared to 1990（-35% by 2025 compared to 1990）

Norway-30% compared to 1990-40% by 2030 compared to 1990Japan

-3.8% compared to 2005*-26% by 2030 compared to 2013

Australia-5% compared to 2000

-26% to -28% by 2030 compared to 2005

New Zealand-5% compared to 1990-30% by 2030 compared to 2005

Russia-15 to -25% compared to 1990-25% to -30% by 2030 compared to 1990

Note: More ambitious emission reduction targets had been submitted as “conditional “ targets from some countries,
but they are not included in this table.
* Emission reduction target assuming zero nuclear power25

Evaluated INDCs（2/2）
2020 (Cancun Agreements)

26

Post-2020 (INDCs)

Non-EU Eastern
Europe―-19% by 2030 compared to 1990*

Ukraine-20% compared to 1990-40% by 2030 compared to BAU

Belarus-5 to -10% compared to 1990-28% by 2030 compared to 1990

Kazakhstan-15% compared to 1992-15% by 2030 compared to 1990

Turkey―-21% by 2030 compared to BAU

Korea-30% compared to BAU-37% by 2030 compared to BAU

Mexico-30% compared to BAU-25% by 2030 compared to BAU**(-22% by 2030 compared to BAU in GHG)

South Africa-34% compared to BAU

614MtCO2eq/yr by 2030

Thailand-7 to -20% compared to BAU(Energy and transportation sectors)-20% by 2030 compared to BAU

China

To reduce CO2/GDP by-40 to -45% compared to 2005

To reduce CO2/GDP by -60 to -65% by2030 compared to 2005 (To achieve thepeaking of CO2 emissions around 2030
and making best efforts to peak early)

India

To reduce GHG/GDP by-20 to -25% compared to 2005

To reduce GHG/GDP by -33 to -35% by2030 compared to 2005

* The reduction rate was estimated from the total emissions by the INDCs of Albania, Makedonia, Moldova, and Serbia.
** Emission reduction target of Mexico includes black carbon.

Notes of the assessments of INDCs in this study

27



LULUCF emissions are not taken into account for international comparison of mitigation
efforts, because they have large uncertainty and their appropriate evaluation is difficult.
(LULUCF emissions are taken into account for the aggregated INDCs evaluation withrespect to 2C target.)For the countries with emission reduction targets compared to the base year, the emissions
in the target year are calculated based on historical emissions excluding LULUCF. Historicalemissions are derived from Greenhouse Gas Inventory Office of Japan for Japan, UNFCCC
for other Annex I countries, and IEA for other countries.



For the countries with emission intensity improvements targets, the emissions in the target
year are calculated based on historical emissions and our GDP scenario.

For the countries with emission reduction ratio targets to BAU, if BAU emissions in target
year are stated in their INDCs, the values of INDCs are adopted for calculation of emissions
in the target year. If not, their INDCs are not evaluated in the international comparison of
mitigation efforts in this study. (For the aggregated INDCs evaluation with respect to 2Ctarget, their carbon prices are assumed to be zero until 2030.)



Other countries with policies and actions targets are omitted from this assessment.



Most of the countries set 2030 as the target year, but the United States and Brazil chose
2025. For these countries, indicators concerning emission reduction efforts in 2025 areevaluated and compared with the other countries’ indicators in 2030.



Evaluation of all of the adopted indicators was carried out for twenty regions.



For Brazil and Indonesia who are large emitters from LULUCF, only the three indicators(emission reductions compared to base year, emissions per capita, and emissions per GDP)are evaluated including LULUCF.

GHG emissions per capita of NDCs

28

Note) The lower range of emission targets are shown for the countries submitting their INDCs with ranges.
Source) estimate by RITE

GHG intensity of GDP (MER)29

The standings of current
GHG intensity amongmajor nations will keep
toward 2030 under thepledged NDCs.

Note) The lower range of emission
targets are shown for the countries
submitting their NDCs with ranges.
Source) estimated by RITEThe drafted energy mix of Japan for 2030
Energy Demand

361 million kL

Economic
growth
1.7%/year

Primary Energy Supply

Attempted energy
savings: a huge amount ofappr. 50.3 million kL(-13% compared to the casewithout energy savings)

489 million kL

Renewables:
appr. 13-14%

Nuclear: appr. 10-11%

Selfsufficiency
around 24.3%
improvement

2013: 6%
Electricity
appr. 25%

Final energy
consumption around
326 million kL

Gas: appr. 18%
Electricity
appr. 28%

Coal: appr. 25%
Heat
Gasoline
Town gas
appr. 75%

FY 2013(historical data)

Heat
Gasoline
Town gas
appr. 72%

FY 2030 (after
energy savings)

Oil: appr. 32%

FY 2030

30

The energy mix target in 2030
– The composition of the power generation mix –Electricity Demand

Breakdown ofelectricity generation

Attempted energy savings: a(Total power generation)huge amount ofappr. 1278 TWhappr. 196.1 TWh(-17% compared to the case(Total power generation)
Economic
without energy savings)
Geothermal
Energy savings
growthappr. 1.0- 1.1%
Transmission andappr. 1065 TWhappr. 17%
distribution losses
1.7%/year
Biomass appr.
Energy3.7-4.6%
savings +Wind 1.7%
Renewables:
Renewables:
renewable
Solarappr. 22-24%
appr. 19-20%
energies:
appr. 7%around 40%

Electricity
966.6
TWh

FY 2013(historical data)

Electricity
appr.
980.8
TWh

FY 2030

Nuclear:appr. 17-18%

Nuclear:appr. 20-22%

LNG:
appr. 22%

LNG:
appr. 27%

Coal:
appr. 25%

Coal:
appr. 26%

Oil: appr. 2%

FY 2030

Hydropower
appr. 8.89.2%

Oil: appr. 3%

In the standard case without energy savings, the GDP elasticity of electricity demand is 0.68. This elasticity is
consistent with the one assessed in the RITE analysis, which is around 0.8 for the 2013-2020 period, and 0.6 for 20202030, and also consistent with that of the ‘Current Policies’ scenario in IEA WEO2014. As a result, the estimate by thegovernment seems a reasonable one. However, in the energy savings case, a significant reduction of electricity
demand (17%) is assumed (the elasticity then being 0.05), this point will be further examined in our analysis.

312030 Emission Target of Japan’s NDC

32

Since GHG emissions are strongly dependent on energy mix issues, policy making andtechnology development for post-2020 targets need to take careful consideration oftechnology availability and costs in order to set achievable goals. Based on this, theJapanese NDCs commit to reduce emission levels in 2030 by 26% compared to 2013
(which corresponds to 25.4% compared to 2005), including CO2 sink (GHG emissions in
2030 would be about 1,042 million tCO2 in total).
2030; Compared to 2013（compared to 2005）
Energy-related CO2-21.9% （-20.9%）

Other GHGs

-1.5% （-1.8%）

LULUCF-2.6% （-2.6%）

Total GHGs

-26.0% （-25.4%）
2005

2013

2030

Industry

457

429

401

Commercial and other

239

279

168

Residential

180

201

122

Transport

240

225

163

Energy conversion

104

101

73

1219

1235

927

Energy-related CO2 Total

Unit: Mt-CO2

Marginal abatement costs estimations across models(RITE DNE21+, FEEM WITCH and NIES AIM)
Only energy-related CO2
emission reductions in 2030

250
200
The average between 2025 and 2030 for GHG emission recutions
150
100

USG Social Cost of
Carbon (SCC):
53$/tCO2 for 2025-30

50

Japan

U.S.

EU

China

India

WITCH

DNE21+WITCH

DNE21+WITCH

DNE21+WITCH

DNE21+WITCH

DNE21+

AIM/Enduse

0
DNE21+

CO2 marginal abatement cost (US$2005/tCO2)

300

33

Marginal abatement
costs if theaggregated NDCs areachieved most costefficiently:
16$/tCO2 by WITCH,
6$/tCO2 by DNE21+

Korea/S.Africa/Australia

Source: B. Pizer, J. Aldy, R. Kopp, K. Akimoto, F. Sano, M. Tavoni, COP21 side-event; MILES project report for Japan- The marginal abatement costs vary across models for some countries, but can be comparable for many
countries/regions.
- The CO2 marginal abatement costs of the NDCs of OECD countries are much higher than the marginal
cost for the case that the total reductions are achieved most cost-efficiently (globally uniform marginal
abatement cost).

Per-GDP emission reduction costs of NDCs
Australia
UkraineThailand
New Zealand
East
Europe(Non-EU
countries)
East
Europe(Non-EU…
Switzerland
Korea
EU28
Japan
CanadaUnited States
South Af rica
MexicoBelarus
0.0
Russia0.0
Turkey
0.0
India
0.0
Kazakhstan
0.0
China
0.0
Norway
0

34

2.4
1.8

bigger

1.4
1.1
1.0
1.0
0.8
0.8
0.6
0.5
0.4
0.3
0.3
0.2

smaller
0.5

11.5
Emission reduction cost per GDP (%)

2* The average values are shown for the countries submitted the INDC with the upper and lower ranges.

2.5Expected global GHG emissions of the aggregated NDCs andthe corresponding emission pathways up to 2100 toward +2 C goal
GHG emissions (GtCO2-eq./yr)

200

150

35

Source) Estimate by RITE
About 70$/tCO2 in 2050 evenunder the least cost measures

About 6$/tCO2 in 2030 under the least
cost measures for the achievement of the
expected global emission reductions by INDCs

Baseline emissions reported
in the IPCC AR5

It is important to induce the achievements of
INDCs and further emission reductions forcountries having room for more reductions
through PDCA (plan-do-check-act) cycle.

100

It is important to seek deeper emission
reductions through developments anddeployments of innovative technologies.

50

around +2.5 to 3ºC
around +2 to 2.5ºC
below +2ºC

About 320$/tCO2 in 2050 evenunder the least cost measures
0
1990

2000

2010

2020

2030

2040

2050

2060

Historical emissions
Emission outlook under current policies

2070

2080

2090

2100

Consistent with the INDC, but also needdeeper emission reductions after 2030

+2.5 ºC stabilization under climate sensitivity of 2.5 ºC (around +2.6 ºC in 2100 and +3.0 ºC in 2200 under C.S. of 3.0 ºC）+2 ºC stabilization under climate sensitivity of 2.5 ºC; temporary overshoot of 580 ppm (+2.5 ºC stabilization under C.S. of 3.0 ºC)
Below +2 ºC in 2100 under climate sensitivity of 3.0 ºC; temporary overshoot of 530 ppm+2 ºC stabilization under climate sensitivity of 3.0 ºC; temporary overshoot of 500 ppm and around 450 ppm in 2300
INDC submitted by October 1 (119 countries) assumed to be implemented- The expected global GHG emission in 2030 is about 59.5 GtCO2eq. when all the submitted INDCs are successfully achieved.
Emissions reductions from the baseline are estimated to be about 6.4 GtCO2eq, in which about 0.5 GtCO2eq reductions areoffset due to carbon leakages from nations with NDCs of high marginal abatement costs to those with zero or low costs
through induced lower fossil fuel prices.
- The expected temperature change in 2100 is +2 to +3 C from preindustrial levels. The range depends on the uncertainties ofclimate sensitivity, and more so on the effects of future developments and deployments of innovative technologies.

A technology-oriented climatechange mitigation model:
DNE21+

Overview of Tech.-oriented Assessment Model: DNE21+37

 Intertemporal 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 demand sides






(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: instantaneouspeak, 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 about 6 million decision variables.

- The model has detailed information in regions and technologies.
- Consistent analyses among regions and sectors can be conducted.

Technology Descriptions in DNE21+
38

Fossil fuels
Coal (coal, brown coal)
Oil (conventional, unconv.)
Gas (conventional, unconv.)

Unit
production
costEnergy conv.
processes(oil refinery, coalgasification, bioethanol, gas
reforming, waterelectrolysis etc.)

Cumulative production

CementPaper & pulp
Chemical (ethylene, propylene,ammonia)

Aluminum
Solid, liquid and gaseous fuels, andelectricity <Top-down modeling>

Renewable energies
Hydro power & geothermalWind power (on-, off-shore)
Photovoltaics
Biomass (plantation, waste)

Industry
Iron & steel

ElectricPower
generation

Unit
supplycost

Transport
vehicleSolid, liquid and gaseous fuels, andelectricity <Top-down modeling>

Residential & commercial
Annual production

Nuclear power

CCS

Refrigerator, TV, air conditioner
etc.
Solid, liquid and gaseous fuels, andelectricity <Top-down modeling>Comparison of energy efficiencies
in major energy sectors (1/2)
43

Japan

41

Germany

39

Coal power generation

US

37

Efficiency(%)

39

Source: RITE, 2014 (estimation based on
IEA data, 2013)

China

35

Korea

33

Russia

31

India

29
EU (27)
27
World
25
1990

1995

2000

2005

2010
60
Spain

Gas power generation

55

Source: RITE, 2014 (estimation based on IEA data, 2013)

Mexico50

Efficiency (%)

The differences in existing
facilities with different levelsof energy efficiency bytechnology including theirvintages are explicitly
considered in the model.

UK

Italy
45

US
Japan

40

Iran
35

RussiaEU (27)

30

World
25
1990

1995

2000

2005

2010

Comparison of energy efficiencies
in major energy sectors (2/2)
Primary energy consumption of BOF steel(GJ/ton of crude steel)

35

2010

30

28.3
25.9 25.7

25

34.5

33.1

2005

23.1

22.9

26.8

27.7 27.7

28.1 28.2

28.9

28.3

29.1

40

33.2
31.1

30.0 30.3

Iron andsteel(BOF
steel)

28.3

23.5 23.8

20

15
Source: Oda et al. 2012;
RITE, 2012

10
Japan

Korea

Germany

China

FranceUK

India

BrazilUS

RussiaUkraineCement(Clinker)

Source: RITE estimatebased on WBCSD/CSI
data

Heat consumption of clinker production(GJ/t-clinker)

6

2005

5.4

2010

5.2

5
3.7 3.84
3.1 3.3

3.3 3.3

Japan

India

4.0

4.2

3.9

3.8

4.0

4.2

4.0

3.5

3
2
1
0
Germany

UK

FranceUS

China

RussiaA CGE-type energy-economic
model: DEARS

Overview of Global Energy-Economic Model: DEARS*(Dynamic Energy-economic Analysis model with multi-Regions and multi-Sectors)

42



Integration model of top-down-typed economic module and bottom-up-typed
energy systems module

Dynamic non-linear optimization model (Maximization of global consumption utility)

Evaluation time period: up to middle of this century (10 years steps)World divided into 18 regionsNon-energy sectors: 18 sectors

Energy: 8 types of primary energy and 4 types of secondary energyEconomic module that represents international economic structures based on
input-output tables of GTAP (Global Trade Analysis Project) database.



Simplified energy systems module Bottom-up modeling for technologies in energy supply (e.g. , power generation) and CCS(carbon capture and storage)
 Primary energy (8 types): coal, crude oil, natural gas, hydro & geothermal, wind,photovoltaics, nuclear and others
 Top-down modeling for energy demand (price and income elasticities of demand forenergy and income, linked to economic module) Final energy (4 types): solid, liquid and gaseous fuels and electricity* T. Homma & K. Akimoto,” Analysis of Japan's energy and environment strategy after the Fukushima
nuclear plant accident “, Energy Policy 62 (2013) 1216–1225

Regions and Industries in DEARS
 18 regions
Other Europe
Canada

EU(W)

US

EU(E)

RussiaOther Europe and FSU

Middle-East and North Africa
Sub-Sahara Africa

South Asia

China

Japan

India
East andSouth-Eastern Asia

BrazilLatin America

Australia and New ZealandSouth Africa

 18 non-energy sectors

energy-intensive
sectors

43

DEARS Model Details
 Objective function（Utility maximization consumption）

  d t  L r    i,r , t  logtr

C i,r,tL r,ti max .

Ci,r,t : consumption amount in period t, region r,

sector i (endogenous)
L r , t : population in period t, region r (exogenous)d : discount factor in period t (exogenous)ti,r,t: consumption-utility weights in period t
region r and sector i (exogenous) Capital accumulation function

K r , t  (1  dep r , t )K r , t 1   I r ,i , tiI r ,i , t : investment amount in period t, region r, sector i(endogenous)
K r , t : capital stock in period t, region r (endogenous)=
depr , t: Depreciation rate of capital in period t, region r(exogenous)

 Modeling the production in non-energy sector
The model has a structure that goods are produced and exported only in the regions where
production of goods is efficient, assuming the production function in the inter-industry structure
under the consumption utility maximization. However, taking into account that products andconsumption of agriculture and food are different in nature from those of industry and services,
products and consumption of agriculture and food are modeled, using the food production anddemand scenario as a constraint so that the variation would be reduced.

44

DEARS Model: Inter-industry-Integration of energy and non-energy sectors1
Intermediate input

Limit
CO2

Coal
Crude OilNatural Gas
Others(Nuclear, Hydro etc.)

Bottom-up
energysystemmodel

International Trade(Primary Energy Sector)

1
2
Sectors・
・
・
N
(=18)

2

・・・

International Capital
Trade
Stock

Final
Consumption

Intermediate demand45(previous time)N・・・

Investment

・・・

・・・

・
・
・

・・・

Solid Fuel・・・

Liquid Fuel・・・

Gaseous Fuel・・・

Electricity

・・・

Industrial structure
in base year

Capital, Labor

Energy consumption

Income elasticity ofenergy demand

CapitalStock