Alternative Paths Toward Stabilization
Some Challenges for New Scenarios
Nebojša Nakićenović
International Institute for Applied Systems Analysis (IIASA)
and Vienna University of Technology (VUT)
naki@iiasa.ac.atWorkshop on GHG Stabilization Scenarios, Sponsored by NIES and EMF
Tsukuba, Japan on January 22-23, 2004

Stabilization Scenarios“Stylized Facts”
z Path-dependence of stabilization vs baselinez Baseline more important than stabilizationz Uncertainty – probabilistic or distributionsz Stabilization reduces emissions uncertainties
z Consequences of lower economic growthz Ancillary and avoided adaptation benefits
z Spatially explicit drivers, mitigation & impacts
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Our dear friend andcolleague is gone,
leaving a void in ourcommunity and in ourlives that can never befilled...
He will live forever inour hearts, in ourthoughts and in ourscientific work ‒ andwe’ll continue to misshim every day!

Carbon Dioxide Emissions

95%

475%
Median21990 range
25%
01900

Nakicenovic

5%
1950

2000

2050

Non-intervention

6

Non-classified

8Intervention

Global Carbon Dioxide Emissions(index, 1990=1)

102100IPCC 2001

Global Carbon Dioxide E missions
40 SRE S Scenarios and Literature Range
Total database range

8A1Median
20
1900

1990 range(all scenarios)

B2

B1
5%

Minimum in Database
1950

#9al.
Nakicenovic et

2000

2050

Intervention

4

Non-classified

A2

2100

SRE S 2000
IIASA&TUW
2004

Non-intervention

95%

Maximum inDatabase

6

IS92 range

Global Carbon Dioxide Emissions
Energy and Industry(index, 1990=1)

10

Carbon Emissions: Scenarios andStabilization Profiles
35 Gt in 2100
A1FI (A1C & A1G)
2520

S450 Stabilization
S550 at 450, 550, 650
S650 ppmv CO2
WGI trajectory
WRE

A2

A1B
B2GtC

15

10S650501800

Nakicenovic et al.

A1T
B1

1900

2000

2100

S550
S450
2200

SRES 2000

Carbon Emissions: Scenarios andStabilization Profiles
35 Gt in 2100
A1FI (A1C & A1G)
25

20

S450 Stabilization
S550 at 450, 550, 650
S650 ppmv CO2
WGI trajectory
WRE
A1B

GtC

15

10S650501800

Nakicenovic et al.

A1T

1900

2000

2100

S550
S450
2200

SRES 2000

Global Carbon Dioxide Emissions

Nakicenovic et. al

IPCC 2001

Global Carbon Dioxide Emissions

Nakicenovic et. al

IPCC 2001

Global Carbon Dioxide Emissions

Nakicenovic et. al

IPCC 2001

Global Carbon Dioxide Emissions

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Global Carbon Dioxide Emissions

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Emissions Mitigation Technologies
Demand Reduction

Carbon Dioxide Emissions [GtC]

30
Fuel switching(mainly shifts away
A2

from coal)

20
scrubbing andremoval - synthetic
fuels production

scrubbing and10

removal - power
sector (natural gas)
A2-550s

scrubbing andremoval - power

01990

Nakicenovic

2010

2030

2050

2070

2090

sector (coal)

Source: K. Riahi

ENERGY SYSTEMS COSTS OF ALTERNATIVEBASELINES AND STABILIZATION SCENARIOS

Cumulative Discounted System
Costs (1990-2100),[trillion US$]

1400
450ppmv CO2 stabilization
1200
550ppmv
650ppmv 750ppmv

1000
450ppmv
450ppmv

800

550ppmv
750ppmv A1G
550ppmv

A1B

450ppmv
600

A1C

550ppmv
Baselines

A1T
400
0500

1000

1500

2000

2500

Cumulative CO2 Emissions [GtC]

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Global Carbon Dioxide E missions
40 SRE S Scenarios and Literature Range
Total database range

8A1
Median20
1900

1990 range(all scenarios)

B2

B1
5%

Minimum in Database
1950

#19
Nakicenovic etal.

2000

2050

Intervention

4

Non-classified

A2

2100

SRE S 2000
IIASA&TUW
2004

Non-intervention

95%

Maximum inDatabase

6

IS92 range

Global Carbon Dioxide Emissions
Energy and Industry(index, 1990=1)

10

E nergy-Related Carbon Dioxide E missions
Global Carbon Dioxide Emissions (GtC)

N = 40 Scenarios
35

36-39

2100

33-36

Sample = 190
Median = 15.4

30-33
30
25

27-30

IS92e

24-27

IS92f

21-24
20

18-21

IS92a,b15-18
1510Median12-15
9-126-95IS92d3-6IS92c0-301990 2010 2030 2050 2070 20900510152025Relative Frequency (%)#20Nakicenovic etal.

SRE S 2000
IIASA&TUW
2004

MAJOR CLIMATE CHANGE UNCERTAINTIESCumulative CO2 of IPCC SRES scenarios and resulting CO2 concentrations
and climate sensitivity in oC temperature change based on MAGICC model0

Global mean surface temperature change oC
23452.5

167

Atmospheric concentrations in ppm CO2300

600

1000

6420
<1001000

2000

3000

IPCC SRES scenarios cumulative emissions 1900 – 2100 in GtC

Vulnerability:
GrüblerNakicenovic#21lowhighIIASA2004
2002
IIASA&TUWDistribution of Cumulative Carbon Emissions
Across the Range of SRES Scenarios
6GDP POPULATION
Veryhigh-High Medium
Medium-Low
TECHNOLOGYHighHigh Medium
Medium-Low
Low
5

Number

4

32

1

0500

1000

Nakicenovic #&22Slentoe
Nakicenovic

1500

2000

2500

Gt C

IIASA 2004
2003
IIASA&TUWDistribution of Cumulative Carbon Emissions
Across the Range of Post - SRES Scenarios
20GDP

Very
high-High Medium
HighHigh Medium
Medium-Low
Low
TECHNOLOGYPOPULATION1816

Number14121086420500

1000

Nakicenovic #&23Slentoe
Nakicenovic

1500

2000

2500

Gt C

IIASA 2004
2003
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ECHAM4

Climate Change
Impacts on
Cereal Production
Potential of
Food Insecure
Countries
2080s

HadCM2CGCM1Fischer et al.,IIASA, 2001

ECHAM4

Climate Change
Impacts on
Cereal Production
Potential of
Food Insecure
Countries
2080s

HadCM2CGCM1Fischer et al.,IIASA, 2001

Population
Density

Nakicenovic#26
Grübler& Prieler

IIASA&TUWIIASA
20042001

Electricity Use

Nakicenovic#27
Grübler& Prieler

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20042001

Electricity Use &Population Density
Nakicenovic#28
Grübler& Prieler

IIASA&TUWIIASA
20042001

1900, actual data

North-East of the USA

1990, actual data

1990, proportional scaling (CIESIN)

1900, actual data

North-East of the USA

2000, actual data

2000, proportional scaling (CIESIN)

2000

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2070

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Per Capita Income Across SRES Scenarios
Industrialized
Developing
countries
countries(Annex-I)(non-Annex-I)
100

1000 US$(90) per capita

1.680

60

1.840

Income ratio:

20

PPP: 44.2

3.0

MER: 1601990

NakicenovicNakicenovicet#al.
33

A2

B2

B1

A1

IIASA 2000
IIASA&TUW
2004

Per Capita Income Across SRES Scenarios
Industrialized
Developingcountries
countries(Annex-I)(non-Annex-I)
100

1000 US$(90) per capita

1.680

60

40

1.8

Income ratio:

4.2

3.0
7.6

20

PPP: 4
MER: 1601990

A2

Nakicenovic
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34

B2

B1

A1

P1

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2004

Carbon Emissions: Scenarios
and Stabilization Profiles
35 Gt in 2100
A1FI (A1C & A1G)
25

20S450 Stabilization
S550 at 450, 550, 650
S650 ppmv CO2
WGI trajectory
WRE

A2A1B
B2
P1GtC

1510S650
501800

Nakicenovic #35

A1T
B1

1900

2000

2100

S550
S450
2200

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Potential Hydrogen Market by 2020
34 IIASA-WEC and IIASA-IPCC Scenarios& Mitigation Scenarios
Baseline...........................
90
Other H2 (mitigation)
80
Other H2 (baseline)

Number of Scenarios

70
60

Coal (mitigation)

50

Coal (baseline)40

Gas (mitigation)30

Gas (baseline)

20

Nuclear (mitigation)

10
Biomass (mitigation)

00.1-2

2-4

4-6

Nakicenovic #36

6-8

8-10 10-12 12-14 14-16 16-18
H2 Production, EJ>18Biomass (baselines)

IIASA&TUW 2004

DYNAMICS OF TECHNOLOGYz Deep Uncertainty:

Limited knowledge on feasibility and costsof future technologiesz Technological Learning:

Improvements are a function of
accumulated experience (learning curve)
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Japan - PV Costs vs. Expenditures
100,000

PV costs (1985) Yen per W

1973: 30,000
1976: 16,300
10,0001980: 4,900
y = 10 4.0 –0.54x
R 2 = 0.989
1985: 1,200
1,000
1995: 640

Basic R&D

Applied R&D

Investment

Data source:
Watanabe, 1995 &1997

1000Grübler #380.1

110100Cumulative expenditures, billion (1985) Yen

1,000IIASA 2002

Fuel Cell Marketing Strategy
Successive Market Niches via Cost Reductions

Source: P. B. Bos, Commercializing Fuel Cells – Managing Risks,Fourth Grove Fuel Cell Symposium, Commonwealth Institute London, September 19-22, 1995

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Technological Uncertainties
Learning rates (push) and market growth (pull)
1.5

1.5
0.1%

Co st in d ex ($/kW )Nuclear Reactors France 1977-2000

1.0

1.050% interval
90% intervalmean learning rate(115 case studies):-20% per doubling0.50.5PVs Japan
1976-1995
0.1%

0.0012340.0
5678

9101112131415

Number of doublings (installed capacity)

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Learning Potentials
Number of Units Sold to Date
9Automobiles
Electric cars

>1<110410Intel chips
PV cells>1<1105
10Gas turbines
Wind turbines<1>1

Nuclear reactors<110

Nakicenovic #41∆=x100,000
Fuel Cells

8

Factor 1,000
Difference!

10410

6

Factor 100
Difference!

3Negative
Learning
Possibility

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Why Are Increasing Returns,Uncertainty and Risk Important?
z Very long-term processesz Substantial effect on economic developmentz Path dependency and technological “lock in”
z Highly uncertain characteristics andeconomic performance of future technologies
z Potentially high impact on global, regional

and local environment
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Stabilization Scenarios“Stylized Facts”
z Path-dependence of stabilization vs baselinez Baseline more important than stabilizationz Uncertainty – probabilistic or distributionsz Stabilization reduces emissions uncertainties
z Consequences of lower economic growthz Ancillary and avoided adaptation benefits
z Spatially explicit drivers, mitigation & impacts
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Next Steps on Urgent Issues• Limitations of downscaling need to be considered(need for scaling methods; other to proportional)• Emissions modeling community could be asked toinclude all GHGs and particulates in multigases
baseline scenarios• Role of additional GHGs and particulates to beconsidered in stabilization scenarios (e.g. burdensharing; uncertainties)

INTERGOVERNMENTAL PANEL ON CLIMATE CHANGE (IPCC)

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http://www.iiasa.ac.at/Research/TNT/index.html
index.htm lnaki@ iiasa.ac.atNakicenovic #45

IIASA 2003
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