IPCC SRES
4 qualitative storylines
6 IA model frameworks
40 Scenarios
6 Illustrative Scenarios
• representative of uncertainty range
• no single, best guess scenario
• probabilities/likelihoods not
assigned
Impact of technological change of
similar importance as population
and economic growth combined
IIASA
A. Grübler, 2002
MAJOR CLIMATE CHANGE UNCERTAINTIES
Socioeconomic (Future Cumulative Emissions SRES Scenarios)
Carbon Cycle (Resulting CO2 Concentration) and
Climate Sensitivity (ºC for 2CO2)
0
°C global mean temperature change
2
3
4
2.5
1
5
6
7
ppm CO2
300
600
1000
6
4
2
0
<100
1000
2000
3000
SRES scenarios cumulative emissions 1900 - 2100, GtC
Vulnerability:
low
high
Emissions from 130,000 Scenarios of Technological Uncertainty
5.5
Relative frequency (percent)
5.0
Set of 520
technology dynamics
4.5
4.0
3.5
3.0
2.5
2.0
Optimal set of 53
technology dynamics
1.5
1.0
0.5
0.0
5
10
15
20
25
30
Emissions by 2100, GtC
Gritsevskyi&Nakicenovic, 2000
Energy Policy 38:907-921
Endogenous Technological Change
• Future characteristics (e.g. costs) depend on
intervening actions (R&D & investments)
• Improvements through accumulation of
experience (learning)
• Interactive rather than linear model
(learning by doing and using;
supply push and demand pull)
• Uncertainty 1: outcomes of R&D and
investment strategies (learning)
• Uncertainty 2: market environment (demand,
environmental constraints, etc.)
IIASA
A. Grübler, 2002
Technological Uncertainties:
Learning rates (push) and market growth (pull)
1.5
1.5
0.1%
Cost index ($/kW)
Nuclear Reactors France 1977-2000
1.0
1.0
50% interval
90% interval
mean learning rate
(115 case studies):
-20% per doubling
0.5
0.5
PVs Japan
1976-1995
0.1%
0.0
0
1
2
3
4
0.0
5
6
7
8
9
10
11
12
13
14
15
Number of doublings (installed capacity)
IIASA
A. Grübler, 2002
Implementation
• Integration of stochastic
draws
• Objective function incl. “risk
term” (Y. Ermoliev)
• Non-convex, stochastic
optimization (A. Gritsevskii)
• parallel computing (IIASA
network to CRAY-T3E)
• Result: optimal diversification
portfolio
IIASA
A. Grübler, 2002
Global Carbon Emissions: Four Models of
Increasing Treatment of Uncertainty
global carbon emissions, GtC
25
20
15
10
(1) no uncertainty
5
(2) uncertain demand, resources, costs
(3) = (2) plus uncertain C-tax
(4) uncertain demand, resources, techn. learning, C-tax
0
1990
IIASA
2010
2030
2050
2070
2090
A. Grübler, 2002
Summary
Endogenous technological change through
anticipation of (uncertain) increasing returns
Non-convex, stochastic optimization problem
solvable (within limits)
Interpretation: Innovation and niche market
development (exploration of learning potentials)
economically rational in view of pervasive
uncertainties
Info: http://www.iiasa.ac.at/Research/TNT/WEB/index.htm
IIASA
A. Grübler, 2002