Analytical Frameworks and Uncertainty
Workshop: R&D Portfolio Analysis Tools
and Methodologies
December 2-3, 2010
Enrica De Cian
Fondazione Eni Enrico Mattei (FEEM)
Policy question and Research Challenge
The availability of advanced technologies
• reduce the costs of climate change policy
• enlarge the set of feasible stabilization targets
Supporting those technologies is fundamental, but how?
THE CHALLENGE
Develop a decision framework to guide policy makers
when deciding on energy R&D portfolios, while
accounting for the uncertain outcome of R&D
investments
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Our Modeling Research: The ICARUS Project
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SUPPLY OF TECHNOLOGIES
DATA ANALYSIS+
EXPERT ELICITATIONS
VALUE OF TECHNOLOGIES
IAMs
FRAMEWORK
FOR
R&D PORTFOLIO ASSESSMENT
A decision-support tool
based on stochastic IA modeling
approach
NORMATIVE ANALYSIS
Optimal portfolio of energy RD&D
Design of effective climate and innovation policies
Role of policies helping diffusion of technologies
Evaluate economy-wide benefits of R&D projects
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Our Solution: Probabilistic Technology Cost Scenario
Today 2030 or 2050
Each node is a permutation of
technology status: Future
Cost/Availability/ Potential of
Technology
CCS Nuclear
energy
Solar Bioerg
pot.
On/High
X X X X
Off/Low
.
.
.
.
.
Stochastic/Dynamic Programming
Each node is weighted by a
probability
Framework Application
A scenario tree contains in a discrete form the information
gathered through expert elicitation
Probabilities associated to nodes
Information on the uncertain parameter, e.g. cost,
availability, potential of future technologies
Two strategies:
1. Exogenous probabilities: evaluate given RD&D
strategy using the corresponding scenario tree
2. Endogenous probabilities: RD&D investments are a
decision variable determining the probability of each
branch in the scenario tree
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Deterministic analysis to understand the relevance of
more sophisticated stochastic analysis
Identify the extreme judgments to explore the solution
space
Criteria of assessment
Impact on technology penetration
Technology wedge potential
Impact on climate policy costs
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Step 1: Exploring the Solution Space
Supply of Technologies: Example of Inputs from EE
Solar PV and
CSPBiofuels
Batteries
Evs PHEVs
Breakthrough cost thresholds
Given a R&D
level,
what is the
probability of
achieving these
breakthrough
costs in 2030
11.27 $/kWh 0.73 $/lge 270 $/kWh 350 $/kWh
5.55 $/kWh 0.40 $/lge
150 $/kWh 250 $/kWh3 $/kWh 0.20 $/lge
Penetration rates
Probability of
different
penetration
rates by 2050
5% 20% 20%
20% 50% 50%
30% 70% 70%
Solar
PV and CSP
Biofuels
2° gen
Most
optimistic
case
Lowest R&D funding scheme
P(technology cost
<5.55cents$/kWh)>0
Lowest R&D funding scheme
P(technology cost <0.40 $/lge )>0
Diffusion
High diffusion
P(electricity share >=30%)>0
Low diffusion
P(electricity share >=5%)>0
Medium diffusion
P(trans. fuel share >=50% or
20%)>0
Low diffusion
P(trans. fuel share >=20%)>0
Worst case Highest
R&D funding scheme
P(technology cost
<5.55cents$/kWh)=0
Highest R&D funding scheme
P(technology cost <0.73 $/lge )=0
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An Example: the Solution Space for Solar and Biofuels
Technology Penetration
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0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
Share of solar powerOptLowDiff_BAU
OptLowDiff_STAB
OptHighDiff_BAU
OptHighDiff_STAB
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
Share of biofuels on energy inputs in transportation
Opt_LowDiff_BaU
Opt_LowDiff_STAB
Opt_MedDiff_BaU
Opt_MedDiff_STAB
The uptake of the breakthrough technology is immediate in
the case of biofuels but takes some time in the case of solar
(capital inertia, build up infrastructure)
Wedge Potential – TPES (535 ppme)
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0
100
200
300
400
500
600
700
800
900
EJ
Worst
0
100
200
300
400
500
600
700
800
900
EJ
Opt_lowdiff
0
100
200
300
400
500
600
700
800
900
EJ
Opt_highdiff
Energy saving
Biofuels-2nd gen
Solar
Ren
Nuclear
Biomass
Coal+CCS
Coal
Gas
Oil
0
100
200
300
400
500
600
700
800
900
EJ
Worst
SOLAR
BIOFUELS
0
100
200
300
400
500
600
700
800
900
EJ
Opt_lowdiff
0
100
200
300
400
500
600
700
800
900
2005
2010
2015
2020
2025
2030
2035
2040
2045
2050
EJ
Opt_highdiffEnergy saving
Biofuels-2nd genSolar
Ren
Nuclear
Biomass
Coal+CCS
Coal
Gas
Policy Costs
1
0
-3.14%
-2.72%
-2.32%
Worst Opt_LowDiff Opt_MedDiff
BIOFULESDISCOUNTED GDP 2005-2100 CHANGE WRT BAU (5% DISC.)
Larger effect on policy costs if the breakthrough occurs in a
sector with high marginal abatement costs(e.g. transport)
-3.14% -3.11% -2.99%
Worst Opt_LowDiff Opt_HighDiff
SOLARDISCOUNTED GDP 2005-2100CHANGE WRT BAU (5% DISC.)
Innovation Diffusion Innovation Diffusion
Towards Definition of “Option Values”
1
1
0.00%
0.10%
0.20%
0.30%
0.40%
0.50%
0.60%
0.70%
0.80%
0.90%
Innovation Diffusion
GDP % gain compared to Worst case
Solar
Biofuels
High diffusion increases the economic value of technologies
Obstacles and Challenges
How to extrapolate the global RD&D portfolio expenditure from US- and EU-centered expert’s opinions?
How to harmonize high and low cost (potentials/availability) of technologies across modeling groups
How to report results from uncertainty group in a intelligible and policy relevant way?
How can results from expert elicitation serve as inputs for modelers in practice? Collect data from different groups and make them available to the modeling community
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