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

2

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

8

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)

9

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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www.icarus-project.org