Transcript Optimal Technology R&D in the Face of Uncertainty

Optimal Technology R&D in the
Face of Climate Uncertainty
Erin Baker
University of Massachusetts, Amherst
Presented at Umass INFORMS
October 2004
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Today’s Talk
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Background on Climate Change
How to model R&D programs in top-down
models?
Theoretical results indicate that
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How R&D is modeled matters, and
How increasing risk is modeled matters.
Results and insights from numerical model.
Including uncertainty in the returns from R&D
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Climate Change
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Humans are changing the climate, through the
accumulation of greenhouse gasses (GHG).
GHG are mainly emitted through the
combustion of fossil fuels.
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Human Contributions to the
Greenhouse Effect
Other CFCs
7%
Methane
15%
Carbon Dioxide
55%
CFCs 11 and 12
17%
Nitrous Oxide
6%
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Carbon Emissions Due to Fossil Fuel
Consumption 1860 -1985
Billion Tons of Carbon
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5
4
Natural Gas
Oil
Coal
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2
1
0
1860
1885
1910
1935
1960
1985
Year
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Reference Carbon Projections
Million Metric Tons
Carbon
30000
ROW
Mexico & OPEC
India
China
EEFSU
Japan
CANZ
EEC
US
25000
20000
15000
10000
5000
0
2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
Year
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Climate Change
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We have seen an increase of about 1°C over
the last 100 years.
A doubling of CO2 from pre-industrial levels
would increase global average temperatures
by about 1.5 – 4.5°C
A 1.5°C rise would be warmest temperatures
in last 6000 years.
A 4.5°C rise would raise temperatures to
those last seen in time of dinosaurs.
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Climate Change - Uncertainty
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100 years is a blip in geologic time.
Climate models are still in infancy.
Regional climate impacts are highly
uncertain.
Human impacts of climate changes are
uncertain.
Potential for catastrophic damages
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Runaway greenhouse effect
Failure of the Gulf stream
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Climate Change
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Uncertainty about how emissions today will
cause damages tomorrow.
But, we are learning more and more.
Uncertainty, learning, and adaptation impact
current decisions
Conclusion: Uncertainty + Learning = less
control of emissions.
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Kolstad
Ulph & Ulph
Manne & Richels
Baker
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What about R&D?
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R&D planning is complicated by different
programs
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Solar PVs versus efficiency of coal-fired electricity
We consider optimal R&D
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uncertainty and learning about climate damages
choice of R&D program
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How to represent R&D?
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Climate change is a complex problem,
involving multiple variables.
In order to get insights about the best policy,
we need a simple representation of
alternative R&D programs.
What matters for climate change is how the
technology that results from the R&D impacts
the cost of abatement.
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Climate Change Policy
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We would like to choose a carbon emissions
level that equated the marginal cost of
abatement with the marginal damages from
climate change.
MC = MD
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Technical change impacts the marginal cost of
abatement.
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The Production Function
t = standard inputs
e = emissions
t
Q = f(t,e)
0
e
e*
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From production function to abatement
cost curve
Production Function
t
Abatement Cost Curve
$ Cost
a
Q = f(t,e)
e
0
e = emissions
e* 0
m
m = emission reductions
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Multiplicative Shift:
Cost Reduction of No-Carbon Alternatives
tmax
a
Production
Function
Cost
a
1-a
1-a
tmin
0
e*
e
0
m
1
The abatement cost curve pivots
downward
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Emissions Reduction of Currently
Economic Alternatives
Production
Function
1- a
Cost
a
a
1- a
a
e
1- a
m
The abatement cost curve pivots
to the right
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Define Risk
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How does optimal investment in R&D change
with an increase in risk?
“Risk” – “uncertainty” – “Mean-preservingspread”
See for example Rothschild & Stiglitz
1970,1971.
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Theory Results
min g a   Ez min cm ,a   Dm , z 
a
m
Proposition:
Optimal R&D decreases with some increases in
risk.
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Theory Results
min g a   Ez min cm ,a   Dm , z 
a
m
Proposition:
Optimal R&D decreases with some increases in
risk.
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“Full abatement”
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Theory Results
min g a   Ez min cm ,a   Dm , z 
a
m
Proposition:
Optimal R&D decreases with some increases in
risk.
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“Full abatement”
Fundamentally different from abatement result
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Theory Results
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The converse is not true – some R&D
programs will always decrease in risk.
Individual R&D programs will react differently
to an increase in risk.
It is crucial to model the specific program.
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R&D impacts convexity of cost curve /
production function
t
t
Cost
Reduction
Emissions
reduction
e
Flatter 
R&D increases
in risk
e
More convex 
R&D decreases
in risk
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Integrated Assessment Model
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William Nordhaus’s DICE
Optimal Growth + Climate Model
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Social Planner chooses how to divide income
between consumption, investment, and emissions
reduction.
Added uncertainty, using stochastic
programming.
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First 5 periods decisions are made under
uncertainty
After 5 periods the world splits into two damage
scenarios.
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Integrated Assessment Model
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William Nordhaus’s DICE
Added R&D as a decision variable.
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One time decision in 1st period before learning
Cost reduction implemented in 50 years, after
learning about damages.
No uncertainty in the returns to R&D.
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2 Types of increasing risk
Increasing Probability
certain
low
Probability of high damage
0
.018
Value of high damage
.042
Value of low damage
.0035 .002794
Increasing Damage
certain
Probability of high damage 0
Value of high damage
Value of low damage
.0035
low
.018
.042
.002794
medium
.05
.042
.001473
medium
.013
.057
.002794
high
.08333
.042
0
high
.002374
.3
002794
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Increasing Probability
Increasing Damage
1
1
1
1
0.75
0.9
0.25
0.
1
0.33
0.78
3
3
0.82
0.75
0.18
0.25
0.33
3
0.33
Damage is on x-axis, Probability is on y-axis
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Increasing Probability
Increasing Damage
1
1
1
1
0.75
0.9
0.25
0.
1
0.33
0.78
3
3
0.82
0.67
0.33
0.18
0
3
0.33
Damage is on x-axis, Probability is on y-axis
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Increasing Probability
Increasing Damage
1
1
1
1
0.75
0.9
0.25
0.
1
0.33
0.78
3
3
0.86
0.67
0.33
0.14
0
3
0.33
Damage is on x-axis, Probability is on y-axis
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Results – Increasing Probability
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Billions of US$
Optimal R&D
0.4
0.3
0.2
0.1
12
8
4
0
0
0
0.02
0.04
0.06
0.08
Probability of high damage
Cost Reduction
0.1
0
0.02
0.04
0.06
0.08
Probability of high damage
Emissions Reduction
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0.1
Results – Increasing Damages
5
Billions of US $
Optimal R&D
0.16
0.12
0.08
0.04
4
3
2
1
0
0
0
20
40
60
% GDP Loss
Cost Reduction
80
0
20
40
60
80
% GDP Loss
Emissions Reduction
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Conclusions
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R&D can be a hedge against uncertainty.
But, it depends on what kind of R&D.
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R&D into reducing the cost of low carbon
alternatives
And what kind of risk.
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Increasing the probability of needing very low
carbon technologies, rather than considering
higher levels of damages.
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Unknowns
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We need to estimate the relationship
between investment in and R&D program,
and the expected impact on the abatement
cost curve.
We need to estimate the amount of
uncertainty surrounding R&D programs.
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Uncertain Returns to R&D
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DICE equations


1
b2

1- 
Qt 
1 - b1m t At K t Lt
2
1  1T   2T
Et  1 - m t At K t L

1-
t
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