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Optimal Technology R&D in the Face of Climate UncertaintyErin Baker

University of Massachusetts, Amherst

Presented at Umass INFORMS

October 2004

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Today’s Talk Background on Climate Change How to model R&D programs in top-down

models? Theoretical results indicate that

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 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

Carbon Dioxide55%

Nitrous Oxide6%

CFCs 11 and 1217%

Methane15%

Other CFCs7%

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Carbon Emissions Due to Fossil Fuel Consumption 1860 -1985

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1

2

3

4

5

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1860 1885 1910 1935 1960 1985

Year

Bill

ion

To

ns

of

Car

bo

n

Natural GasOilCoal

6

0

5000

10000

15000

20000

25000

30000

2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100Year

Mill

ion M

etric

Tons

Ca

rbon

ROW

Mexico & OPEC

India

China

EEFSU

Japan

CANZ

EEC

US

Reference Carbon Projections

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Climate Change

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 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

Runaway greenhouse effect Failure of the Gulf stream

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Climate Change 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. Kolstad Ulph & Ulph Manne & Richels Baker

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What about R&D?

R&D planning is complicated by different programs Solar PVs versus efficiency of coal-fired electricity

We consider optimal R&D uncertainty and learning about climate damages choice of R&D program

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How to represent R&D?

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

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 Technical change impacts the marginal cost of

abatement.

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The Production Function

*

0

Q = f()

= standard inputs

= emissions

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Cost

*

0

Q = f()

0

$

Production Function Abatement Cost Curve

From production function to abatement cost curve

= emission reductions = emissions

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Multiplicative Shift:Cost Reduction of No-Carbon Alternatives

max

min

1-

00 1

1-

Production Function

Cost

The abatement cost curve pivots downward

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Production Function

Cost

Emissions Reduction of Currently Economic Alternatives

The abatement cost curve pivots to the right

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Define Risk

How does optimal investment in R&D change with an increase in risk?

“Risk” – “uncertainty” – “Mean-preserving-spread”

See for example Rothschild & Stiglitz 1970,1971.

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Theory Results

Proposition:

Optimal R&D decreases with some increases in risk.

zDcEg z ,,minmin

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Theory Results

Proposition:

Optimal R&D decreases with some increases in risk.

“Full abatement”

zDcEg z ,,minmin

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Theory Results

Proposition:

Optimal R&D decreases with some increases in risk.

“Full abatement” Fundamentally different from abatement result

zDcEg z ,,minmin

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Theory Results

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

Cost Reduction

Emissions reduction

Flatter R&D increases

in risk

More convex R&D

decreases in risk

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Integrated Assessment Model William Nordhaus’s DICE Optimal Growth + Climate Model

Social Planner chooses how to divide income between consumption, investment, and emissions reduction.

Added uncertainty, using stochastic programming. 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 William Nordhaus’s DICE Added R&D as a decision variable.

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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Increasing Damage certain low medium high Probability of high damage 0 .018 .013 .002374 Value of high damage - .042 .057 .3 Value of low damage .0035 .002794 .002794 002794

Increasing Probability certain low medium high Probability of high damage 0 .018 .05 .08333 Value of high damage - .042 .042 .042 Value of low damage .0035 .002794 .001473 0

2 Types of increasing risk

Increasing Probability

1

1

0.78 3

0.9

0.1

0.75

0.25

0.33 3

Damage is on x-axis, Probability is on y-axis

Increasing Damage1

1

0.75

0.25

0.33 3

4

0.82

0.18

0.33

1

1

0.78 3

0.9

0.1

Damage is on x-axis, Probability is on y-axis

1

1

0.75

0.25

0.33 3

0.67

0.33

0 3 4

0.82

0.18

0.33

Increasing Probability Increasing Damage

1

1

0.78 3

0.9

0.1

Damage is on x-axis, Probability is on y-axis

0.67

0.33

0 3 5

1

1

0.75

0.25

0.33 3

0.86

0.14

0.33

Increasing Probability Increasing Damage

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Results – Increasing Probability

0

4

8

12

16

0 0.02 0.04 0.06 0.08 0.1

Probability of high damageB

illio

ns

of

US

$

Cost Reduction Emissions Reduction

0

0.1

0.2

0.3

0.4

0 0.02 0.04 0.06 0.08 0.1

Probability of high damage

Op

tim

al R

&D

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Results – Increasing Damages

% GDP Loss

0

1

2

3

4

5

0 20 40 60 80

Bill

ion

s o

f U

S $

0

0.04

0.08

0.12

0.16

0 20 40 60 80

% GDP Loss

Op

tim

al R

&D

Cost Reduction Emissions Reduction

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Conclusions

R&D can be a hedge against uncertainty. But, it depends on what kind of R&D.

R&D into reducing the cost of low carbon alternatives

And what kind of risk. Increasing the probability of needing very low

carbon technologies, rather than considering higher levels of damages.

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Unknowns

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

112

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211

1ttt

btt LKAb

TTQ

11 ttttt LKAE