Applying risk analytic techniques to the integrated ... · business communities as opposed to the...

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Applying risk analytic techniques to the integrated assessment of climate policy benefits

Roger JonesCSIRO Marine and Atmospheric Research

Gary YoheDepartment of Economics, Wesleyan University

Acknowledgements Ben Preston CSIRO

GLOBAL FORUM ON SUSTAINABLE DEVELOPMENTON THE

ECONOMIC BENEFIT OF CLIMATE CHANGE POLICIESParis 6-7 July 2006

Incr

easi

ng c

ost o

f mitig

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nPrecautionary (risk averse) towards economy

Fast rate of time preference

Increasing likelihood of DAI

Precautionary (risk averse) tow

ards environment

Slow rate of tim

e preferenceIn

crea

sing

cos

t of m

itigat

ion

Precautionary (risk averse) towards economyFast rate of time preference

Incr

easi

ng c

ost o

f mitig

atio





ards environment

Slow rate of tim

e preference



ards environment

Slow rate of tim

e preference

Two extremes of perceived risk

Climate-related risks

Policy-related risks

Integrated Assessment Modelling

(Cost Benefit Analysis)

Policy impasse

Fear No.1 of the highly averse to economic risksType I ErrorFalse Positive – we act on greenhouse but it turns out wrongFavoured policy mix – wait and see, R&D to narrow uncertainty to predictive range, eschews targets and central controls not based on cost signals, favours market-driven tech solutions on supply

Fear No.1 of the highly averse to environmental risksType II ErrorFalse Negative – we don’t act fast enough and end up with DAIFavoured policy mix – set early targets and learn by doing, GHG trade/permit system, social and technological solutions on supply and demand, precautionary approach to DAI

Conventional economic paradigms

Conventional cost-benefit approaches dominate “economically rational” framing of climate issuesThese perceptions are held more strongly in political and business communities as opposed to the research community

• Actors who are risk averse to the short-term costs of mitigating climate change want solid estimates of the benefits of avoiding damage before they will act

• This drives climate modelling and impact assessment into a predictive framework; ill-suited to managing large uncertainties

Adding risk into integrated assessment

Incr

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ost o

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Precautionary (risk averse) towards environment

Slow

rate of time preference


(Risk-weighted damages, costs & benefits)Incr

easi

ng c

ost o

f mitig

atio


Fast rate of time preference In

crea

sing

cos

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itigat

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Slow




Slow



(Risk-weighted damages, costs & benefits)

Strategy I•Wait and see on everything•Reduce uncertainty through experience•Reactive adaptation (min loss/max benefits)•Modest mitigation – known low cost options

Strategy II•Wait and see on climate and impacts•Research economic, tech uncertainty•Reactive adaptation (min loss/max benefits)•Efforts to reduce mitigation costs

Strategy III•Act early to stabilise•Research climate & impact uncertainty•Anticipatory adaptation•Strong mitigation – develop low cost options

Strategy IV•Act on everything•Research everything•Anticipatory adaptation and cost reduction•Anticipatory mitigation and cost reduction

Strategy I•Wait and see on everything•Reduce uncertainty through experience•Reactive adaptation (min loss/max benefits)•Modest mitigation – known low cost options

Strategy II•Wait and see on climate and impacts•Research economic, tech uncertainty•Reactive adaptation (min loss/max benefits)•Efforts to reduce mitigation costs

Strategy III•Act early to stabilise•Research climate & impact uncertainty•Anticipatory adaptation•Strong mitigation – develop low cost options

Strategy IV•Act on everything•Research everything•Anticipatory adaptation and cost reduction•Anticipatory mitigation and cost reduction


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Slow rate of tim

e preference

II

I III

IV

Competing views


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Slow rate of tim

e preference

II

I III

IV

Competing views

Integrated approaches to risk

Almost certain

Highly likely

Least likely

Low probability, extreme outcomes

Damage to the most sensitive, many benefits

Increased damage to

many systems, fewer benefits

Considerable damage to most

systems

Moderately likely

Probability Consequence

Core benefits of adaptation and mitigation

Probability – the likelihood of reaching or exceeding a given level of global warmingConsequence – the effect of reaching or exceeding a given level of global warming

Risk = Probability × Consequence

Vulnerable to current climate

Happening now

Almost certain

Highly likely

Least likely

Low probability, extreme outcomes

Damage to the most sensitive, many benefits

Increased damage to

many systems, fewer benefits

Considerable damage to most

systems

Moderately likely

Probability Consequence

Core benefits of adaptation and mitigation

Probability – the likelihood of reaching or exceeding a given level of global warmingConsequence – the effect of reaching or exceeding a given level of global warming

Risk = Probability × Consequence

Vulnerable to current climate

Happening now

Adaptation and mitigation

Adaptation increases the coping range through biological and social means

Mitigation reduces the magnitude and frequency of greenhouse-related climate hazards

At the policy scale they are complementary, not inter-changeable.

They also reduce different areas of climate uncertainty

Global mean warming probabilities 2100

0

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0 1 2 3 4 5 6 7 8 9 10

Global Mean Temperature Change (°C)

Dec

reas

e in

GD

P (%

)

A1FI A1B A1T

Incremental damages

0

0.5

1

1.5

2

2.5

3

3.5

4

1990 2010 2030 2050 2070 2090Year

Glo

bal M

ean

Tem

pera

ture

Incr

ease

(°

C)

Assessing market damages

Testing cost curve sensitivity

0

10

20

30

40

50

60

70

80

90

100

0 1 2 3 4 5 6 7 8 9 10


Dec

reas

e in

GD

P (%

)

Linear Quadratic Cubic Step function

Risk under different emissions limits – market

0

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60

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90

100

0 1 2 3 4 5 6 7 8 9 10


Dec

reas

e in

GD

P (%

)

0

10

20

30

40

50

60

70

80

90

100

Prob

abilit

y of

exc

eedi

ng

tem

pera

ture

(%)

Linear Quadratic Cubic Step function A1FI A1B A1T

Assessing non-market damages

Critical thresholds – coral reefs

0

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30

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50

60

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80

90

100

0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0Global Mean Temperature Change (°C)

Area

of c

ritic

al th

resh

old

exce

edan

ce

(%)

CT1: Bleaching

CT2: Sensitive species

CT3: Tolerant species

Critical thresholds – extinction risk

0

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0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0


Spec

ies

unde

r Ext

inct

ion

Ris

k (%

)

Critical thresholds – thermohaline circulationy = 9.9747x - 2.9767

R2 = 0.6055

0

10

20

30

40

50

60

0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0

Global Mean Temperature Change (oC)

% R

educ

tion

in A

tlant

ic O

vert

urni

ng

Raper et al. (2001)IPCC (2001)Zickfield et al. (2004)Voss and Milkalajewicz (2001)Boar et al. (2000)Schmittner et al. (2005)Dai et al. (2005)Washington et al. (2000)Bleck and Sun (2004)Kamenkovich et al. (2003)Gent (2001)Wood et al. (1999)Sun and Bleck (2001)Hu et al. (2004)

Critical thresholds – Greenland ice-sheet

0.0

0.1

0.2

0.3

0.4

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0.6

0.7

0.8

0.9

1.0

0.0 0.5 1.0 1.5 2.0 2.5 3.0

Global Mean Temperature Change

Cum

ulat

ive

Prob

abili

ty o

f Col

laps

e

Hansen (2005)Huybrechts et al. (1991)Greve (2000)Huybrechts and De Wolde (1999)

Critical thresholds – all

0

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30

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50

60

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100

0 1 2 3 4 5 6 7 8 9 10Global Mean Temperature Change (°C)

Prop

ortio

n of

loss

(%)

Irr

ever

sibl

e G

reen

land

ice-

mel

t (%

)

Species Extinction Coral Reefs THC Greenland

Risk under different emissions limits – non-market

0

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60

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100

0 1 2 3 4 5 6 7 8 9 10Global Mean Temperature Change (°C)

Prop

ortio

n of

loss

(%)

Gre

enla

nd ic

e-m

elt (

%)

0

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50

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100

Prob

abilit

y of

exc

eedi

ng

tem

pera

ture

(%)

Species Extinction Coral Reefs THC GreenlandA1FI A1B A1T

Testing a Kyoto-like mitigation

0

1

2

3

4

1990 2010 2030 2050 2070 2090Year

Mea

n G

loba

l War

min

g (°

C)

Mitigated warmingRemaining warming

Changed temp risk with Kyoto-like mitigation

0

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0 1 2 3 4 5 6 7 8 9 10


Dec

reas

e in

GD

P (%

)

A1FI A1B A1T A1FI (KP-like) A1B (KP-like) A1T (KP-like)

Risk-weighted damages – non-monetary

Species Coral Reefs THC slow-down

Green-land ice sheet

Scenario upper limit

(% damage) Chance of loss (%)

A1FI 54.6 97.3 36.1 99.3 A1B 31.2 94.5 27.2 98.3 A1T 25.1 92.3 24.3 96.7

Risk-weighted damages – monetary

Linear Squared Cubic Step change Scenario

upper limit (% decrease in GDP) A1FI 3.9 5.1 5.5 9.4 A1B 3.0 3.1 3.2 4.3 A1T 2.4 2.6 2.6 3.2 (NPV $Trillion 1990) A1FI 68.3 53.1 74.2 131.6 A1B 49.2 35.1 46.2 57.1 A1T 43.6 30.1 38.8 44.8

Risk-weighted benefits – non-monetary

Species Coral Reefs THC slow-down

Green-land ice sheet

Scenario upper limit

(% damage) Chance of loss (%)

A1FI -3.0 -0.4 -1.3 -0.4 A1B -3.3 -0.8 -1.4 -1.1 A1T -2.8 -0.9 -1.3 -2.0

Risk-weighted benefits – monetary

Linear Squared Cubic Step change Scenario

upper limit (% decrease in GDP) A1FI -0.1 -0.3 -0.4 -0.7 A1B -0.1 -0.2 -0.3 -0.5 A1T -0.1 -0.2 -0.2 -0.4 (NPV $Trillion 1990) A1FI -2.8 -2.7 -4.2 -11.6 A1B -2.7 -2.3 -3.5 -5.6 A1T -2.7 -2.3 -3.5 -5.6

Damages and benefits

Damages build from bottom upBenefits of mitigation come from top down (highest temperatures, worst plausible risks developed from a comprehensive set of reference scenarios)Benefits of adaptation work from bottom-up, reducing damagesKnowledge of costs and benefits of adaptation remains poorIt is possible to assess near to mid-term adaptation needs (adapting to the inevitable) by using risk analysis based on changes already committed to

Conclusion

Risk can be used to compare policy and climate risksFrameworks for assessing the benefits of climate policy within probabilistic “risk” frameworks provide insights into where policy benefits may lieBenefits could be expressed in familiar terms (e.g. cost effectiveness) but expanded to include monetary and non-monetary benefits of risk managementNew knowledge and actions will both alter risk profile –learn by research, learn by doing

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