risk management: approaches and methods - diw
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Risk management: approaches and methods
COST OF INACTION Workshop
10th-11th April 2006
Roger Jones
CSIRO Marine and Atmospheric Research
Adaptation and mitigation
� Adaptation increases the coping range through biological and social means
� Mitigation reduces the magnitude and frequency of greenhouse-related climate hazards
Therefore, they are complementary, not interchangeable.
They also reduce different areas of climate uncertainty
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
Operational differences
Adaptation
Most needed for impacts that are vulnerable to current climate risks or small changes in climate change (These are the most likely to be affected).
Cannot cope with large changes or many impacts (too expensive and difficult)
Adaptation will be local and mainly shorter-term adjustments
Mitigation
Reduces climate hazards (e.g. global warming) progressively from the top down.
Unlikely to prevent a certain level of climate change – adaptation will be needed for such changes.
Mitigation actions perceived as a cost now may become profitable later when damages can be better accounted for in carbon price.
Conventional economic paradigms – the view from Oz
� Conventional cost-benefit approaches dominate “economically rational” framing of climate issues
� These perceptions are widely held in political and business communities
• 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
Some assumption used in CBA
� Assesses cost of diverting from conventional economic pathway to avoid damage� Marginal net present costs are the incremental costs of a small
increase in emissions, discounted to the time the decision aboutemission reduction is made
• E.g. Kyoto is often assessed as costing a significant amount for a small benefit (0.04–0.10°C by 2050 and 0.08–0.28°C by 2100; Wigley 1998). Monetised, this adds up to a small amount of damage
� The benefit assesses the difference in damage between a reference scenario and a policy scenario
� Most studies use GDP with “guesstimated” damage functions to assess benefits
Kyoto-like abatement
0
1
2
3
4
1990 2010 2030 2050 2070 2090Year
Mea
n G
loba
l War
min
g (°C
)
Mitigated warmingRemaining warming
Cost-benefit+
� Efforts to overcome this impasse include:� Framing Kyoto as one step of a long-term program to avoid dangerous
outcomes, i.e. invoking the UNFCCC: the damages will eventually be serious enough to warrant starting now
• Fails to impress actors sensitive to short-term risks and insensitive to long-term risks
� Assessing multiple pathways and making a semi-quantitative estimate• Which one to choose?
• Both costs and benefits must be finite
� Alternative approaches: safe minimum standards / safe corridors / targets• E.g. assessing a threshold of dangerous climate change, then assessing
conditions required to remain below this
• Rejected by US, Australia, a broad proportion of industry, and finance/treasury in some countries that have policy targets
• Will developing country “tigers” accept regional caps?
Application of risk management approaches
� Can cope with a range of different benefit types
� Designed to manage multiple uncertainties (ideally by encompassing all “knowable” uncertainties)
� Can accommodate non-rational approaches
� Can utilise methods such as CBA, other optimisation methods, stakeholder evaluation, multi-criteria analysis, game theory
Adaptive and mitigative capacities
Coral Reefs
Developed Country Agriculture
Developing Country Agriculture
Protected Coastal Infrastructure
Mitigative ←capacity
Analyse risk
Manage risk (adapt)
Residual risk
Coping range→ Adaptive
capacity
Manage risk (mitigate)
Coral Reefs
Developed Country Agriculture
Developing Country Agriculture
Protected Coastal Infrastructure
Mitigative ←capacity
Analyse risk
Manage risk (adapt)
Residual risk
Coping range→ Adaptive
capacity
Manage risk (mitigate)
Mitigative ←capacity
Analyse risk
Manage risk (adapt)
Residual risk
Coping range→ Adaptive
capacity
Manage risk (mitigate)
Mitigative ←capacity
Analyse risk
Manage risk (adapt)
Residual risk
Coping range→ Adaptive
capacity
Manage risk (mitigate)
Autonomous adaptation
Improve technology accessInstitutional reformImproved equityAccess to informationBuild social capitalAccess to wealth creation
Adapting (generic)
Replace activityAbandon activityTransform activity
Adapting (transformative)
Mainstreaming adaptationNatural resource managementNew technologyDisaster planningRetrofit existing structuresBuild resilience/resistance
Adapting (specific)
Mitigative ←capacity
Analyse risk
Manage risk (adapt)
Residual risk
Coping range→ Adaptive
capacity
Manage risk (mitigate)
Hydrogen economyGeosequestration
Society dematerialisesTechnology spike (bio-energy)
Solar interception
Improve technology accessEnergy reform
Improved equityAccess to information
Build social capitalReduce carbon intensity
Efficient hydrocarbon technologyLand-use & sequestration
Development of new technologySocial investment in low energy
Retrofit infrastructureImprove existing alternatives
(wind, water, solar, bio)
Mitigating (transformative)
Mitigating (specific)
Mitigating (generic)
Assessing risk of exceeding key vulnerabilities
� Critical thresholds have highly non-linear damages functions (e.g. step functions, zero to -100 in <1°C)
� Assess the likelihood of exceeding critical thresholds for key vulnerabilities under a range of emissions pathways
Combined with:
� Near-term reference emission projections coupled to longer term emission scenarios (Post SRES environment)
Uncertainty explosion
global climatesensitivity
×××× emissionscenarios
×××× regionalvariability
×××× range ofpossible impacts
Placing thresholds within scenario uncertainty
global climatesensitivity
×××× emissionscenarios
×××× regionalvariability
×××× range ofpossible impacts
A
B
0
1
2
3
4
5
6
0 100Probability (%)
0
1
2
3
4
5
6
0 5
Probability (%)
0
1
2
3
4
5
6
1990 2010 2030 2050 2070 2090
Year
Tem
pera
ture
Incr
ease
(°C
)
2.5°C Threshold
1°C Threshold
0
20
40
60
80
100
1990 2010 2030 2050 2070 2090
Year
Sea
Lev
el R
ise
(cm
) 75cm Threshold
25cm Threshold
0
20
40
60
80
100
0 6
Probability (%)
0
20
40
60
80
100
0 100
Probability (%)
Damage functions for key vulnerabilities
� Constructed as a function of global warming
� Biophysical so as not to be subject to underlying socio-economic assumptions
� Temperature rather than rainfall dependent
� The examples here are large-scale – though limited and local examples can be constructed for national assessments
ReefState model (Wooldridge et al. 2005)
0 10 20 30 40 50 60 70 80 90 100
Coral
Bare Algae100
90
80
70
60
50
40
30
20
10
0
10
20
30
40
50
60
70
80
90
100
0 Bare
0 10 20 30 40 50 60 70 80 90 100
Coral
Bare Algae100
90
80
70
60
50
40
30
20
10
0
10
20
30
40
5 0
60
70
80
90
100
0
Yr = 2010 Yr = 2030
Yr = 2050
50%
75%
95%
Simulation Endpoints
50%
75%
95%
Simulation Endpoints
Critical thresholds – coral reefs
0
10
20
30
40
50
60
70
80
90
100
0 1 2 3 4 5 6 7Global Mean Temperature Change (°C)
Are
a of
Crit
ical
Thr
esho
ld E
xcee
danc
e (%
)
CT1: Bleaching
CT2: Sensitive species
CT3: Tolerant species
Core bioclimatic range loss – warming
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 1 2 3 4 5 6 7
Warming (°C)
Num
ber
of s
peci
es
Montane tropical vertebrates (65)
Victorian endemic vertebrates (42)
Eucalypts (>300)
Critical thresholds – extinction risk
0
10
20
30
40
50
60
70
80
90
100
0 1 2 3 4 5 6 7
Global Mean Temperature Change (°C)
Spe
cies
und
er E
xtin
ctio
n R
isk
(%)
Critical thresholds – thermohaline circulation
0
10
20
30
40
50
60
70
80
90
100
0 1 2 3 4 5 6 7
Global Mean Temperature Change (°C)
TH
C S
low
dow
n (%
)
Critical thresholds – Greenland ice-sheet
0
10
20
30
40
50
60
70
80
90
100
0 1 2 3 4 5 6 7
Global Mean Temperature Change (°C)
Irre
vers
ible
Gre
enla
nd Ic
e-m
elt (
%)
Critical thresholds – all
0
10
20
30
40
50
60
70
80
90
100
0 1 2 3 4 5 6 7
Mean Global Warming (°C)
Like
lihoo
d of
loss
(%
)
Species@riskCT1: BleachingTHC@riskGreenland@riskSpecies@risk
Reference CO2 projections - basis
Projections to 2030
� International Energy Agency projections to 2030
� Recent growth estimates for India, China and several other countries
Scenarios beyond 2030
� VUT reference assumes widespread take-up of technologies currently in development (e.g. carbon capture and storage, the hydrogen economy)
� ABARE AP6 reference projects A2-type growth beyond 2030, less tech take-up
Reference CO2 projections
0
5
10
15
20
25
30
35
2000 2020 2040 2060 2080 2100
Year
CO
2 em
issi
ons
(Gt/y
r)
VUT Ref ABARE Ref A1B A1F A1T A2 B1 B2
Reference projections and intervention scenarios
0
5
10
15
20
25
1990 2010 2030 2050 2070 2090
Year
CO
2 em
issi
ons
(Gt/y
r)
Ref 2035 2030 2025 2020 2015 2010
CO2-equivalent atmospheric concentrations
200
300
400
500
600
700
800
900
1000
1100
1990 2010 2030 2050 2070 2090
Year
CO
2-e
conc
entr
atio
ns (
ppm
)
Ref 2035 2030 2025 2020 2015 2010
Mean global warming ( 2xCO2 = 3.5°C)
0
1
2
3
4
5
1990 2010 2030 2050 2070 2090
Year
Mea
n G
loba
l War
min
g (°C
)
Ref 2035 2030 2025 2020 2015 2010
Warming curves and critical thresholds
2035
2030
2025
2020
2015
2010
Ref
0
10
20
30
40
50
60
70
80
90
100
0 1 2 3 4 5 6 7Global Mean Temperature Change (°C)
Like
lihoo
d of
Exc
eedi
ng
Tem
pera
ture
Cha
nge
(%)
0
10
20
30
40
50
60
70
80
90
100
Pro
port
ion
of lo
ss (
%)
Ir
reve
rsib
le G
reen
land
ice-
mel
t (%
)
Species Extinction Coral Reefs THC Greenland
Conclusion
� High reference projections to 2030, the likelihood that climate sensitivity is >2.5°C, combined with some very sens itive biophysical damage functions, suggests that major global systems face high risks without prompt action. Delayed action reaps some benefit.
� Frameworks for assessing the benefits of climate policy within probabilistic “risk” frameworks are currently subjective, game playing methods but do offer insights and will be improved with better information
� The maximum value of risk approaches is likely to be gained in combination with other methods, but
� Other approaches also need to be combined with risk assessment
Many gaps remain
For example:
� Costs of adaptation
� Limits of adaptation
� How to include underlying socio-economic drivers within a probabilistic framework
� How to combine rational and non-rational methods
� How to combine multiple numeraires
Acknowledgments
� Colleagues in CSIRO
� Peter Sheehan and colleagues at Victoria University of Technology
� Australian Institute of Marine Science
� ABARE
� The creators and developers of the MAGICC model