Synthesizing GHG Stabilization with Impacts
and Adaptation

Gary Yohe
January 23, 2004
Workshop on GHG Stabilization Scenarios
Tsukuba, Japan

Outline of Remarks• Reflections from the TAR• Perspectives on a risk-based approach tosynthesis• Risk and robustness – distributions versus notimplausible futures• Implementation uncertainty and the proximity ofthe target• Incorporating the results of mitigation into
adaptation analyses, and visa-versa

A Perspective from the TAR• Climate related damages that can be avoided bymitigation are the benefits of that mitigation• Credible calculations of the benefits of mitigationmust therefore recognize the potential that
adaptation (autonomous and planned) could
reduce damages and therefore the benefits ofmitigation.

Support for that Approach

The environmental economics literature –optimal intervention assumes efficient evasive
activity
The finance literature – calculates risk premia
net of diversifiable risk thereby assuming
efficient diversification

More from the TAR• Adaptation may or may not reduce damages
significantly– SLR examples from developed coastlines (the US
developed coastline work of Yohe shows significant
cost savings from adaptation; corroboration insubsequent global coverage by Nichols and friends)– SLR examples from low-lying islands (Atoll stateswork by Adger shows abandonment only option toSLR, but earlier significant stress from other sources)Including Adaptation can be Critical• It follows that adaptation cannot beignored in any credible calculation of thebenefit side of mitigation– It passes the Lave test (factor of two)
– But we are not sure where, when and how.

Two Asides from Neil Adger• What can be attributed to SLR when atoll states
are more vulnerable to extinction in the nearterm from internal development paths?
• How much mitigation would be forthcoming if theCOP of the UNFCCC did not know which 5 of
the 180+ members were facing extinction?

Fundamental Conclusions from Chapter 18• “Current knowledge of adaptation and adaptive
capacity is insufficient for reliable prediction ofadaptations; it is also insufficient for rigorous
evaluation of planned adaptation options,
measures and policies of governments” (pg 880
or WGII Report)• Vulnerability is a function of exposure andsensitivity; and both can be influenced byadaptive capacity• All of these are path dependent and site specificRecall the Determinants of Adaptive Capacity• Availability of adaptation options• Availability and distribution of resources• Stocks of human and social capital• Ability of decision makers to– Assume responsibility– Process information– Separate signal from noise• Access to risk spreading mechanisms• Public perception – attribution and responsibility

A Potentially Unsettling Conclusion• Asking for estimates of the economic value of
mitigation might be wrong question.
• Thinking about mitigation in the context of acost-benefit framework might be the wrong
approach…….at least for a while….

A Risk-based Approach that Accommodates
the Synthesis of Impacts cum Adaptation with
Mitigation Targets• Thinking about both mitigation and adaptation as
tools to reduce the risk of troublesome,intolerable, etc…. climate change makes themcomplements rather than substitutes.
• Mitigation is then a means of hedging against
bad outcomes measured, net of adaptation, interms of the likelihood of crossing criticalthresholds.
• Adaptation is then a means by which systems
can expand their coping ranges or delay theircontraction.

The Cost Side• The cost side of mitigation (thought of as a riskreducing tool whose outputs are measured interms of a vector of impacts) is one of costeffectiveness; i.e., minimizing the cost ofachieving certain objectives.
• The cost side of adaptation (thought of as a riskreducing tools whose outputs are measured interms of the likelihood of crossing thresholds) isone of opportunity cost informed byunderstanding how the determinants of adaptive
capacity help or impede adaptation.

Decision-makers’ Context• Their job is to assess the relative opportunity
costs of achieving specific risk reductions.
• Double causality is required to assess theeffectiveness of mitigation.
• Single causality is sufficient to assess
adaptation; but not in a synthetic approach.
• Uncertainty becomes the reason forcontemplating policy rather than the reason
for contemplating delay.

Can Science Support this Approach?
Will there be Literature to Assess in the AR4?
• Recent MIT work (Webster, et. al., “Uncertainty
Analysis on Climate Change and Research
Policy Response”, Climatic Change, 2003)
produces distributions of temperature change
associated with a specific concentrationthreshold and translates that into SLR
possibilities (at least for 2100, but could producetranscients).

Will there be Literature?
• Recent Schneider work (See OECD Workshop
on the Benefits of Climate Policy andforthcoming special issue of Global
Environmental Change) produces distributionsof an extreme event (THC shutdown) conditionalon– natural variables (climate sensitivity, etc…)– policy-related variables (the discount rate in anotherwise informed optimization exercise).

Will there be Literature?
• Roger Jones (See OECD Workshop on theBenefits of Climate Policy and forthcoming
special issue of Global Environmental Change) :links site specific thresholds to adaptation andclimate variables– SLR illustration with the likelihood of crossing criticalthresholds at specific years– Episodes of coral bleaching and mortality with thelikelihood of crossing critical ocean temperaturethresholds at specific years

One Schematic of the Next Step to Mitigation
Temperature (climate variable) distributions →Impact (vector) distributions →Frequency of crossing critical thresholds
Adding adaptation assesses potential of changing
the thresholds [or the correlation between
temperature (climate variable) and impact].
Contemplating mitigation influences thetemperature (climate variable) distribution

Sea Level Rise is a Great Example – As Usual• Distributions of temperature change support
distributions of SLR.
• Local subsidence combines with this to produce
distributions of local SLR.
• Distributions of impacts (inundation, salt-water
intrusion, vulnerability to coastal storms, etc….)
follow from local modeling links to SLR.
• Adaptations are obvious (protect or not; set-backrules, etc….)• Mitigation effects distributions of temperature.

A Second Approach – Not Implausible Futures• Not-implausible futures produce ranges ofimpacts across which adaptations must cope.
• The key on the adaptation side is to look forrobust responses that handle many possible
futures.
• The link to mitigation follows from changes in not
implausible futures.
• The key on the mitigation side is to look at theeffect on the range or timing of futures acrosswhich robustness might be measured.

A New Example – Flooding in Bangledesh• Strzepek has calibrated a hydrologic model ofthe Ganges and Brahmaputra rivers to COSMIC
output to produce trajectories of maximum
monthly flow; critical variables include– Monthly precipitation in lowlands (monsoon months)– Monthly precipitation and temperature (wintermonths) in highlands (determines timing andsignificance of snowmelt)• Strzepek has also calibrated the likelihood ofvarious degrees of flooding to maximum flows

Preliminary Results – 684 Scenarios

450000400000350000Flow in 2100

30000025000020000015000010000050000
0100000120000140000160000Flow in 2050

180000200000220000Representative Scenarios

450000400000Flow in 2100

35000030000025000020000015000010000050000
0100000120000140000160000FLow in 2050

180000200000220000An Alternative View of the Representative
Scenarios

400000Maximum Monthly Flow

350000
Scenario 1

300000

Scenario 2
250000

Scenario 3
Scenario 4200000

Scenario 5
Scenario 6

150000

Scenario 7
100000

Scenario 8

50000
02000

2020

2040

2060
Year

2080

2100

The Likelihood of Severe Flooding

Probability of an Extreme Flood (per year)

1.000

0.800

Scenario 1
Scenario 2
Scenario 30.600

Scenario 4
Scenario 50.400

Scenario 6
Scenario 7
Scenario 80.200

0.000
2000

2020

2040

2060
Year

2080

2100

The Likelihood of Moderate Flooding

Probability of Medium Flooding (per year)

1.000

0.800

Scenario 1
Scenario 2
Scenario 30.600

Scenario 4
Scenario 50.400

Scenario 6
Scenario 7
Scenario 80.2000.000
2000

2020

2040

2060
Year

2080

2100

The Likelihood of Modest Flooding

Probability of Low Flooding (per year)

1.000

0.800
Scenario 1
Scenario 20.600

Scenario 3Scenario 4
Scenario 5

0.400

Scenario 6
Scenario 7
Scenario 8

0.200

0.000
2000

2020

2040

2060
Year

2080

2100

Two Stabilization Options• Limit concentrations – temperature uncertainty
persists, particularly with 5% to 10% of the tail ofthe cumulative probability distribution at 9degrees or more.
• Limit temperatures – produces significant
uncertainty about the cost of compliance.
• Implementation uncertainty – the ability toachieve the target and/or effect midcourse
corrections.

A Working Hypothesis• Implementation uncertainty is greater withtemperature than concentration targets.
• Perhaps AR4 should include a discussion of any
literature that speaks to implementation
uncertainty.

In Any Case
• AR4 will include a chapter on synthesis in WGII
(because the temptation to add things up
globally is smaller than in WGIII)• Analysis of mitigation should focus on costeffectiveness, the ability to make mid-course
corrections, and implementation uncertainty.
• Analysis of adaptation should focus onunderstanding the roles played by the various
determinants of adaptive capacity and theantecedents of robust options.

A Two Way Street• Adaptation must be included in any assessmentof what may or may not be accomplished by
mitigation in terms reducing the likelihoodcrossing critical impact thresholds.
• The degree to which mitigation complements
adaptation in reducing those likelihoods must be
explored with full recognition of associateduncertainties in the outcome of mitigation.