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The Use of New Economic DecisionSupport Tools for Adaptation:
A review of methods and applications, and guidance on applicability
Paul Watkiss and Alistair Hunt
Costs and Benefits of Adaptation

Economic literature primarily uses scenario based impact assessment

Focuses on technical options, where unit costs can be applied

e.g. coastal costs per km of dyke, costs of irrigation per ha

Adaptation options are assessed for one defined scenario at a time, using an
if-then framework (predict and optimise).

Using this approach adaptation appears highly beneficial/cost-effective

But this doesn’t recognise ‘uncertainty’

Uncertainty can be defined in many ways, but the critical difference in
economics is between risk (Prob*Mag) versus uncertainty (no probability)
2080s, A1B
(Wettest)
2080s, A1B
(Driest)
Rainfall – RCM output
Source Christensen, Goodess, Harris, Climatic and Watkiss, 2011
What happens when costs and
benefits are this uncertain ?
• Central tendency is not a useful concept
• Not solved by moving to probability or
expected values
• Results are not helpful for decision making
or decision makers
• Noting uncertainty not a reason for inaction
Change in EAD between the 2080s (2071-2100) and baseline
period (1961-1990) for the A1B scenario based on
LISFLOOD simulations driven by various regional climate
models. Each plate represents the results for one of the 12
model combinations
Feyen et al, 2011
Economic Decision Support

The existing tools used in economic decision support and socio-economic
evaluation / appraisal are not well suited for adaptation

Cost-benefit analysis, cost-effectiveness analysis and multi-criteria analysis all
problematic, particularly in relation to uncertainty

Emerging new economic decision support tools better address uncertainty

Real Option Analysis, Robust decision making, Portfolio analysis

As part of FP7 work, looking into these methods, working with practical experts

Testing (and cross-comparing) on practical adaptation case studies
Real Option Analysis (ROA)

A financial option gives the investor the right, but not the obligation, to acquire a
financial asset in the future (with price reflected in option contract). Options
Analysis (OA) helps investigate these decisions, i.e. of uncertainty.

Same approaches provide insight for investment decisions in physical assets
(hence ‘real’ options) under risk/uncertainty - Real Options Analysis (ROA)

ROA relevant when option to invest in an asset, and can adjust timing based on
changes in conditions or information, to improve the return on investment –
whether to invest or wait.

OR when phased projects with multiple stages, adjust the timing, adapt, expand
or scale-back the project in response to external events as unfold (flexibility).

Sounds useful ! Widely recommended for adaptation, but mis-understood. Many
recommending ROA actually discussing adaptive pathways or decision trees!
Approach
Cash
Flow ($)
Immediate investment
decision point
Upper estimate of gross margin
Expected (average) gross margin
Lower estimate of gross margin
Capital cost
Now
t=0
time
t=Tp
Now or never” investment
option at t=0
Invest
now
€100m
50%
Risk
event
High revenue €175m, net return = €175-100 = €75m
Expected net return = 50%x €75m + 50% x -€25m = €25m
50%
Decision
point
Wait
Option to wait until after t=Tp, the
expected time of a change that
affects the investment
50%
Risk
event
Low revenue €75m , net return = € 75-100 = - €25m
High revenue €175m, net return = €175-100 = €75m
Expected net return = 50%x €75m + 50% x €0m = €37.5m
50%
Don’t invest, net return = €0m
Application to Adaptation

Works well when:

Investment decision is irreversible (major infrastructure) and decision-maker has
some flexibility over when to carry out the investment,

When potential new and valuable information regarding the success of the
investment.

Where opportunity cost of waiting, e.g. adaption deficit or long build time
(otherwise always wait)

There are relevant decision points, and possible to derive an evaluation of
possible outcomes with probabilities assigned to these different outcomes.
Example

UK Treasury (HMT, 2009) has produced guidance on climate change appraisal
that recommends real option analysis

Provides example to show how ROA can demonstrate higher NPV (expected
value) from including flexibility with example of upgradeable sea wall

However, highly simplified and does not reflect complexity of real world example
-10
+25
Advantages/disadvantages

Strengths - information on large investment decisions for adaptation, providing a
way to assess in quantitative and economic terms

A) the benefits of implementing now versus waiting. B) value of flexibility.

Useful way to set out adaptation decisions (with decision trees) – providing a way
to conceptualise adaptive management

Disadvantages, small set of cases where the approach is likely to be applicable
(most current examples are for SLR – as easiest area – difficult to apply other)

High complexity, high data requirements, expert application, the need for
probabilities, and the need to consider multiple risk points for climate change.

Resource intensive, and will not be applicable for most studies because of
resource limitations.
TE2100

No adaptation presentation is complete without a Thames Estuary 2100 slide

Based on a managed adaptive approach to ensure that the right investments are
made at the right time.

But misconception that TE2100 used ROA – it did not ! And that pathways are
ROA – not the same thing !
Robust Decision Making

Robust Decision Making (RDM) is a decision support tool that is used in situations
of deep uncertainty, where little or no probabilistic information available

Premised on the concept of “robustness”, i.e. minimising regret, rather than
“optimality” such as with predict-then-act approaches

A robust strategy is one that performs reasonably well - compared with
alternatives - over a wide range of scenario futures

Formal approach (RAND corporation) uses computer-guided modelling interface
(Lempert et al, 2003: Groves and Lempert, 2007) and XLRM framework . Test
across thousands or even millions of computer-generated future states

But also application to climate model uncertainy (only) - Dessai 2007; Hallegatte,
2012
Formal RDM

Analysis begins by structuring the problem (and
performance measures), then propose alternative strategies

Characterises the uncertainties, assigning values for each
variable using stakeholder consultation / other

Assesses each strategy over a wide range of computer
generated scenarios (thousands or millions)

Analyse performance measures indicating if pre-selected
desirable outcomes have been achieved, i.e. to identify a
robust, well performing strategy

Summarise key trade-offs among promising strategies e.g.
suggest one strategy over another as long as future
precipitation likely to remain above threshold
Groves et al, 2008
Case Study

Lempert and Groves (2010) Southern California’s Riverside County Inland
Empire Utilities Agency (IEUA) and Urban Water Management Plan (UWMP)

Identify alternative strategies, using decision-tree framework within a Water
Evaluation And Planning (WEAP) model environment

Identify potentially significant uncertainties

Adaptive strategies were presented using decision-tree framework

Performance was measured using projected present value (PV) costs in USD
billions against PV shortage costs.

Findings - many local resource development options, including increasing
efficiency and storm water capture, not only reduce vulnerability to climate
change, but offer cheaper options in the future than acquiring more expensive
imported supplies.
Advantages/Disadvantages

Good when future uncertainties are poorly characterised and probabilistic
information is limited or not available,

However, lack of quantitative probabilities with scenarios can make analysis a
more subjective decision, influenced by stakeholders’ perception

Formal approach very data and resource intensive, and requires expert
application

Useful for identifying early no regret options, but complex when move to
multiple time frames and inter-linkages

Most interest is in ‘light-touch’ approach, e.g. just climate projection
uncertainty, but misses the issue of wider uncertainty
Portfolio Theory and Analysis

Evolved from financial markets and use of portfolios of financial assets to
maximise the return on the investments subject to a given level of risk

Principle is spreading investments over a range of asset types (diversification)
spread risks at the same time, reducing dependence on a single asset.

PA is a decision support tool that highlights the trade-off between the returns on
an investment and the riskiness of that investment, i.e. helps in picking portfolios

PA can help in selecting a range of options that, together, are effective over
range of possible projected future climates

(rather than one option that is best suited to one possible future climate)
Advantages/Disadvantages

Main strength - provides a structured way of assessing climate change
adaptation whilst accounting for (climate change) uncertainty in a way that
consideration of individual adaptation options does not allow.

Returns can be measured widely: physical effectiveness, cost effectiveness, or
economic efficiency

Disadvantage relies on availability on effectiveness (return) and (co-variance)

Requires probability, ranking of uncertainty (or else assumptions that
different outcomes equally likely)

Harder to assess effectiveness and co-variance for some impact/risk
categories, e.g. where attribution to climate change is less straightforward,
effectiveness of adaptation actions are not easily measured or identified.

Additional complexity of option inter-dependence
Some overall observations

While powerful, all of these are technically complex approaches to apply

They work well for specific applications – not all cases

For formal application, large resources involved – and considerable expertise

Likely to mean niche applications, where large potential costs are involved

Unlikely to be generally applicable, i.e. not possible to download off the web

Most interesting question is whether possible to take the general concepts and
simplify for more general application, or use elements (e.g. decision trees)

Help decision makers ‘muddle through’ complex decisions

Guidance on when approaches useful – which techniques for different problems
Economic decision making under uncertainty
Decision Support Tool
Brief Description
Usefulness & limitations in climate
adaptation context
Real Options Analysis
(ROA)
ROA extends the principles of CBA to allow
economic analysis of learning, delay and future
option values, thus providing context to decisions
under uncertainty. It can also provide economic
analysis on the benefits of flexibility and value
information that reduces the uncertainty relating
to climate risks.
Most useful for:
-Large irreversible capital intensive
investment, with potential for learning
(especially when long
decision/construction lifetime;
Climate risk probabilities are known or range is bounded
Informal ROA (decision
trees)
Informal application of the decision trees used in As above, but wider application due to
ROA with formal economic appraisal (rather than less focus on probabilities, and economic
formalised ROA analysis).
option value.
Portfolio Analysis (PA)
PA allows an explicit trade-off to be made
between the return – measured e.g. in net
benefit terms (from CBA) – and the uncertainty of
that return – measured by the variance – of
alternative combinations (portfolios) of adaptation
options, under alternative climate change
projections.
Robust Decision Making RDM quantifies the trade-offs implied by
(RDM)
adopting adaptation options that address
possible vulnerabilities under future uncertainty,
compared with other criteria such as economic
efficiency, stress testing options against large
numbers of future scenarios.
Most useful when:
-A number of adaptation actions are likely
to be complementary in reducing climate
risks.
Most useful when:
-Deep uncertainty.
Scenarios of alternative climate, socioeconomic and vulnerability futures can be
constructed, and data for their
characterisation is available.
Coming soon…..