| Tool | Strengths | Weaknesses |
Cost-Benefit
Analysis | - Provides
direct analysis of economic benefits,
justification for
action, and optimal solutions. - Well known and
widely applied.
| - Difficulty of
monetary valuation for non-market
sectors and non-technical
options. - Uncertainty usually limited to
probabilistic risks.
|
Cost-
Effectiveness
Analysis | - Benefits
expressed in physical terms (not
monetary) thus applicable
to non-market
sectors. - Relatively simple
to apply and easily
understandable ranking and outputs. - Use
of cost curves can assess policy targets
with least-cost
optimisation. - Used for mitigation, thus widely
recognised and
resonance with policy makers
| - Benefits
can be difficult to identify and single
metric does not
capture all costs and benefits.
Less applicable
cross-sectoral / complex. - Works best with technical
options, and often
omits capacity building and soft measures. - Sequential
nature of cost curves ignores interlinkages
and potential
for portfolios. - Does not lend itself to the
consideration of
uncertainty, as works with central tendency.
|
Multi-criteria
analysis | - Combines
quantitative and qualitative data, and
monetary and
non-monetary units, thus
applicable where quantification is
challenging. - Relatively simple and transparent, and
relatively
low cost / time requirement. - Expert
judgement can be used very efficiently,
and involves
stakeholders, thus can be based
on local knowledge.
| - Results
need further interpretation and
elaboration in more detailed
studies. - Different experts may have different
opinions, i.e.
subjectivity involved. - Stakeholders
may have lack of knowledge and
can miss important options. - Analysis
of uncertainty is often qualitative and
subjective.
|
Real Options
Analysis | - Assesses
value of flexibility and learning, in
quantitative and
economic terms. - Decision trees conceptualise and
visualise the
concept of adaptive management.
| - Data
and resource intensive, with high
complexity and
expert input. - Data a potential barrier,
(probabilistic climate,
quantitative and economic
information). - Identification decision points often
complex.
|
Robust
Decision
Making | - Assesses
robustness rather than optimisation.
- Applicable
where probabilistic information is low
or missing, or
climate uncertainty is high. - Can work with physical
or economic metrics,
enhancing application across sectors.
| - Lack
of quantitative probabilities can make more
subjective,
influenced by stakeholders. - The formal application
has a high demand for
quantitative information, computing
power, and
requires a high degree of expert knowledge.
|
Portfolio
Analysis | - Assesses
portfolios, which analysis of individual
adaptation options
not allow. - Measures “returns”
using various metrics,
including physical or economic, thus
broad
applicability. - Use of the
efficiency frontier an effective way of
visualising results
and risk-return trade-offs.
| - Resource
intensive and needs expert
knowledge. - Relies
on the availability of quantitative data
(effectiveness and
variance/co-variance). - Requires probabilistic
climate information, or an
assumption of likelihood
equivalence. - Issues of inter-dependence between
options.
|
Adaptive
Management
/
Iterative
Risk
Assessment
/
Adaptation
turning
points | - Process
of monitoring, research, evaluation and
learning that avoids
irreversible decisions and
encourages learning to adjust
decisions over
time. - Uses scenarios to
delineate uncertainties not to
predict the future. - Is
more policy orientated and flexible in
objectives and
appraisal methods. - Encourages discussion about
(un)acceptable
change and definition of critical indicators.
| - Challenging
when multiple risks acting together,
or indirect links to CC. - Thresholds
are not always easy to identify,
especially those that are
poorly defined. - Focuses on existing management
objectives.
Unknown impacts and new challenges may be
overlooked
/ difficult. - Loses simplicity for communication
less-well
defined thresholds and multiple drivers.
|
Analytic
Hierarchy
Process | - Can
be applied where elements difficult to
quantify or not
directly comparable. - Relatively simple approach and
produces simple
rankings that are easy to communicate. - Does
not require information on economic
benefits so wide
applicability. - Can accommodate a wide range of
disciplines,
opinions and groups of people who do not
normally
interact.
| - Results change as new
options are considered.
- Becomes complicated if lots
of criteria and
options are considered. - Subjective
scale can lead to biases.
- Trans-disciplinary
capacity building can be
undermined at the cost of the
expediency. - Software can conceal conflicting value
judgments.
|
Social Network
Analysis | - Understanding
of socio-institutional structures,
actors, linkages and
decision framing, to improve
information and knowledge
transfer. - Qualitative SNA quick and easy and
encourages
participation across diverse viewpoints and
actors - Quantitative
SNA provides quantitative
information and correlations to
understand
network variables
| - Subjective
bias.
- Networks have artificial boundaries.
- Does
not have a temporal or spatial dimension.
- Time-consuming,
intensive process (quantitative).
- Design of process
is critical to get as many
differing viewpoints as possible.
|
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