Download Gary Yohe - Evaulating Adaptation Methods

A General Method for Evaluating Adaptation Options in an Integrated Context
Gary Yohe
Wesleyan University
May 10, 2002
This module on evaluating adaptation options will lead participants through a practically
motivated evaluation method designed to
(1) organize evaluative thoughts about adaptations to climate-related stresses,
(2) identify sources of weakness and strength that may apply to specific adaptations to a particular
source of stress or more pervasively to multiple adaptations to multiple stresses, and
(3) explore general robustness in adaptive capacity against a range of not-implausible futures.
It draws heavily from Yohe and Tol (2002), and it derives its utility from the UNDP-GEF Adaptation
Policy Framework and the organization of the AIACC Training Program. More broadly, its application can
uncover complementarities and conflicts between adaptation options and programs designed to promote
progress against other sustainable development objectives and thereby help assess decision-making
priorities.
Major Take-home Lessons:
1.
Adaptation can matter – can significantly reduce impacts of a climate stress (measured in currency
or other indicators); and it can “buy time” for further adaptation to significant change.
2.
It is critical to keep track of who adapts to what. Adaptation is path dependent and location
specific.
3.
There is no need to be tied to regional climate scenarios for adaptation studies, although reviewing
a range of not-implausible futures along those scenarios can provide a context within which to
evaluate the efficacy and robustness of adaptations.
4.
Adaptation to current climate variability can provide insight into how to adapt to climate change;
the key to climate change may be that these adaptations may have to cope with more frequent
episodes of large variability.
5.
Coping ranges and thresholds of “tolerable” variability can be a useful tool in analyzing the need
for adaptation and in portraying its potential for reducing vulnerability. Dynamic changes in these
thresholds can be a useful tool in conceptualizing how future changes might unfold and be
recognized.
6.
Reviewing the determinants of adaptive capacity and how they support or hinder adaptation
options can help uncover sources of strength and weakness. It can also help discover which
strengths and weaknesses are site specific (micro scale) and which are more ubiquitous (macro
scale).
7.
Contemplating a range of not-implausible unintended consequences and ancillary responses can
provide insight into how adaptations to various stresses might interact positively or negatively.
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1.
A Basic Framework [drawn from Yohe and Tol (2002)]
The authors of Chapter 18 of the Report of Working Group II to the Third Assessment Report
(TAR) of the Intergovernmental Panel on Climate Change (IPCC) focused considerable attention on the
role of adaptation in judging the economic implications of climate change and climate variability [IPCC
(2001)]. Four major conclusions can be drawn from their work:
1.
The vulnerability of any system to an external stress (or collection of stresses) is a function of
exposure, sensitivity, and adaptive capacity.
2.
Human and natural systems tend to adapt autonomously to gradual change and to change in
variability.
3.
Human systems can also plan and implement adaptation strategies in an effort to reduce potential
vulnerability or exploit emerging opportunities even further.
4.
The economic cost of vulnerability to an external stress is the sum of the incremental cost of
adaptation plus any residual damages that cannot be avoided.
Moreover, the authors of Chapter 18 emphasized that adaptive capacity varies significantly from system to
system, sector to sector and region to region. This means that it is critically important, in any assessment of
adaptation, to identify immediately who is adapting to what. Moreover, the determinants of adaptive
capacity include a variety of system, sector, and location specific characteristics; they are recorded in Table
1. It is, though, essential to note that exposure to variability and to extreme events is an important source of
vulnerability. In fact, systems typically respond to variability and extreme events before they respond to
gradual changes in the mean. This simple observation can be the key to bringing concerns about lont-term
climate impacts and adaptation issues to the fore in discussions with decision-makers who come to the table
with much shorter time horizons.
In summary, the vulnerability cum adaptation literature recognizes explicitly that systems’
environments are inherently variable from day to day, month to month, year to year, decade to decade, and
so on [see Mearns, et al. (1997) and/or Karl and Knight (1998)]. It follows that changes in the mean
conditions that define those environments can actually be experienced most noticeably through changes in
the nature and/or frequency of variable conditions that materialize across short time scales and that
adaptation necessarily involves reaction to this sort of variability. This is the fundamental point in Hewitt
and Burton (1971), Kane, et al. (1992), Yohe, et al. (1996), Downing (1996) and Yohe and Schlesinger
(1998). Some researchers, like Smithers and Smit (1997), Smit, et al. (1999), and Downing et al (1997),
use the concept of “hazard” to capture these sorts of stimuli. These authors claim that adaptation is
warranted whenever either changes in mean conditions or changes in variability have significant
consequences.
1.
The Coping Range.
For most systems, change and variability over short periods of time fall within a “coping range” –
a range of circumstances within which, by virtue of the underlying resilience of the system, significant
consequences are not observed [see Downing, et al (1997) or Pittock and Jones (2000)]. There are,
however, limits to resilience for even the most robust of systems. As a result, it is important to understand
the boundaries of systems’ coping ranges – thresholds beyond which the consequences of experienced
conditions become significant. Coping ranges are not necessarily fixed over time, of course. Indeed, de
Vries (1985), de Freitas (1989) and Smit, et al. (2000) all make it clear that judging adaptive capacity
depends critically upon both defining a coping range and understanding how the efficacy of any coping
strategy might be expanded by adopting new or modified adaptations.
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The various panels of Figure 1 display the characteristics of a hypothetical context within which
we can productively illustrate how coping-ranges might be employed to help assess adaptive capacity.
Panel A establishes a point of departure by portraying annual flows in a fictitious river over the past 50
years; flow, here, simply represents a specific, climate sensitive environmental variable that carries
potentially enormous significance for community located on the river bank. A significant degree of interannual variability is depicted there around a static mean; this is the no-climate change scenario. Panel A
also portrays hypothetical upper and lower thresholds that define a coping capacity at a particular location
along its banks. The upper threshold that has been set arbitrarily at 120% of the 50-year mean could, for
example, indicate annual flows above which significant flooding might occur with unacceptable frequency.
The lower threshold, meanwhile set at 80% of the 50-year mean, might indicate annual flows level below
which existing irrigation systems would be rendered temporarily inoperable. Notice that people living in
this location would expect to see 5 years of serious flooding over a 50-year period and only 1 year in which
irrigation would be interrupted.
Panel B portrays the same series with a gradually increasing mean added to the historical trend –
the result, perhaps, of climate change or perhaps of changes in upstream land use practices. Even without
any change in variation around the mean, the frequency of flooding would climb to 7 years over the 50-year
period; note, as well, that the frequency of serious interruptions of irrigation practices would be unchanged.
Panel C adds expanding variability from whatever source to the mix. Its contribution increases the
frequency of floods even more (to 12 years over half a century), and it adds one additional dry year to the
series (a reflection of what would fundamentally be an ambiguous effect for irrigation relative to the
historical context).
The concept of coping capacity can now be employed to portray the potential benefit of a variety
of possible adaptations. Three different adaptation options that would either alter the flow or adjust the
coping indicated thresholds can illustrate how:
Option A: Construction of a series of protection levies.
Figure 2 portrays the possible effect of building protective levies by expanding the upper
threshold of the coping range. The frequency of flooding would be reduced in all cases, but
there would be no change in exposure to flows that fall below the lower threshold. Flooding
could be eliminated, at least for the historical period, if the levies were large enough; but levies
constructed to accommodate historical experience could still be overwhelmed if mean flow or
variability rose unexpectedly over time. Construction and maintenance costs would be incurred,
to be sure, but local environmental effects, local amenity costs, and increased flooding
downstream of the levies could also be experienced.
Option B: Periodically dredging the river.
Figure 3 portrays the hypothetical effects of periodic dredging as a saw-toothed pattern for the
upper threshold. Dredging would allow the river to accommodate more water and thereby
increase the upper threshold, but this benefit would depreciate over time as silt re-deposits on
the riverbed. To maintain long-term benefit, therefore, dredging would have to be repeated on a
regular basis. This option also holds the potential of eliminating exposure to flooding, but only
for short periods of time. On the other hand, opting for dredging regime could allow managers
to accommodate unexpected changes in the underlying mean or variance by increasing or
decreasing the frequency of dredging operations. The recurring cost of dredging plus some
environmental damage could be expected as well as increased flooding risk downstream.
Option C: Building a dam upstream.
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Figure 4 portrays the effect of building a dam upstream by reducing the variability in observed
river flow at our location. This option would, in particular, allow managers to release water
from the dam during low flow periods and retain water during high flow periods so that the
actual flow below the dam would be a moving average over the past (e.g.) four years. Exposure
to flooding could, with a dam of sufficient capacity, limit variability in actual flow to the size of
the original coping range of Figure 1. This option therefore holds the potential of eliminating
vulnerability to crossing either the high or the low threshold; but it, too, could fail to
accommodate a long-term change in the underlying mean or variance. Construction and
maintenance cost would again be incurred, as well as significant environmental impact
upstream; but energy, recreation, and tourism benefits could be created.
These adaptations would clearly have different effects, but diversity is a fact of life; and their relative
efficacy would surely change across alternative scenarios of future river flows. Indeed, it is instructive to
superimpose the varied futures displayed in Figure 1 on the dynamic changes in coping range depicted in
Figures 2 and 3 with and without the inter-annual smoothing effect of building an upstream dam. Two
general points can now be made from this hypothetical example. First of all, the range of possible futures
could expand almost exponentially if we were to explore combinations and permutations of multiple
adaptation options and a wide range of “not-implausible” climate futures. Secondly, any indicator of
coping capacity would have to be able to handle this sort of diversity in a consistent and comparable way.
The concept of adaptive capacity and the potential contribution of any adaptation option to that capacity
can support an organizing methodology designed to do just that.
2.
Scale Issues and the Determinants of Adaptive Capacity
Some of the determinants of adaptive capacity, like the set of available, applicable and appropriate
technological options (Determinant 1 in Table 1), operate on micro scales that are precisely location
specific even if the complete set of possible remedies were larger. If one were concerned about flood
control, for example, available adaptations would be determined by the local conditions of the river bed and
available engineering knowledge; and this knowledge may be restricted to indigenous knowledge on the
one hand or informed by worldwide consultants on the other.
Other determinants operate on macro-scales in which national or regional factors play the most
significant role. Determinants 2 through 6 (in Table 1) should all have large macro components to them
even if their micro-scale manifestations could vary from location to location or even from adaptation option
to adaptation option. Resources (Determinant 2) could be distributed differently across specific locations,
but adaptive capacity may be more sensitive to larger scale distributional issues across different locations.
The essential questions here focus on whether sufficient funds are available to pay for adaptation and
whether the people who control those funds are prepared to spend them on adaptation. Empirical results
have shown that poorer people are more likely to fall victim to natural catastrophes than are richer people
and that more densely populated areas are more vulnerable to these events. Moreover, a positive
relationship between income inequality and vulnerability can be gleaned from international comparisons;
i.e., people in more egalitarian societies seem to be less likely to fall victim of natural disasters than are
people in a society with a highly skewed income distribution. This result is, of course, consistent with the
negative correlation between income and vulnerability, and it suggests that measures designed to highlight
a skewed distribution would confirm the notion that the poorest communities within a country would face
similar resource deficiencies when it comes to protecting themselves. Some other explanatory variables are
insignificant, but it is important to note that health care and education have strong positive correlations with
per capita income.
Macro-scale and even international institutions (Determinant 3) could certainly matter even at a
micro level, especially in determining how decisions among various adaptation options might be made and
who has access to the decision-making process. For example, the World Bank follows certain procedures
in its investment decisions, and adaptation projects in countries seeking World Bank support must satisfy
Bank criteria before even being considered. The European Union also has a framework (on procedures as
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well as consequences) into which all water management projects must fit, so macro-scale influences can be
felt even in developed countries. On the other side of the coin, though, adaptation projects in other places
can be decided and implemented completely according to local custom alone. The stock of human capital
(Determinant 4) could be a local characteristic, as well, but its local manifestation would likely be driven in
large measure by macro-scale forces such as national support of local education.
The stock of social capital (Determinant 5) and efficacy of risk-spreading processes (Determinant
6) should be largely functions of macro-scale structures and rules; but they could again take different forms
from location to location and option to option. Property rights may be well defined through national
institutions, and they may be the basis of private insurance markets; but issues of moral hazard and adverse
selection may or may not be particularly severe in one location or another. Risk can be spread through
national markets for commercial insurance and the international reinsurance markets, but some companies
may refuse to sell flood insurance. Risk can also be spread through mutual obligations in the extended
family, the strength of which varies between cultures and city and countryside.
By way of contrast, Determinants 7 (managing information) and 8 (attributing signals of change to
their sources) listed in Table 1 may have some general macro-scale foundations, but their primary import
would be felt on a micro-scale. Indeed, decision-rules and public perceptions could take on forms that
would be quite particular to the set of available options.
3.
Devising a Workable Index of Coping Capacity to a Specific Source of Stress.
Suppose that attention had been focused on a range of adaptations that might be applied to
ameliorate exposure or sensitivity of a specific community or system to a specific climate stress. Modeled
after the UNDP-GEF Adaptation Policy Framework, Part A of Table 2 suggests how this focusing exercise
might be accomplished. The construction of an index of the potential contribution of any adaptation option
(to be denoted by j) to an indicator of overall coping capacity for that stress (denoted by PCC j ) can begin
with a step by step evaluation of feasibility factors – index numbers that are judged to reflect its strength or
weakness vis a vis the last seven determinants of adaptive capacity. These factors will be subjective values
assigned from a range bounded on the low side by 0 and on the high side by 5 according to systematic
consideration of the degree to which each determinant would help or impede its adoption. Let these factors
be denoted by ffj (k) for determinants k = 2, …, 8 from Table 1. An overall feasibility factor for adaptation
(j) should be reflected by the minimum feasibility factor assigned to any of these determinants; i.e.,
FFj  min{ffj (2), … ffj (8)}.
(1)
Each factor inserted into equation (1) would, in particular, suggest whether the local manifestations of each
determinant of adaptive capacity would work to make it more or less likely that adaptation (j) might be
adopted. A low feasibility factor near 0 for Determinant #k would, for example, indicate a significant
shortcoming in the necessary preconditions for implementing adaptation (j), and this shortcoming would
serve to undercut its feasibility. A high feasibility factor near 5 would indicate the opposite situation;
assessors would, in this case, be reasonably secure in their judgement that the preconditions included in
Determinant #k could and would be satisfied. Notice that the structure of equation (5) makes it clear that
high feasibility factors for a limited number of determinants would not be sufficient to conclude that
adaptation option (j) could actually contribute to sustaining or improving an overall coping capacity. The
overall feasibility of option (j) could still be limited by deficiencies in meeting the requirements of other
determinants. Panel B of Table 2 highlights some (but not necessarily all) of the questions that need to be
confronted in evaluating each feasibility factor.
The ability of adaptation option (j) to, in fact, influence a system’s exposure or sensitivity to an
external stress can meanwhile be reflected in an efficacy factor EF j – a subjective index number assigned
from a range running from 0 to 1. As suggested by the questions that need to be confronted in evaluating
efficacy listed in Part C of Table 1, this factor will reflect the likelihood that adaptation (j) will perform as
expected to influence exposure and/or sensitivity. The potential contribution of any adaptation to a
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system’s social and economic coping capacity can then, finally, be defined as the simple product of its
overall feasibility factor and its efficacy factor; i.e.,
PCCj  {EFj }{FFj }.
(2)
Any method that focuses attention on the feasibility and efficacy of adaptation options through equations
(1) and (2) can be extended to handle the complication of interactions across multiple options designed to
mitigate vulnerability to one source of stress and/or multiple sources of stress. The key here would involve
simply keeping track of the degree to which options might serve to complement each other, to substitute for
each other, or perhaps even to work at cross-purposes to each other.
To be operational, of course, a feasibility-based evaluation of an adaptation’s potential
contribution to coping capacity must be informed by a compounding evaluation of feasibility and efficacy
for each possible adaptation. The method described in equations (1) and (2) suggests one way in which this
might be accomplished. Notice, in particular, than an adaptation option could receive a low coping
capacity indicator for many reasons. It might, by virtue of shortcomings in meeting the determinants of
adaptive capacity, receive a low overall feasibility index. An adaptation could, as well, receive a low
indicator if it were relatively ineffective in reducing exposure and/or sensitivity or if the mechanism by
which it could accomplish its tasks were uncertain. It remains only to describe the how the adaptationbased PCCj can be combined into an indicator of overall coping capacity.
Suppose, for the sake of argument, that m potential adaptation options have been identified for a
specific vulnerability and that they have been assigned potential coping capacity indicators {PCC1 , …,
PCCm}. The spirit of equations (1) and (2) suggests that
CCI  max {PCC1 , …, PCCm}
(3)
would be a consistent and workable indicator. It would make sense to order the adaptation options in terms
of their PCCj so that CCI = PCC1. That accomplished, an index of tentative robustness of overall coping
capacity in the face of any vulnerability (denoted by R) can be defined as the ratio of the average of the
PCCj of the next two highly rated alternatives and PCC1:
R = {[ PCC2 + PCC3 ] / 2 PCC1}.
(4)
A high R ratio would indicate that several options of relatively comparable potential were available, but a
low ratio would indicate a lack of diversity that could prove harmful if the assessment of adaptation were
flawed.
4.
An Egyptian Application
Turning now to an Egyptian case study, Strzepek, et al (2001) highlighted two alternative socioeconomic scenarios that were differentiated in qualitative terms. One, a high capital scenario, would be
supported by relatively efficient government investment, attractive investment opportunities for foreign
capital, and significant domestic investment from the private sector. The alternative scenario envisioned
lower growth because government investment continued to focus on “mega-projects” that crowded-out
domestic investment and reduced the attraction to foreign sources of investment.
Panels A and B in Table 3 offer subjective views of feasibility and efficiency factors for three
adaptations along two “not-implausible” climate scenarios. Figure 5 displays these as scenarios (3) and (9)
in terms of flow along the Nile; they are displayed in the context of seven other representative scenarios
that were created by inserting climate scenario results from COSMIC into a hydrological model of the
entire Nile Basin. In creating Table 3, feasibility factors were judged for each adaptation along two
development scenarios. Different readers may assign higher or lower numbers to any or all of them, but it
is most important for present purposes to notice that the availability of resources is reduced in the low
development scenario for recycling and drip irrigation and not for groundwater drilling. This may appear
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odd, but a large groundwater project is just the sort of “mega-project” that would appeal to central
authorities even along a slow-growth trajectory.
Efficiency factors were meanwhile judged in terms of their abilities to handle climate-driven
shortfalls in the flow of the Nile along two scenarios in terms of economic activity and food selfsufficiency. Underlying estimates of economic activity were supported GDP estimates and efficacy in
maintaining self-sufficiency were judged by comparing post adaptation outcomes under climate change
with outcomes consistent with the current climate. Analysis showed, for example, that self-sufficiency
consistent with the current climate could apparently be sustained in 2067 with adaptation along either
development scenario for the modest climate change of scenario (3). Under the severe climate change of
scenario (9), however, a different story emerged. Along the high development scenarios, modest
adaptation could sustain only 4-tenths of the 60 to 100% self-sufficiency levels that could be achieved if the
climate did not change, and groundwater pumping would only increase that fraction to 6-tenths. The news
was even worse along the low development scenarios where only 1-tenth of anticipated levels could be
maintained.
Combining these factors according to equations (2) and (3) shows the sensitivity of overall coping
capacity to alternative climate and socio-economic futures; but measures of coping capacity also depend on
the metric. The overall index for modest climate change along scenario 3 runs from 2 to 4 for low and high
development scenarios, respectively, when self-sufficiency is the metric; and it runs from 0.4 through 2.4
for the extreme climate future of scenario 9. The corresponding ranges are 2.8 to 4 and 2.8 to 3.6 for
economic activity. Moreover, Table 3 makes it clear that maximally effective adaptation to severe climate
change measured in terms of either metric will require groundwater drilling at some point in the future.
5.
Possible Outcomes and Insights.
Working through evaluation exercise can produce a wide range of possible outcomes and insights
that can be summarized best by constructing mental portraits of the feasibility factors for each adaptation
option – histograms of the subjective values assigned for each determinant. If some determinants display
consistently low values, for example, then fundamental weaknesses will have been discovered; and
examining their sources could pay dividends. Some of these sources may operate on micro scales so that
location specific interventions would be appropriate; but other sources may have their roots in structures or
influences that operate on a macro scale. Examining these macro scale determinants can offer insight in
two directions. On the one hand, researchers can possibly discover that the best interventions for
facilitating adaptation may lie well beyond the boundaries of any single climate sensitivity. On the other
hand, judgments about strength of the determinants for any adaptation can be sensitive to judgments about
future socio/economic development trajectories.
Working through the evaluation exercise can also provide evidence to support a range of
general conclusions that extend beyond the boundaries of any specific application. Each can be
viewed as an informed hypothesis to be examined in any application; it follows that explaining
confirmation or contradiction can serve as a way of making certain that results make sense (i.e.,
pass the laugh test). For example, early application of the method to the Egyptian case reported
above and other contexts suggests that
(1) Adaptation can make a significant difference on a macro scale, especially for pessimistic
climate scenarios.
(2) Socio-economic context matters in determining adaptive capacity, especially on a macro
scale. Scenarios hampered by inefficient investment displayed, for example, diminished
capacities to adapt.
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(3) The goals of adaptation at a macro scale can be denominated in terms of objectives other
than increased economic activity; food security was the major beneficiary of adaptation
to all climates but the most severe in the Egyptian case, for example.
(4) The value of information that supports early differentiation between two strikingly
different climate futures can be significant even when it is denominated in non-economic
terms.
(5) Early preparation for adaptation can be important because macro-scale adaptations can
involve capital reallocation between sectors in anticipation of large future investments in
adaptation infrastructure.
(6) Planning for bad news and adapting to good can be a better choice than the other way
around.
(7) Working to expand the potential of some options to increase adaptive capacity can create
rigidities or cause systems to under-prepare for adopting other options. These omissions
can reduce the capacity to cope with more extreme climate futures because the first set of
adaptations may be overwhelmed even as more efficacious alternative adaptations
become less feasible.
(8) Experimenting with the value of information calculations suggests that
a.
Increased short-term variance increases value of information and delays
midcourse corrections; it is even better to plan for the worst in these cases.
b.
Increased costs increase the value of information, but they can be neutral with
respect to timing of adjustment decisions.
c.
Differentiating between climate scenarios that track close to each other in the
near-term delays the adjustment decision. These delays increase the value of
information even though the costs of being wrong in any one year would be
relatively smaller.
It must finally be emphasized in that highly ranked adaptations for a specific climate stress must be
evaluated in the broader context of opportunity cost. Implementing any adaptation would, more to the
point, consume financial and human resources that could be expended elsewhere; and so progress in
reducing vulnerability to one climate stress could come at the expense of less progress in the pursuit of
other goals. It follows that two final evaluation steps must be taken. The first must subject adaptations
with high coping capacity indices to the decision framework that would be employed by the decisionmaker. The artificial nature of the indexing scheme suggested here does not, however, necessarily combine
the cost of implementation with the potential efficacy in a way that is consistent with a more involved
decision process. It is, for example, possible that the expected discounted benefits of a highly ranked
adaptation might not exceed the expected discounted cost of its implementation. Moreover, a second step
must examine at least qualitatively the complementarities and conflicts of attractive adaptations to any
specific stress to initiatives designed to promote other social objectives (adaptations to other climate related
stresses, development objectives, and the like). Even adaptations with positive net expected benefits may
not be adopted if their opportunity costs, in terms of diverted resources or secondary effects, are too high.
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References
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and Disaster, Clarke, J.I., Curson, P., Kayastha, S.L., and Nag, P. (eds), Blackwell, Oxford, 98-113.
de Vries, J., 1985, “Analysis of Historical Climate-Society Interaction” in Climate Impact Assessment,
Kates, R.W., Ausubel, J.H., and Berberian, M. (eds), John Wily and Sons, New York, 273-291.
Downing, T.E. (ed), 1996, Climate Change and World Food Security, Springer, Berlin, 662 pages.
Downing, T.E., Ringius, L, Hulme, M. and Waughray, D., 1997, “Adapting to Climate Change in Africa”,
Mitigation and Adaptation Strategies for Global Change 2: 19-44.
Hewitt, J. and Burton, I., 1971, The Hazardousness of a Place: A Regional Ecology of Damaging Events,
University of Toronto, Toronto, 312 pages.
Intergovernmental Panel on Climate Change (IPCC), 2001, IPCC, 2000 – Impacts, Adaptation, and
Vulnerability – The Contribution of Working Group II to the Third Scientific Assessment of the
Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge.
Kane, S.J., Reilly, J., and Tobey, J., 1992, “An Empirical Study of the Economic Effects of Climate
Change on World Agriculture”, Climatic Change 21: 17-35.
Karl, T.R. and Knight, R.W., 1998, “Secular Trends of Precipitation Amount, Frequency and Intensity in
the United States”, Bulletin of the American Meteorological Society 79: 231-241.
Mearns, L.O., Rosenzweig, C., and Goldberg, R., 1997, “Mean and Variance Change in Climate Scenarios:
Methods, Agricultural Applications and Measures of Uncertainty”, Climatic Change 34: 367-396.
Pittock, B. and Jones, R.N., 2000, “Adaptation to What and Why?”, Environmental Monitoring and
Assessment 61: 9-35.
Smit, B., Burton, I., Klein, R.J.T., and Street, R., 1999, “The Science of Adaptation: A Framework for
Assessment”, Mitigation and Adaptation Strategies for Global Change, 4: 199-213.
Smit, B., Burton, I., Klein, R.J.T., and Wandel, J., 2000, “An Anatomy of Adaptation to Climate Change
and Variability”, Climatic Change 45: 223-251.
Smithers, J. and Smit, B., 1997, “Human Adaptation to Climatic Variability and Change”, Global
Environmental Change 7: 129-146.
Strzepek, K., Yates, D., Yohe, G., Tol, R., and Mader, N., 2001, “Constructing ‘Not-Implausible’ Climate
and Economic Scenarios of Egypt”, Integrated Assessment 2: 139-157.
Yohe, G., Neumann, J., Marshall, P., and Amaden, H., 1996, “The Economic Cost of Greenhouse-Induced
Sea-Level Rise for Developed Property in the United States”, Climatic Change 32: 387-410.
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Working Definition of Adaptive Capacity”, Global Environmental Change, in press.
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Enhancing the Capacity of Developing Countries to Adapt to Climate Change, University Press, London.
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Table 1
One Researcher’s List of the Determinants of Adaptive Capacity
1.
The range of available technological options for adaptation,
2.
The availability of resources and their distribution across the population,
3.
The structure of critical institutions, the derivative allocation of decision-making
authority, and the decision criteria that would be employed,
4.
The stock of human capital including education and personal security,
5.
The stock of social capital including the definition of property rights,
6.
The system’s access to risk spreading processes,
7.
The ability of decision-makers to manage information, the processes by which these
decision-makers determine which information is credible, and the credibility of the
decision-makers, themselves, and
8.
The public’s perceived attribution of the source of stress and the significance of exposure
to its local manifestations.
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Table 2
Some Questions for Implementing an Evaluation Method
A. Background and Context
1.
2.
3.
4.
Identify vulnerable sector or system (stakeholders, experts, significance in development plans,
etc…)
Assess current vulnerability
a. Climate risks, impacts, potential damages
b. Socio-economic drivers
c. Natural resource drivers
d. Adaptive experience and capacities
e. Sustainable development needs
Characterize future conditions
a. Climate trends
b. Socio-economic trends
Prospective adaptations
a. Who adapts to what?
b. Specific adaptation options
B. Evaluating How the Determinants of Adaptive Capacity Support a Coping Capacity –
Illustrative Questions for any Specific Prospective Adaptation (The numbering refers to the
determinants listed in Table 1)
2.
Resources:
a. How much would it cost?
b. How would the costs be distributed over time– fixed start-up versus on-going?
c. Sources of funding (private, public, local, national)?
d. How are resources distributed vis a vis those sources?
3.
Decision Criteria – governance:
a. Who would take the responsibility?
b. Would that person, persons or institution accept responsibility?
c. Relative importance vis a vis other responsibilities?
d. Decision criteria (precautionary principle, cost-benefit, cost effectiveness, consistency
with other objectives, etc…)?
4.
Human Capital:
a. Are there autonomous elements?
b. Monitoring and implementation requirements?
5.
Social Capital:
a. Property rights and claims to adaptation?
b. Obligation to respond?
6.
Access to Risk Spreading:
a. Is financial insurance against damage available and/or appropriate?
b. Does the adaptation incorporate other forms of risk spreading?
7.
Managing Information:
a. What information is required?
b. Can signal be separated from noise?
c. Can information and risk be communicated?
d. How might success or failure be evaluated and performance otherwise monitored?
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8.
Public Perception:
a. Would the public at large, or the relevant powerful portions of the public, support the
adaptation?
b. Does the public accept the causality between the adaptation and reduced exposure of
sensitivity?
C. Evaluating Efficacy
1.
2.
3.
Can the adaptation be expected to work as planned?
How would the adaptation change the frequency of crossing critical coping thresholds?
How would this frequency change over time?
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Table 3; Panel A
Potential Capacity Indices for Adaptation Options – High Development Scenarios
Determinant
Recycling
Municipal
Water
Drip
Irrigation
Groundwater
Drilling
Resources
4
4
5
Institutions
4
4
5
Human Capital
4
4
5
Social Capital
5
5
5
Risk Spreading
4
4
4
Manage Information
4
4
4
Public Perception
5
5
5
Feasibility Factor
4
4
4
Climate (3)
1
1
1
Climate (9)
0.2
0.2
0.9
Climate (3)
4
4
4
Climate (9)
0.8
0.8
3.6
Climate (3)
1
1
1
Climate (9)
0.4
0.4
0.6
Efficacy Factor – GDP
Potential Coping Index – GDP
Efficacy Factor – Food Self-sufficiency
Potential Coping Index – Food Self-sufficiency
Climate (3)
4
4
4
Climate (9)
1.6
1.6
2.4
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Table 3; Panel B
Potential Capacity Indices for Adaptation Options – Low Development Scenarios
Determinant
Recycling
Municipal
Water
Drip
Irrigation
Groundwater
Drilling
Resources
2
2
5
Institutions
4
4
5
Human Capital
3
3
5
Social Capital
5
5
5
Risk Spreading
4
4
4
Manage Information
3
3
4
Public Perception
5
5
5
Feasibility Factor
2
2
4
Climate (3)
0.7
0.7
0.7
Climate (9)
0.1
0.1
0.7
Climate (3)
1.4
1.4
2.8
Climate (9)
0.2
0.2
2.8
Climate (3)
1
1
1
Climate (9)
0.1
0.1
0.1
Efficacy Factor – GDP
Potential Coping Index – GDP
Efficacy Factor – Food Self-sufficiency
Potential Coping Index – Food Self-sufficiency
Climate (3)
2
2
4
Climate (9)
0.2
0.2
0.4
Source: Table 3 in Yohe, et al, 2002.
15
Figure 1 – Panel A
Inter-annual Variability in River Flow with a Static Climate
Figure 1 – Panel B
Inter-annual Variability in River Flow with a Climate Change – A Climbing Mean
16
Figure 1 – Panel C
Inter-annual Variability in River Flow with a Climate Change – A Climbing Mean
and Expanding Variance
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Figure 2
Annual River Flow (milliards)
The effect on the upper threshold of building levies over a five year period
beginning in the fifth year.
Flow
100
90
80
70
60
50
40
30
20
Upper
Low er
0
10
20
30
40
50
Year
Figure 3
The effect on the upper threshold of periodic dredging of the river bed.
Annual River Flow (milliards)
100
90
80
70
Flow
60
Upper
50
Lower
40
30
20
0
10
20
30
40
Time Period
18
50
Figure 4
Annual River Flow (milliards)
The smoothing effect on inter-annual variability of building an upstream dam.
100
90
80
70
60
50
40
30
20
Flow
Upper
Low er
0
10
20
30
40
Time Period
19
50
Figure 5
Not-Implausible Climate Scenarios – Annual Flow in the Nile.
20
Exercises:
1.
Refer to the attached spreadsheet. It will allow you to conduct a few adaptation simulations for
protecting property from sea level rise using a cost-benefit framework; it is entirely made-up, but
very similar to a model that has been applied to the U.S. coastline in Yohe, et al (1996).
Sea level rise is linear in time; the pace in centimeters per year can be adjusted in blue cell
C1. Blue cells C2 and C3 allow you to adjust the annual rate of growth in population and
percapita income; use fractions, so 0.01 is 1% per year. You can decide when SLR will cause
trouble by setting a threshold in centimeters in the red cell C5. The current value of threatened
property and cost of building a protective barrier can be recorded in blue cells C7 and C8.
Property value increases in proportion to increases in population and income; and the cost of
building climbs at an inflation rate given as a fraction in blue cell C10. Maintenance costs are a
fraction of building costs; it is set in blue cell C9. And the discount rate for the calculation can be
set as a fraction in blue cell C11. The total present value of the costs of protection and the benefits
of protection (property value not lost) are shown in the green cells; and the present value of net
benefits is displayed in the orange cell.
a.
Experiment with the spreadsheet to get a feel for what it does. Try different thresholds,
inflation rates, discount rates, and the like. Pick a few interesting combinations. Find
some for which PV(B-C) is positive and others for which it is negative.
b.
For the combinations you pick, test the sensitivity of the protection decision (protect if
and only if PV(B-C) > 0) to systematic changes in the threshold, interest rate, rates of
inflation in cost and property value, and rates of growth in income and population. Find
the levels for each parameter for which the PV(B-C) is exactly zero (all other parameters
held constant)
c.
You can interpret your high or low income, inflation and population cases from part b as
different variants on underlying socio-economic economic scenarios. If you assume that
low growth and/or high protection cost inflation scenarios are reflections of restricted
overall economic development, you could imagine that fewer resources available for
protection projects in these cases. Do your results suggest that this would fly in the face
of an increase in the demand for protection, indicated by an increased likelihood that the
net present value of protection would be positive? If not, could you see that coastal
inundation would be a more severe problem in these cases, in the sense that more
property would be abandoned along any sea level trajectory?
d.
Now envision foresight so that the property value would depreciate to 50% of the quoted
value by the time of inundation and the protection decision. Experiment with the levels
you found in part b to show the effect of this sort of autonomous adaptation – less
property protected with diminished cost associated with sea level rise (under the
assumption that expenditures that would have been devoted to maintaining these
threatened properties were devoted to other worthwhile projects.
e.
Compare the cost of protection with the cost of abandonment at the critical time of
decision to see how much adaptation can save, especially if property is very valuable.
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f.
This is a more difficult problem. Return to your original set-up and imagine that coastal
storms can damage property well in advance of inundation from rising sea levels.
Assume that the property cannot really be protected from these storms, and suppose that
the likelihood of a storm that would destroy the property in any given year is currently
1%. Pick a sea level trajectory, like 0.5 centimeters per year, and assume that every 10
centimeter rise in sea level would double the likelihood of destruction by a storm – i.e.,
1%, 2%, 4%, 8% and so on for 0, 10, 20, 30, …. centimeters of sea level rise. Pick one
interesting combination of other parameters from part b for which PV(B-C) was positive
and another where it was negative. Compute a time series of the expected economic loss
from storms for both with and without sea level rise under the assumption that property
would be always be rebuilt (to its then current value) after a storm but only protected
from sea level rise if PV(B-C) > 0. Now redo the calculation under an alternative
assumption that property that has been destroyed by a storm can never be rebuilt and so
would not need to be protected from future sea level rise. Does the cost attributable to
sea level rise (including the increased frequency of storms) go up or down under either
assumption in comparison to the estimates of the discounted value of protection costs or
abandoned property that ignored coastal storms? This sort of “retreat from the sea” is
being employed in South Carolina as a means of retarded excess development of the
coastline; but it has cost of climate change implications.
2.
This is the beginning of a value of information problem. Envision two possible trajectories for the
mean of a climate variable (like river flow). Both start in the present at the same point – index that
by 100. Trajectory A sees flow increasing by 1 percent per year. Trajectory B would show flow
falling by 1 percent per year. Clearly, an adaptation for trajectory A (designed to minimize the
chance of flooding) would be different than an adaptation for trajectory B (designed to help
irrigated agriculture cope with shortfalls in water availability). So, it will be important to use
current observations as the future unfolds to try to decide whether trajectory A or B is actually
materializing. There is climate variability around the mean; assume that it is normally distributed
with a mean of 0 and a standard deviation of ten. After five years, what is the probability that an
observation drawn from a distribution from trajectory B would higher than the mean value along
trajectory A? After 10 years, what is the probability that an observation drawn from a distribution
from trajectory A would lower than the mean value along trajectory B?
3.
Pick a specific climate variable of interest for your project and experiment with COSMIC to see if
you can get a handle for a range of “not-implausible” futures. Choose two critical dates (like 2020
and 2040), and run three different climate scenarios (say S1, S3 and S5) with three different
climate sensitivities (1.5, 2.5 and 4.5 degrees) for three different global circulation models.
Repeat the experiment along a path that restricts concentrations to 550 ppm.
4.
Pick one or more specific adaptations in your project that might be considered in more than one
place. Assign one location to a different member of your team and ask him or her to evaluate the
strength of the determinants of adaptive capacity in support of each adaptation. Compare the
results to see if you can uncover which determinants act locally and which might operate on a
macro scale. Dealing with the former will focus your attention to specific locations; but
discovering the others will lead you to more widespread strengths or weaknesses that will have to
be handled regionally or nationally.
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