Methods for Evaluating Adaptation Options (In an Integrated Context)

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.

1. It is critical to keep track of who adapts to what. Adaptation is path dependent and location specific.

1. 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.

1. 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.

1. 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.

1. 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).

1. 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.

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.

1. Human and natural systems tend to adapt autonomously to gradual change and to change in variability.

1. Human systems can also plan and implement adaptation strategies in an effort to reduce potential vulnerability or exploit emerging opportunities even further.

1. 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.

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 inter-annual 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.

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.

1. 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 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.

1. 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 PCCj ) 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.,