18.3. Future Adaptations
Predictions or estimates of likely future adaptations are an essential element
of climate change impact and vulnerability assessment. The degree to which a
future climate change risk is dangerous depends greatly on the likelihood and
effectiveness of adaptations in that system. Studies that ignore or assume no
adaptation are likely to overestimate residual or net impacts and vulnerabilities,
whereas those that assume full and effective adaptation are likely to underestimate
residual impacts and vulnerabilities (Reilly, 1999; Reilly and Schimmelpfennig,
1999; Risbey et al., 1999; Smit et al., 2000). Hence, it is important
to have an improved understanding of the process of adaptation and better information
on the conditions under which adaptations of various types are expected to occur.
Such scholarship on the "how, when, and why" of adaptation is necessary
to make informed judgments on the vulnerabilities of sectors, regions, and communities
(Ausubel, 1991a; Kane et al., 1992a; Reilly, 1995; Burton, 1997; Smithers
and Smit, 1997; Tol et al., 1998; Klein et al., 1999). Insights
into processes of adaptation have been gained from several types of analysis,
including listing of possible adaptation measures, impact assessment models,
adaptation process models, historical and spatial analogs, and empirical analysis
of contemporary adaptation processes.
18.3.1. Possible Adaptation Measures
There are many arbitrary lists of possible adaptation measures, initiatives,
or strategies that have a potential to moderate impacts, if they were implemented
(e.g., Benioff et al., 1996; Smith et al., 1996; Mimura, 1999b).
Such possible adaptations are based on experience, observation, and speculation
about alternatives that might be created (Carter, 1996); they cover a wide range
of types and take numerous forms (UNEP, 1998). For example, possible adaptive
measures for health risks associated with climate change listed by Patz (1996)
appear in Table 18-2.
Similarly, in coastal zone studies, comprehensive lists of potential adaptation
measures are presented; these adaptations include a wide array of engineering
measures, improvements, or changes, including agricultural practices that are
more flood-resistant; negotiating regional water-sharing agreements; providing
efficient mechanisms for disaster management; developing desalination techniques;
planting mangrove belts to provide flood protection; planting salt-tolerant
varieties of vegetation; improving drainage facilities; establishing setback
policies for new developments; developing food insurance schemes; devising flood
early warning systems; and so forth (Al-Farouq and Huq, 1996; Jallow, 1996;
Rijsberman and van Velzen, 1996; Teves et al., 1996; Mimura and Harasawa,
2000). In many other sectors and regions, arbitrary lists of possible adaptations
are common (Erda, 1996; Iglesias et al., 1996). In the Canadian agricultural
sector alone, 96 different adaptation measures have been identified, as summarized
in Table 18-3.
| Table 18-3: Adaptation strategies for the agricultural
sector (adapted from Smit, 1993; Carter, 1996). |
 |
| Adaptation Strategy |
Number of Measures
|
|
| Change topography of land |
11
|
| Use artificial systems to improve water use/
availability and protect against soil erosion |
29
|
| Change farming practices |
21
|
| Change timing of farm operations |
2
|
| Use different crop varieties |
7
|
| Governmental and institutional policies and programs |
16
|
| Research into new technologies |
10
|
|
Such lists indicate the range of strategies and measures that represent possible
adaptations to climate change risks in particular sectors and regions. They
show that there is a large variety and number of possible adaptations, including
many with the potential to reduce adverse climatic change impacts. Many of these
adaptations—especially in agriculture, water resources, and coastal zone
applications—essentially represent improved resource management, and many
would have benefits in dealing with current climatic hazards as well as with
future climatic risks (El Shaer et al., 1996; Harrington, 1996; Huang,
1996; Stakhiv, 1996; Frederick, 1997; Hartig et al., 1997; Mendelsohn
and Bennett, 1997; Major, 1998). In only a few cases are such lists of possible
adaptations considered according to who might undertake them, under what conditions
might they be implemented, and how effective might they be (Easterling, 1996;
Harrington, 1996; Frederick, 1997; Major, 1998; Moss, 1998).
18.3.2. Impact Assessment Models
Estimates of likely future adaptations are essential parts of climate change
impact models. Integrated assessment models also include assumptions about adaptations
in the impact components (Leemans, 1992; Rotmans et al., 1994; Dowlatabadi,
1995; Hulme and Raper, 1995; West and Dowlatabadi, 1999). Some early studies
of impacts assumed no adaptation (Tol et al., 1998), invoking the so-called
"naive" or "dumb farmer" assumption. The "dumb farmer"
assumption—which is not unique to agriculture—is a metaphor for any
impacted agent that is assumed not to anticipate or respond to changed climate
conditions but continues to act as if nothing has changed (Rosenberg, 1992;
Easterling et al., 1993; Smit et al., 1996). By ignoring autonomous and planned
adaptations, such studies do not distinguish between potential and residual
net impacts and are of limited utility in assessing vulnerability.
An alternative approach that is common in more recent impact modeling has been
to assume levels of adaptation. Applications include Nicholls and Leatherman
(1995) for coastal zones, Mendelsohn et al. (1994) and Rosenzweig and Parry
(1994) for agriculture, Sohngen and Mendelsohn (1998) for timber, and Rosenthal
et al. (1995) for space conditioning in buildings. These studies demonstrate
that adaptive measures have the potential to significantly alleviate adverse
impacts of climate change and to benefit from opportunities associated with
changed climatic conditions (Helms et al., 1996; Schimmelpfennig, 1996; Mendelsohn
and Neumann, 1999). The models of Rosenzweig and Parry (1994) show that, with
adaptations assumed, food production could be increased under climate change
in many regions of the world. Stuczyinski et al. (2000) conclude that climate
change would reduce Polish agriculture production by 5-25% without adaptation;
with adaptation assumed, production is estimated to change by -5 to +5%
of current levels. Downing (1991) demonstrates the potential of adaptations
to reduce food deficits in Africa from 50 to 20%. Mendelsohn and Dinar (1999)
estimate that private adaptation could reduce potential climate damages in India's
agriculture from 25 to 15-23%. Reilly et al. (1994) estimate global "welfare"
losses in the agri-food sector of between US$0.1 billion and 61.2 billion without
adaptation, compared to +US$70 to -37 billion with adaptation assumed.
These studies indicate potential rather than the likelihood of
adaptation to alleviate damages (or benefit from opportunities) associated with
changes in climatic mean conditions (rather than changing conditions that include
variability and extremes of climate).
Impact models invariably are based on climate scenarios that focus on adaptation
to changed average conditions, with little attention given to interannual variations
and extremes. Limited research suggests that the potential of adaptation to
cope with changes in average conditions is greater than its potential to cope
with climate change-related variability. For example, Mendelsohn et al. (1999)
show that, assuming adaptation, increases in average temperature would be beneficial
for U.S. agriculture, but increases in interannual variation would be harmful.
West and Dowlatabadi (1999) demonstrate that considering variability and extremes
can lead to estimates of "optimal" adaptation and damages that differ
considerably from those based on gradual changes in mean climatic conditions.
The importance of considering variability, not just mean climate, when estimating
adaptation is widely recognized (Robock et al., 1993; Mearns et al., 1997; Alderwish
and Al-Eryani, 1999; Alexandrov, 1999; Luo and Lin, 1999; Murdiyarso, 2000).
In numerical impact models, assumptions about perception and adaptation are
more commonly arbitrary or based on principles of efficiency and rationality
and assume full information (Yohe et al., 1996; Hurd et al., 1997; Mendelsohn
et al., 1999). As Tol et al. (1998), Schneider et al. (2000), and others have
noted, however, actual and assumed behavior do not necessarily match. In an
analysis of global food production, Parry et al. (1999) assume farm-level and
economic system adaptations but recognize that the "adoption of efficient
adaptation techniques is far from certain." In addition to questions relating
to rationality principles, adaptation behavior is known to vary according to
the amount and type of information available, as well as the ability to act.
Hence, rational behavior that is based on assumed perfect information differs
from rational behavior under uncertainty (Yohe et al., 1996; Yohe and Neumann,
1997; West and Dowlatabadi, 1999). Replacing the "no adaptation" model
with one that assumes rational, unconstrained actors with full information replaces
the "dumb farmer" assumption with the "clairvoyant farmer"
assumption (Smit et al., 1996; Risbey et al., 1999). Reilly (1998) questions
the ability and hence the likelihood of agents to detect and respond efficiently
to the manifestations of climate change. Tol (1998b) also questions whether
perfect foresight and rational behavior are realistic assumptions for predictive
models. Schneider (1997) explores further the assumptions that underlie equilibrium
approaches (ergodic economics), including the equivalence of temporal and spatial
variations.
Numerical impact assessment models tend to use, rather than generate,
information on adaptations to estimate future impacts of climate stimuli, after
the effects of adaptation have been factored in. They indicate the potential
of human systems to adapt autonomously and thus to moderate climate change damages.
