Ways of framing adaptation
This subsection reviews the main ways that climate adaptation is framed
both in the scientific literature as well as in practise.
The IPCC definition
The IPCC defines adaptation as “adjustment in natural or
human systems to a new or changing environment. Adaptation to climate
change refers to adjustment in natural or human systems in response to
actual or expected climatic stimuli or their effects, which moderates
harm or exploits beneficial opportunities” (McCarthy et al.,
2001). In this guideline, we restrict the definition of adaptation to
those “adjustments in natural or human systems ...”
that are induced by humans themselves, excluding autonomous adaptation
of the natural system (i.e., the natural system adapting itself).
Restricting the definition of adaptation to human activity, including
the human activity of managing natural systems, makes sense because
this is the way adaptation is predominately used within the policy and
practitioner communities. This does not mean that we consider studying
autonomous adaptation of natural systems to be less important, but we
only do not call this process adaptation.
Under this broad definition of adaptation, a diversity of approaches
are applied for assessing adaptation and several different ways of
framing adaptation can be distinguished.
Adaptation as response to climate impacts
The “classical” framing of adaptation in the
context of the IPCC is the impact-analytical one, which sees adaptation
as a single decision (or as a few single decisions) that is (are) taken
on the basis of projected future impacts. The IPCC defines climate
change impact assessment as “the practice of identifying and
evaluating, in monetary and/or non-monetary terms, the effects of
climate change on natural and human systems" (Parry et al., 2007). The
basic assumptions are that impacts and adaptation decisions can be
singled out and be represented formally by means of mathematical or
computational models. Emission scenarios are fed into climate models to
produce climate scenarios which are then downscaled to a regional level
and fed into impact models to estimate potential impacts. Based on the
simulated potential impacts, adaptation measures are identified and
evaluated via multi-criteria, costeffectiveness or cost-benefit
analysis (Carter et al., 1994, 2007; Adger et al., 2007).
Several limitations of the impact-analystical approach have become
apparent and are increasingly motivating other approaches. Opponents of
the impact-analytical approach criticise that both impacts and the
effects of adaptation options are difficult to predict or project
(e.g., van Aalst et al., 2008). Regional climate models exhibit high
uncertainties and often show no coherence in results for specific
regions (IPCC, 2007). Impact models are only available for some sectors
(e.g., agriculture, forestry) and, if available, exhibit similar
uncertainties. For many of the world’s regions this means
that there is little or no agreement on how the regional climate will
change and what impacts to expect.
Adaptation as decision making under uncertainty
In recent years, approaches that frame adaptation from the perspective
of decision making under uncertainty have received more attention. In
this framing, the analysis does not start with climate scenarios and
the projection of impacts, but with a concrete decision (e.g., by how
much to raise the dikes), based upon which then all available
information on the full range of possible impacts is collected. Climate
scenarios may only play a limited role in this framing, as other
sources of uncertainty might be more significant (Dessai et al., 2005).
Policy documents emphasise so-called no-regret, low-regret or robust
options, that is the implementation of options that are beneficial or
robust no matter how the climate changes (e.g., European Commission,
2009).
One approach that is particularly relevant under this framing is robust
decision making. Uncertainty about mid to long term impacts will
continue to make the construction of probability density functions for
impacts problematic (Adger et al., 2009). Robust-decision making
requires running a large amount of scenarios (without probability
attached) and analysing alternatives over these scenarios on a given
set of criteria. It does not require probability functions to the
different scenarios. This way options can be eliminated which do not
perform well in projected futures, even when the likelihoods of future
evolutions are not well known. For example, Wilby and Dessai (2010)
apply a method of robust decision-making to address the question of
ranking adaptation options in the water sector in Wales and
the UK.
Adaptation as a social and institutional process
A further argument made against the impact and decision-analytical
approach to adaptation is that even when effective, robust or
low-regret adaptation options can be identified, this does not meant
that these options are also realised. In many cases it is found that
institutional barriers prohibit the implementation of options or that
“existing” adaptive capacity is not realised (Adger
et al., 2007).
This has led to institution-analytical approaches, which frame
adaptation as a social and institutional process that involves many
actors and many decisions at different levels. The basic assumptions
are that impact and adaptation decisions can not necessarily be singled
out and even if they can not be represented formally. Outcomes of
actions can usually not be predicted because they depend on actions of
(many) other agents as well as the social and cultural context. The
focal point of analysis thus are institutions, in the broad sense of
“formal and informal rules in use” (Ostrom, 2005),
that shape the interplay between the actors. Various variants of this
framing can be found in the literature.
Adaptation as reducing current social vulnerability or enhancing adaptive capacity
On a local or community level, a variety of participatory or so-called
community-based approaches under the names of Vulnerability and
Capacity Assessment (VCA), Livelihood analysis, Rapid Rural Appraisal
(RRA) and Participatory Rural Appraisal (PRA) are applied (Chambers,
1994). These assessments emphasise social conditions, individual
perceptions and informal institutions in that they build on local
experiences in coping (or not coping) with current climate conditions
as a starting point for developing appropriate adaptation responses
(Pelling and High, 2005; van Aalst et al., 2008).
Adaptation as policy integration/mainstreaming
On the level of national and international policy making,
institution-analytical approaches emphasise the need for horizontal
integration of policy, which is often also called mainstreaming
adaptation or climate-proofing policies. The idea behind these concepts
is to integrate (or mainstream) climate change adaptation
considerations into existing policy processes. Mainstreaming adaptation
was first discussed in the context of development policy (Klein et al.,
1995, 2007), but now receives attention in other policy fields. The
current European Commission’s White Paper on Adaptation, for
example, stresses the integration of adaptation considerations into
existing and future policy, legislation and funding programmes
(European Commission, 2009).
Adaptation as multi-level governance
Recently, concepts from the domains of governance and adaptive
management are receiving broader attention in climate change
adaptation. The concept of multi-level governance stresses both
vertical and horizontal integration of policy. It acknowledges that (i)
policy fields are increasingly difficult to separate, (ii) that the
influence of private or non-state actors is increasing, and (iii) that
decisions are less-top down. Outcomes are thus determined by the
interplay of intuitions at various levels of decision making and
multilevel institutions are seen to be key to adaptation and
sustainability (Adger et al., 2005; Anderies et al., 2005; Berkes,
2007; Armitage et al., 2008).
Adaptation as (social) learning and adaptive management
The concept of adaptive management (AM) has emerged as a response to
the complexity and non-determinism inherent in many resource management
situations (Holling, 1973; Walters, 1986) and is seen as an instrument
to bridge the gap between adaptation research and policy (Arvai et al.,
2006). AM is the process of improving management goals, policies and
practises through learning from the outcomes of management (Pahl-Wostl
et al., 2007). Rather than attempting to generate more knowledge about
regional climate change, impacts and adaptation options via analytical
methods, adaptive management builds on the ability of institutions to
adapt ad-hoc when experiencing environmental (or social) change (Dietz
et al., 2003; Pahl-Wostl et al., 2007; Huitema et al., 2009).
Institutions are not designed specifically in response to climate
change or other threats but are designed to be adaptive in general
through their ability to experiment and learn. AM is thus closely
connected to the concepts of social and institutional learning
(Armitage et al., 2008).
Next to the ability to learn, the AM literature emphasises, further
generic properties of institutions that makes them adaptive (Huitema et
al., 2009). Polycentric institutions are seen to be adaptive because
rights and responsibilities are distributed between different
“centres” thus allowing for redundancy, diversity
and experimentation, which, in turn, helps to better cope with change
and uncertainty (Dietz et al., 2003; Ostrom, 2005). Other literature
emphasises “institutional fit”, which refers to the
matching of the institutional arrangement to the bio-physical scale of
the socio-ecological system that is being managed (Young, 2002). The
evidence about the effectiveness of institutions designed according to
these properties is, however, weak as it is generally difficult to
attribute outcomes to particular institutions (Huitema et al., 2009).
It is widely accepted that there are no panaceas to institutional
design (Ostrom, 2007, 2009).
