Download Dynamic modeling of adaptation indicators related to climate

Dynamic modeling of adaptation indicators related to climate
change from a socio-economic perspective
Tamás Pálvölgyi – Nóra Szécsi
Abstract
Today, every socio-economic system is integrated into an environment shaped by constantly changing global
trends. These systems cannot continue to function unaltered, thus they are forced to react in a spontaneous or
conscious manner. The main aim of this paper is to examine adaptation processes related to evolving external
circumstances through the theoretical concept of the adaptation capacity. Our research is based on the
comparison and systemization of the evolution and continuous enlargement of the different dimensions and
partial indicators of the model through a vast number of scientific publications and case studies. The primary
result of the research is the delimitation of a group of indicators corresponding to the complex structures and
processes. During the identification of our model we established 56 distinct input data sets and grouped them
into separate clusters according to the three dimensions of sustainability. It is possible to model cause-effect and
synergic relations, along with the evolution of indicators in time, using the methodology of fuzzy systems.
Introduction
This paper reviews the concept of adaptation capacity in the context of adaptation of
human systems to global changes, especially climate change. Before interpreting the
dimensions of the complex indicator under discussion, it is necessary to define, explain and
determine the adaptation mechanisms themselves. Only then can we determine the capacity in
connection with this concept, and measure the complex factor element with indicators.
Adaptation in the context of human dimensions of global change usually refers to a
process, action, outcome in a system in order for the system to better cope with, manage or
adjust to some changing condition, hazard, risk or opportunity (Smit & Wandel, 2006). The
definition is approached from a broader aspect based on time horizon, the effected measures
of adaptation directed at enhancing the ability to tackle with external stress, so it requires
taking preparations for the possible future impacts. Beside the anticipatory behaviour the
adaptation does include minimising or reducing the unavoidable consequences of the altering
process at the present and based on their degree of spontaneity they can be autonomous or
planned (IPCC, 2001). Even though this concept was first applied in the natural sciences,
especially in biology, the behaviour-centered intent of the original concept makes it relevant
when examining global trends. Adaptability, in other words capacity to adapt, was created as
a concept involving social sciences, because contrary to the long established random
adaptation forms in nature, human dimension is characterised by consciousness, multi-faceted
planning and prudence. The term capacity refers to the response time to the modifying
circumstances. It is now recognized that societies that adapt quickly and easily to a range of
stimuli, dispose of a higher adaptive capacity, and its factors as driving forces could influence
and advance the adaptation process. Still, a measurable, normative approach to these
capacities appeared only when the political and strategic tackling of global changes brought it
to the forefront.
It is important to note that the evaluation of any adaptation process is plagued by
uncertainty, since the outcome of future stimuli and the development scenarios of socioeconomic systems may lead to different alternatives. Therefore the flexibility and predictive
characters of capacities are extremely important, also from the point of view of partial
indicators. Adaptive capacity is multidimensional: it is determined by complex interrelationships of number of factors at different scales (Vincent, 2006). Furthermore, it is
evident that adaptation capacity depends on a number of social, economic, institutional and
technological factors, and comprises both quantitative and qualitative characteristics,
requiring a holistic and integrated approach all through the modeling process. A common
denominator of general concepts is that all they can provide is a mere snapshot of the
capacities of a given system, without the changes in time (Engle, 2011). Concerning the
chronological dimension, it is important to make a distinction between coping and adaptive
capacity. Our analysis and base model do not venture to explore and measure capacities to
cope, immediately or in short term, with well-known stimuli the subject encountered before
(Adger et al, 2004). Adaptation capacities are, by their very nature, long-term processes, and
refer to the capability to react to new, unexpected effects (Twomlov et al., 2008), therefore
comprise more sustainable solutions (Smit & Wandel, 2006). Our research is aimed at
creating a new, dynamic adaptation model which complies with the requirements mentioned
above and reflects the relations between partial index numbers. Before introducing the
dynamic modeling approach, we will expand on the main tendencies in the scientific
evolution of adaptation models. The analysis is carried out by comparing indicators.
Development of adaptive capacity models
The quantitative definition of this concept was triggered by a general relational
premise, namely that of a more developed economic system or country usually having a
higher adaptation capacity towards external effects than a less developed region. However,
this initial thought already leads to a contradictory situation. A paradox observation has been
made in relation with the development of a given system: in seemingly stable natural
conditions, including the access to natural resources, certain economic and production
structures may form which, if they become exclusive, may divert the given system to an
undesirable trajectory, reducing potential adaptation capacities and possibilities (HAS, 2011).
Therefore it is self-evident that it is insufficient to depart from sheer economic resources and
parameters.
Concerning the dimensions of capacities related to climate change, the relevant
literature primarily refer to the third IPCC1 report (IPCC, 2001), which names 6 main areas:
(1) economic resources; (2) technology; (3) information and skills; (4) infrastructures; (5)
institutions; (6) equity. Obviously these areas are only attribute categories, and do not define
specific, detailed indicators. The report underlines the tight interdependence of the factors,
which may change in space and time. The weight given to each factor also depends on which
system and which effect we are looking at, therefore these may vary. Within the system,
access to and distribution of certain resources are of key importance. This is reflected in the
last dimension. Human, political and social capitals have also been taken into account, which
marks the probable course of the models’ further development. A general conclusion is that
besides generic factors – including mainly physical resources, fixed factors and native
characteristics – the focus of research has moved to factors that enable specific, unique and
innovative adaptation. Specialization refers to the measurement of the creation and transfer of
knowledge, social networks, institutional and governmental issues, by indicators (Yohe &
Tol, 2002; Haddad, 2005; Lemos & Engle, 2010; Gupta, et al., 2010).
Reflecting on the role of human capital, a further aspect has been considered, that of
individual psychological and cognitive effects. Based on the so called socio-cognitive models,
research has been conducted to examine how and to what extent perceived adaptation capacity
differs from the objective capacity values defined by indicators (Grothmann & Patt, 2005;
Alberina et al., 2004; Williamson et al, 2012). High adaptation capacity values do not
necessarily trigger specific actions (IPCC, 2007). Therefore, the developers of models either
1
Intergovermental Panel on Climate Change: The scientific intergovernmental body was established in 1988 by
the organization of the United Nations. The panel tasked with reviewing and assessing the most recent scientific,
technical and socio-economic information produced worldwide relevant to the understanding of climate change.
tried to include, as an additional basic factor, an indicator reflecting willingness to act or they
applied the cognitive approach described above. A third strategy is that, they set out to
systemize indices along the threefold dimension of awareness, ability and action, thus
attempting to determine which phase of adaptation a given society is currently undergoing and
whether specific adaptation measures have been taken beyond resources (ATEAM, 2004;
ESPON Climate 2011).
Practical applicability and the need for objective comparison of given systems is an
obvious motivation for the quantification of an abstract concept. The most common method in
empirical research is the spatial delimitation of indicators, which refers to an essentially
country-based approach (Downing et al., 2001; Yohe & Tol 2002; Parson et al, 2003; Brooks
et al., 2005; Grothmann & Patt, 2005; Adger & Vincent, 2005; Alberina et al., 2006; Eakin &
Lemos, 2006; Twomlov et al., 2008; Lemos-Engle, 2010). The reasons for this are twofold:
on one hand, policy planning applicability and the access to appropriate statistical input; on
the other hand, the usage of this level is justified by theory, as local and sub-national
processes are largely influenced by national policies and target values (Adger et al., 2004).
Therefore, even though the effects themselves are specific to their location, the higher spatial
levels ought not to be ignored when measuring adaptation capacities. In addition, researchers
have made attempts at defining capacity on a regional (ATEAM, 2004; ESPON Climate
2011) and local/household level (Gupta, et al., 2010; Vincent, 2007), but these meant mainly
turning the concepts top-down.
When examining the development of models, it is worth noting that there are two
distinct approaches to the basic concept. Theory-driven or deductive models (Vincent, 2007)
elaborate the concept of capacity by meticulously describing cause-effect relations on a
certain level, and focus on the ability to comply with these. Data-driven methodology,
however, intends to reveal relations and patterns among a large quantity of statistical data in
an inductive way, and then draw conclusions as to the basic concept (Adger et al, 2004). Due
to the intense need for data in this latter case, research is limited to national level. The scope
of analyses is usually limited to the correlation between the number of natural disasters, the
proportion of population exposed to hazardous phenomena, and mortality (Yohe & Tol 2002;
Adger et al., 2004; Brooks et al., 2005; Alberina et al., 2006). This summary provides a short
summary of the research activity in this field to illustrate the complex nature of the different
approaches of this concept. In the next chapter we will present the new angle in our research,
as well as key issues we intend to examine.
New dynamic construct from the view of sustainability
One of the aims of our research was the synthesis of academic literature about
modeling adaptation capacities, as described above. We studied a body of relevant
publications, case studies and other research ranging from the IPCC Report 2001, considered
a point of departure in the area, to the most recent scientific results. In our analysis we defined
21 adaptive conceptual frameworks based on relevance and references. We intend to highlight
that, due to the heterogenous nature of the subject, as visible from the course of development,
indicators and dimensions had been selected with different approaches, emphases and depth.
Half of the modeling systems provided only aggregate dimensions, disregarding specific base
factors. Taking these difficulties into consideration, we included 151 entry-level factors in the
82 aggregate dimensions of the final database. The large number of categories is due to
redundant content and partial overlaps. The input base for the final cluster analysis comprises
56 distinct main indicators.
Based upon the development of modeling capacity, the main directions of further
progress can be summarized in three points. The lack of dynamism (1) pointed out in the
introduction is evident both internally and in the long term. On one hand it is obvious that
dimensions do not affect the final capacity value equally. However, these relational
differences were reflected solely in the relative weight of factors, taking factors to be
independent from each other (ATEAM, 2004; Adger & Vincent, 2005; Vincent, 2007;
ESPON Climate 2011). The real issue, beyond snapshots of adaptation capacities, is the future
trend in capacities and the predictive nature of the model. By including the interaction of
factors into the analysis, the model immediately comes to life and becomes dynamic.
Complex global processes can be approached more realistically when taking internal
dynamics into account.
The next issue (2) is how to shape the above mentioned relations into a model. There
are both quantitative and qualitative factors at play. Indeed, due to special, varied factors
related to society, the majority of these belong to the latter category. The comparison of
indicators of different measurement units and characters was done by fuzzy systems. By
examining direct two-way influences we can skip intermediary aggregate dimensions and
directly generate alternative adaptation capacities by regrouping factors in different manners.
The third aspect (3) of the new structure is the criterion of sustainability. By this we
mean, among others, the equal consideration for and harmonization of societal, economic and
environmental systems. As highlighted in the introduction, research literature generally relates
sustainability with adaptation capacities, but only on the long run. In addition, sustainability
also figures among the potentially negative synergic effects of adaptation. Evaluation is
related to the expected consequences of different tools of adaptation when they are applied in
practice (Barnett & O’Neill 2010). In the case of so-called vulnerability surveys, adaptation
capacity is often examined as part of a larger theoretical system (Smit & Wandel, 2006). Our
research did not study this concept, however, it provides a point of view to evaluate ecological
systems within the adaptation capacity. In connection with climate change, the vulnerability
concept treats both the climatic and climate-sensible indicators separate from the factors of
adaptation capacity. Therefore, in practical, empirical research, natural resources rarely
appear among factors of adaptation capacity. In the fuzzy modeling of our research, input data
include intrinsic natural parameters independent of climate. Our model views economic and
social contact points from this angle.
Conclusions
Our research offers an innovative interpretation of the model of adaptation capacity, regarding
adaptation capabilities of a given socioeconomic system towards global external processes.
Relying on a synthesis of research literature in the field, this paper explains the general trends
in the development of models, as well as the most relevant shortcomings, and then deduces
the criteria for a dynamic approach. During the identification of our model we established 56
distinct input data sets and grouped them into separate clusters according to the three
attributes of sustainability. It is important to emphasize 32% of the indicators have direct
connection to the dimension of environment. Relations are examined by fuzzy systems. The
generation of alternative adaptation capacity indicators based upon relations between factors
will be the subject of further research.
Acknowledgement
The work reported in the paper has been developed in the framework of the project „Talent
care and cultivation in the scientific workshops of BME” project. This project is supported by
the grant TÁMOP – 4.2.2.B – 10/1--2010-0009.
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