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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. References Alberini, A., Chiabai, A. & Muehlenbachs, L. 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