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The Representation of Human-Environment Interactions in Land Change Models
Conceptual models of human-environment interactions in land science
A theory of land system change should conceptualize the relationships between the driving and conditioning forces and land use change; including the relationships among the driving forces and human behavior and organization underlying these relationships. Existing disciplinary theories can help to analyze aspects of land change in specific situations and under well-defined assumptions. However, the paradigms and theories applied by the different disciplines are often difficult to integrate and their specific foci do not easily combine into an integrated understanding of land change. So far researchers have not yet succeeded in defining an all-compassing theory of land change and it can be questioned if the formulation of such theory is within reach. The lack of such overarching theory hampers the design of (conceptual) models to represent the human-environment interactions underlying land change.
Theories from multiple disciplines, such as economics, geography, ecology and anthropology, contribute to the explanation of land change. Often, these theories are related to specific land conversion processes or sectors, e.g. Boserupian theory concerning the effects of population on land use intensity (Boserup 1965; Turner and Fischer-Kowalski 2010; Turner and Ali 1996), neo-Thünen theory about moving frontiers and urban markets (Walker 2004; Walker and Solecki 2004) and the theories of Fujita and Krugman about urban development (Fujita et al. 1999a, b) as notable examples. Most theories cannot adequately explain the complexity of land use decision making underlying the observed land changes. Assumed agent behaviors in the common rational choice paradigm are very restricted and a variety of alternative decision making models are available (Meyfroidt 2012). Rational choice theory may reasonably explain land use decisions under the bid-rent paradigm. However, in reality individuals may rather seek to minimize risks or take them, as the case may be (Rabin 1998). Poorly defined property rights are not conducive to the competitive bidding process that leads to the equilibrium rent profile, which is most frequently underlying urban and agricultural models (Parker and Filatova 2008). In a recent review of the representation of decision making in land change research, Meyfroidt (2012) concludes that in land change science the cognitive aspects of decision making are underrepresented. His overview of alternative decision making models is synthesized by the notion that (i) land use choices result from multiple decisionmaking processes and rely on various motives, influenced by social norms, emotions, beliefs, and values toward the environment; (ii) social–ecological feedbacks are mediated by the environmental cognitions, that is, the perception, interpretation, evaluation of environmental change, and decision-making; (iii) human agents actively re-evaluate their beliefs, values, and functioning to adapt to unexpected environmental changes (Meyfroidt 2012).
The different, alternative, representations of decision making in land change and land change models are discussed by Hersperger et al. (2010) who describe 4 conceptual models that (often implicitly) underlie much land change model representations. Figure 8.1 summarizes the three most important models identified by Hersperger. We have added a fourth model that explicitly addresses the socio-ecological feedbacks and re-evaluation of decision making upon environmental change.
The first model looks for a direct relation between driving factors and land change, e.g. between population and agricultural intensity or between road building and deforestation. The identification of the underlying driving factors of land change has been a popular research topic and many papers have, for specific case studies, revealed the locally most important drivers of land change. Decision making that moderates the relation between driving factors and land change is often implicit and not analyzed explicitly. The relations between driving factors and land change can
Fig. 8.1 Conceptual models for the representation of the relation between driving factors and land change (Modified after Hersperger et al. 2010)
be established by empirical analysis using observed land change data and statistical techniques, either based on spatial data or household interviews (Bürgi et al. 2004; Verburg et al. 2004a; Walsh et al. 1999). When using spatial data, statistical models are estimated that relate locations of observed land change (as dependent factor) to the spatial distribution of the driving factors (as independent factors). For example, locations of urbanization may be associated with locations of improved accessibility, resulting in a statistical model that relates accessibility to urbanization.
The second model represents the chain from driving factors to actor to land change. Although the actor has an explicit role in this sequence, the decision making of the actors itself may not be studied in detail and uniform decision making structures may be assumed. In addition, the driving factors are assumed to be independent of the actors. Examples of the application of this conceptual model include many economic land change models in which all actors are assumed to behave according to an uniform rational choice model (Happe et al. 2006). In such models the actors are supposed to make decisions based on land rent. Land rent is then explained as a function of driving factors, e.g. soil suitability and transportation costs.
The third conceptual model explicitly addresses the decision making process and accounts for the fact that the same driving factor may lead to a different land change outcome depending on variations in the decision making process. Examples include many social science studies in which variations in decision making between groups of the population are studied. As an example, Overmars et al. (2007) identified that in a case study in the Philippines, different ethnic groups have different land use decision strategies based on cultural tradition and knowledge. In many agent-based land change models a typology of agents is made in which the different groups are represented by different decision making rules towards land change (Valbuena et al. 2008). In the model of Valbuena et al. (2010a) hobby farmers are distinguished from commercial farmers as the decision making of both groups is governed by different objectives and motivations.
The fourth conceptual model, which we have added in addition to the models of Hersperger et al., represents an explicit feedback from land change to the actor and the driving factors. These feedbacks cause an impact of land change on the driving factors of land change, or invoke changes in the decision making strategy as result of actor learning, adaptation and perception in response to the experienced land change. Feedbacks between land change and decision making are not always straightforward and direct. Often the feedback operates across different spatial or temporal scales. Local land changes add up to impacts on the global climate system, in turn leading to local impacts in vulnerable regions in terms of changes in cropping conditions or increased flood risks to which people adapt their decision. The importance of such feedbacks was stressed by van Noordwijk et al. (2011) and Meyfroidt (2012). Unfortunately, only a small number of examples of the study of such feedbacks are available in the land science literature, mostly due to the difficulty of observing and quantifying such feedback mechanisms (Claessens et al. 2009; Verburg 2006).
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