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PhD Defence Sara Mehryar


Sara Mehryar is a PhD student in the Department of Urban and Regional Planning and Geo-Information Management. Her supervisors are prof.dr.ir. M.F.A.M. van Maarseveen and prof.dr. R.V. Sliuzas  from the ITC Faculty of Geo-information Science and Earth Observation.

Climate change and global warming are noticeably increasing the losses and damages of natural resources, including fish stocks, lakes, water resources, forests, and farms. Climate change adaptation is a response to such environmental changes by attempting to reduce the vulnerability of social and ecological sub-systems to changes of temperature, rainfall, sea level, etc. Policies designed for climate change adaptation should consider both social and ecological systems impacted by climate change as well as the interaction between such systems, which calls for a Social-Ecological System (SES) perspective.

In this thesis, we argue that participatory policy analysis methods are crucial for decision-making related to climate change adaptation in SESs. Such methods involve a diversity of knowledge, perceptions, preferences, and decision-making of people managing or depending on the natural resources in the process of policy preparation. The participatory policy analysis approach has evolved in response to the failures of traditional policy analysis approaches that place emphasis on formal (quantitative) modelling, rational planning and cost-benefit analysis to find solutions for all sorts of complex problems. We argue that the participatory approach helps policy makers to address the most important features of an SES, i.e., complexity, dynamics and uncertainty, in their policy options analysis.

The main objective of this research is to develop and demonstrate participatory policy analysis methods to support policy-making in SESs’ environmental challenges. The research is built upon a case study of a farming community facing water scarcity in Rafsanjan, Iran. To achieve the objective, a combination of participatory methods in a specific sequence is designed to integrate farmers’ and policy makers’ knowledge, perception, preferences and decision-making in the process of policy making for water scarcity in Rafsanjan. The Driver-Pressure-State-Impact-Response (DPSIR) framework is used in step 1 to categorize and structure the complex SES problem of water scarcity in the case study. In step 2, the DPSIR framework is combined with a participatory Fuzzy Cognitive Mapping method (FCM, i.e. a knowledge coproduction method) to systematically collect the knowledge and perceptions of farmers and policy makers on the data-scarce part of the system and to represent these in a semi-quantitative model. In step 3, the qualitative knowledge produced by participatory FCM is combined with quantitative data to develop a mixed-FCM representing complex SES related to water scarcity. In step 4, the outcome of the mixed FCM is used as an input to develop an Agent-Based Model (ABM) to model the decisions and actions of farmers and simulate the macro-level patterns of the system that emerges from individual behaviour. Finally, the impact of government policy options are simulated by integrating knowledge, perceptions and preferences of stakeholders represented in FCM models, and their decisions and actions represented by ABM.

The outcome of this research is a policy support toolbox that provides four different methods, each one addresses different problem contexts or policy objectives related to climate change adaptation for SESs. DPSIR is a problem structuring framework that helps to categorize and understand different aspects of the problem before policy making. The Perceived-FCM is valuable in predicting stakeholders’ responses to new policies. The Mixed-FCM is useful in simulating and analysing impacts of policy options in the multi-factorial SESs, for which both subjective and objective knowledge is relevant for a better understanding of system’s behaviour. Finally, the FCM-based ABM is useful in simulating impacts of policy options by considering actual human decisions and actions as well as the multifactor behaviour in the SESs.

Moreover, the outcome of this study contributes in bridging gaps in 1) integrated use of qualitative and quantitative evidence in SES modelling, 2) modelling the micro-level and macro-level behaviour of the SESs, and 3) the combination of actor-based and factor-based approaches in SES modelling. Furthermore, this study introduces policy support methods that cover the main features of a complex SES i.e. causality, feedback loops, social-spatial heterogeneity, and temporal dynamics.

Finally, the outcomes of this research might be useful for three groups of people: 1. researchers who have an interest in novel methods to model SESs, 2. practitioners who can use the methods for participatory policy option analysis, and 3. students who can learn from the step-wise approach developed in this research for their own future studies.