Integrated bio-economic models as tools to support land-use decision making: a review of potential and limitations
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Bio-economic modelling has become a useful tool for anticipating the outcomes of policies and technologies before their implementation. Advances in mathematical programming have made it possible to build more comprehensive models. In an overview of recent studies about bio-economic models applied to land-use problems in agriculture and forestry, we evaluated how aspects such as uncertainty, multiple objective functions, system dynamics and time have been incorporated into models. We found that single objective models were more frequently applied at the farm level, while multiple objective modelling has been applied to meet concerns at the landscape level. Among the objectives, social aspects are seldom represented in all models, when being compared to economic and environmental aspects. The integration of uncertainty is occasionally a topic, while stochastic approaches are more frequently applied than non-stochastic robust methods. Most multiple-objective models do not integrate uncertainty or sequential decision making. Static approaches continue to be more recurrent than truly dynamic models. Even though integrating multiple aspects may enhance our understanding of a system; it involves a tradeoff between complexity and robustness of the results obtained. Land-use models have to address this balance between complexity and robustness in order to evolve towards robust multiple-objective spatial optimization as a prerequisite to achieve sustainability goals.
KeywordsOptimization Uncertainty System dynamics Time Objective functions
Mathematics Subject ClassificationQ57
We want to express our gratitude to the Deutsche Forschungsgemeinschaft (DFG) for their financial support (KN 586/5-2, KN 586/9-1) and to the members of the research group FOR 816. The authors also wish to thank Mr. Dave Parsons and Michael Du for language editing and Dr. Patrick Hildebrandt for valuable comments on this article.
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