Abstract:
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 howaspects 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 allmodels, 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. Mostmultiple-objectivemodels 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.
