Economic modelling approaches to land use and cover change

This report explores and developes approaches to spatially explicit modelling of land use/cover change and the potential impact on land use related emissions of greenhouse gases. The approaches are based on economic theory and related to biophysical and socio-economic information. The literature study revealed a number of different economic approaches to land use modelling. The approaches are described and evaluated on their theoretical foundation and practical applicability. In modelling studies models have been developed that take both socio-economic and biophysical information into account. First the models have been run with hypothetical parameter values. In the second stage, data requirements have been compared to a data set, that is based on census data of Java. Two different approaches have been developed. In the first approach a model (the 'aggregated model') is developed where the production value is expressed as the value of all crops. This model can be parameterised by Ordinary Least Squares. In the second approach (the 'detailed model') separate crop production functions have been estimated by means of Maximum Entropy Econometrics. The parameterised models have been run and the results are shown in this report. The models offer promising opportunities and can be are improved and extended in many ways. Integrating biophysical aspects in economic models and comparing economic models and models from other disciplines requires a similar scale level of both approaches. The application of an economic model on a higher scale level implies some loss of detail: however, the comparability of the models with other models can gain insight that will not be obtained with a non-comparable, detailed economic model. By applying an economic model to a data set defined on a coarser scale suited for regional analysis, this report has made a contribution to the mutual understanding of economists and geographers in the issue of land use and cover change, and thereby also to a better understanding of LUC