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GTAP Events: Center Seminar Series

"Integrating the Socio-economic and Physical Drivers of Land-use Change at Climate relevant Scales: an Example with Biofuels"
by Joshua Elliott

Understanding the factors that drive land-use change, and developing better methods for projecting future land-use change, become vitally important in the climate change context. The 3rd Assessment Report of the UN Intergovernmental Panel on Climate Change (IPCC) noted that "the emissions scenarios considered in future climate change studies need to integrate high resolution representations of land use change" and that increased coupling among the various relevant components—such as mitigation and adaptation responses to climate change, and climate response to land use—should be included in a consistent framework for integrated assessment (Jones et al. AR-3 2001, Chapter 3). While much work has been done to include global forecasts of natural land cover types such as forests and deserts at high spatial resolutions, little has been done to incorporate into these forecasts the socioeconomic drivers of land-use change, such as agriculture, forestry, and urbanization on a global scale. To address this urgent need for improved global land-use forecasts, we are developing a versatile high-resolution companion to the CIM-EARTH computable general equilibrium (CGE) global economics framework. The Partial-Equilibrium Economic Landuse (PEEL) model generates consistent forecasts of the land-use change (LUC) that results from changing growing conditions, technology, resource availability, and demands. We illustrate the usefulness of the model by applying the prototype to the important and highly topical question of the implications of an aggressive 1st generation biofuels policy on LUC. We note that the high resolution (5 arcminutes, or approximately 10km on a side) of the model elements allows for the direct integration of climate and socioeconomic factors at the scale of relevant climate variation, without the need for aggregation (and the associated potential for significant information loss). We conclude with a discussion of model limitations and future directions, focusing especially on data needs and potential new data sources.

Date/Time: 10/14/2010   10:00 AM - 11:30 AM
Location: KRAN 661