The quantitative analysis of REDD supply schedules were carried out in a global total land-use context. The Global Model cluster combines geographically explicit biophysical models with economic modeling. The model cluster covers all land-use types and thus allows for fully integrated analysis of competitive interactions between different land uses and land use change types. Combining the different models allows for geographic explicit analysis of REDD policies in a global context. The geographic explicit analysis of REDD policy options is carried out using the G4M (former DIMA) model (e.g. Rokitiansky et al., 2007; Kindermann et al., 2006, 2008b). G4M is driven by exogenous market price assumptions for land and commodities without taking market feedbacks into account. The partial equilibrium model GLOBIOM generates endogenous prices. GLOBIOM has global geographic coverage and accounts for all land uses and thus allows for REDD policy analysis in a wider land use and global change context. When the two models are coupled the G4M model serves a double purpose. First it informs GLOBIOM on basic biophysical forest growth information and engineering costing of various forest management options. Second, results from GLOBIOM, such as endogenous commodity and land prices and trade, are used as exogenous drivers for the geographically explicit modeling using G4M. In the latter G4M becomes a "sophisticated" downscaling algorithm for GLOBIOM results facilitating "visual validation" of results and geographic REDD hot spotting.
In the following the two models are described. In the description of G4M we provide a detailed description of the improved carbon accounting and calibration methods departing from (Kindermann et al., 2006). Changes in the calibration methodology have necessarily created considerable differences in baseline emissions and thus REDD costs as published in (Kindermann et al., 2006). Baselines in (Kindermann et al., 2006) are determined mainly by future GDP and population development assuming low institutional barriers for expansion of the agricultural and forestry sectors whereas the latter is mainly driven by the continuation of historical emissions and the continuation of institutional barriers of agricultural and forestry sector development. The version of G4M presented in this document was calibrated to the global emissions estimates provided by the IPCC while the one in (Kindermann et al., 2006) was calibrated to the estimates provided by global analysis using remote sensing methods. Differences in the results of these two model versions of G4M provide valuable insights on the impact of changes in methodologies on REDD costs. The description of GLOBIOM is provided with less detail due to space limitations