Models of deforestation for setting reference levels in the context of REDD: A case study in the Peruvian Amazon

One of the main elements in the mechanisms proposed by REDD+, is to pay countries (and then the users and/or forest owners, depending on each type of project), for real reductions in deforestation and the resulting GHG emissions, as well as to ensure other benefits, such as technical assistance and qualification, among others. To be able to estimate the emission reductions, it is necessary to establish a Reference Level either through different forms of a historical average or in the form of a BAU or "Business as Usual" scenario. In this sense, current proposals set the Reference Levels equal to historical deforestation, which apply another political logic to the predictions made by the Forest Transition (FT) theory. According to this theory, when using a simple historical extrapolation, it is possible that: "countries with a lot of forest and little deforestation", lose in the initial stages of forest transition, while "countries with little forest and a lot of deforestation", win in the later stages of the FT. Our study shows how the predictions of FT and other socio-economic variables can be incorporated into predictive models (historical trend), by including the forest area as an explanatory variable. Sub-national data from the 15 departments with forest cover in the Peruvian Amazon are used to develop 6 optional deforestation models for comparative purposes. It is observed that the most important predictive variable to explain current deforestation is historical deforestation. In the same way, it is observed that when applying and implementing econometric models with different variables, there are projections very close to the results of spatially explicit models for Peru (models that include spatial data for distance to roads, elevation, slope, distance to populated centers, among others). The variation of results is only 3-4%, so it can be concluded that the projections based on the historical trend considering the forest transition of each region and other socio-economic variables, are very good estimators of the deforestation expected in the future and are adequate to define the possible reductions by deforestation and degradation in the Peruvian Amazon or other areas with similar conditions.