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Finalizing Avoided Deforestation Baselines III. D. Simulation of the carbon baseline When we model deforestation under a ‘business-as-usual’ scenario (BAU) we allocate the predicted quantity of deforestation to the highest-ranking candidate cells in the risk map. The location of each cell deforested within the boundary of the climate action project is then referenced to the carbon map to find the amount of carbon emitted per year. These yearly quantities of carbon emissions, over the course of the project’s lifetime, are the baseline estimate of carbon released, assuming the absence of a carbon set-aside project. IV. The APPLICATION of GEOMOD to predict the ‘business-asusual’ carbon baseline for the Calakmul region of the State of Campeche IV. A. Project Area Description We analyzed deforestation within the State of Campeche, Mexico located on the Yucatan Peninsula. The area analyzed (16,662 km 2 ) includes 6,597 km 2 of the Calakmul Biosphere Reserve plus an additional 10,025 km 2 to the west of the reserve, an area that includes both heavily impacted regions and remote areas of the Mayan Forest (la Selva Maya). Vegetation in the region according to the Instituto Nacional de Estadística Geografía e Informática (INEGI) year 2000 classification consists primarily of selva alta, mediana, y baja subperrenifolia (tall, medium and low evergreen forest) (Figure 2). Winrock International A- 6
Finalizing Avoided Deforestation Baselines Figure 2. Year 2000 Vegetation Map for the State of Campeche (INEGI 2000). Yellow box illustrates area of analysis. IV. B. Data Inputs The data required to make this analysis possible were contributed by many organizations and individuals working in the area, such as Ben DeJong, and Miguel-Angel Castillo of El Colegio de la Frontera Sur (ECOSUR); Daniel Juhn of Conservation International (CI); Kim Batchelder, The Nature Conservancy (TNC); Gabriela Guerrero UNAM Michoacan; Larry Gorenflo of Argonne National Laboratories; Billy Turner, Clark University; or purchased from the Mexican government’s Instituto Nacional de Estadística Geografía e Informática (INEGI), or were created through satellite image analysis and digitizing of INEGI paper maps. Data, data sources, metadata Data for this analysis was selected to capture those factors, which we assume may influence the rate and pattern of forest conversion. Biophysical factors include elevation, hydrography, presence of natural water bodies both seasonal and constant, and wetlands; economic factors are those related to investments in infrastructure that give people increased access to forested lands or increased access to markets, or make areas more desirable to live, e.g. railroads, roads, agricultural crop delivery points, ports, towns with commercial importance, schools, medical clinics. Political factors are incorporated by 1) analyzing individual political units, in this case ‘municipios,’ and 2) incorporating the ‘ejido’ boundaries as a potential determinant of the quantity and pattern of change across the landscape. Such political boundaries delineate different decision-making units, where the policies effected by governing bodies may affect the Winrock International A- 7
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