Diagnosing the uncertainty and detectability of emission reductions for REDD+ under current capabilities: an example for Panama

In preparation for the deployment of a new mechanism that could address as much as one fifth of global greenhouse gas emissions by reducing emissions from deforestation and forest degradation (REDD+), important work on methodological issues is still needed to secure the capacity to produce measurable, reportable, and verifiable emissions reductions from REDD+ in developing countries. To contribute to this effort, we have diagnosed the main sources of uncertainty in the quantification of emission from deforestation for Panama, one of the first countries to be supported by the Forest Carbon Partnership Facility of the World Bank and by UN-REDD. Performing sensitivity analyses using a land-cover change emissions model, we identified forest carbon stocks and the quality of land-cover maps as the key parameters influencing model uncertainty. The time interval between two land-cover assessments, carbon density in fallow and secondary forest, and the accuracy of land-cover classifications also affect our ability to produce accurate estimates. Further, we used the model to compare emission reductions from five different deforestation reduction scenarios drawn from governmental input. Only the scenario simulating a reduction in deforestation by half succeeds in crossing outside the confidence bounds surrounding the baseline emission obtained from the uncertainty analysis. These results suggest that with current data, real emission reductions in developing countries could be obscured by their associated uncertainties. Ways of addressing the key sources of error are proposed, for developing countries involved in REDD+, for improving the accuracy of their estimates in the future. These new considerations confirm the importance of current efforts to establish forest monitoring systems and enhance capabilities for REDD+ in developing countries.