Effect of Uncertainties in Estimated Carbon Reduction from Deforestation and Forest Degradation on Required Incentive Payments in Developing Countries

For reducing emissions from deforestation and forest degradation (REDD+) programs, it is particularly important that monitoring for emission reductions is tied to the revenues a developing country receives from REDD+ projects; any estimated uncertainties will have significant impacts on the emission reduction estimation and incentive scheme of REDD+. However, the effects of estimated uncertainties on incentives for developing countries have not been deeply discussed in the current literature. To fill this gap, two estimation approaches for emission reductions are introduced by considering the incentive coefficient by the principle of reliable minimum estimation. The relationship between estimated uncertainties and incentive coefficient is simulated to illustrate the effects of estimated uncertainties on the emission reduction estimation and incentive scheme. Data from six tropical developing countries are used, including Nigeria, Honduras, Indonesia, Cambodia, Ghana, and Brazil. The results indicate that both the errors of referential and actual carbon stock must be considered when estimating and predicting emission reductions. The effects of the error of actual carbon stock on the emission reduction estimation and incentive coefficient were determined to be more influential. The current incentive scheme was more favorable to developing countries with high carbon stock variability, while developing countries with low carbon stock variability had insufficient incentives to implement REDD+ project.