Significance of carbon stock uncertainties on emission reductions from deforestation and forest degradation in developing countries

A historical agreement was reached in Bali under the United Nations Framework Convention on Climate Change, encouraging countries to initiate actions to reduce emissions from deforestation and forest degradation in developing countries (REDD). In this context, we use a Panama‐based example to show the impacts of the current levels of uncertainty in forest carbon density estimates on GHG baseline estimation and estimations of emission reductions. Using five aboveground tree carbon stock estimates for Moist Tropical forest in a simulation study, we found a difference in terms of annual CO2 emissions of more than 100% between the lowest and the highest estimates. We analyze the economic significance to show that when comparing the income generated for the different forest carbon density estimates to the cost of 10% reduced deforestation, the break‐even point differs from US$6.74 to US$16.58 per ton of CO2e between the highest and the lowest estimate. We argue that for a country such as Panama, improving the quality of forest carbon stock estimates would make economic sense since the highest forest carbon density estimates were developed nationally while the lowest estimate is the global default value. REDD could result in a huge incentive for forest protection and improved forest management, in consequence, we highlight that progress on the incorporation of uncertainty analysis and on the mitigation of the main sources of error in forest carbon density estimates merit further methodological guidance.