Illustration of the method proposed by Grassi et al (2008), (figure 4) to calculate conservative emission reductions from REDD+ using the Reliable Minimum Estimate approach based on the 95% confidence interval around the data using our simulation for Panama
Figure 5. Illustration of the method proposed by Grassi et al (2008), (figure 4) to calculate conservative emission reductions from REDD+ using the Reliable Minimum Estimate approach based on the 95% confidence interval around the data using our simulation for Panama. The simulated assessment periods (Tier-1 and -2) are the Monte Carlo scenarios using recurring sources of error with Tier-1 and Tier-2 data. In the hypothetical assessment periods, we assumed a 50% reduction in mean emissions and a proportional uncertainty (in % standard deviation) between the simulated reference period and the hypothetical assessment period.In this approach, one of the two proposed by Grassi et al (2008), a conservative emissions estimate is obtained based on the uncertainty of the reference as: a50–b50 (most stringent estimate) or a50–c50 (less stringent estimate) for the 50% confidence conservative estimate, and a95–b95 (most stringent estimate) or a95–c95 (less stringent estimate) for the 95% confidence conservative estimate.
The United Nations Framework Convention on Climate Change (UNFCCC) defined the technical and financial modalities of policy approaches and incentives to reduce emissions from deforestation and forest degradation in developing countries (REDD+). Substantial technical challenges hinder precise and accurate estimation of forest-related emissions and removals, as well as the setting and assessment of reference levels. These challenges could limit country participation in REDD+, especially if REDD+ emission reductions were to meet quality standards required to serve as compliance grade offsets for developed countries' emissions. Using Panama as a case study, we tested the matrix approach proposed by Bucki et al (2012 Environ. Res. Lett. 7 024005) to perform sensitivity and uncertainty analysis distinguishing between 'modelling sources' of uncertainty, which refers to model-specific parameters and assumptions, and 'recurring sources' of uncertainty, which refers to random and systematic errors in emission factors and activity data. The sensitivity analysis estimated differences in the resulting fluxes ranging from 4.2% to 262.2% of the reference emission level. The classification of fallows and the carbon stock increment or carbon accumulation of intact forest lands were the two key parameters showing the largest sensitivity. The highest error propagated using Monte Carlo simulations was caused by modelling sources of uncertainty, which calls for special attention to ensure consistency in REDD+ reporting which is essential for securing environmental integrity. Due to the role of these modelling sources of uncertainty, the adoption of strict rules for estimation and reporting would favour comparability of emission reductions between countries. We believe that a reduction of the bias in emission factors will arise, among other things, from a globally concerted effort to improve allometric equations for tropical forests. Public access to datasets and methodology used to evaluate reference level and emission reductions would strengthen the credibility of the system by promoting accountability and transparency. To secure conservativeness and deal with uncertainty, we consider the need for further research using real data available to developing countries to test the applicability of conservative discounts including the trend uncertainty and other possible options that would allow real incentives and stimulate improvements over time. Finally, we argue that REDD+ result-based actions assessed on the basis of a dashboard of performance indicators, not only in 'tonnes CO2 equ. per year' might provide a more holistic approach, at least until better accuracy and certainty of forest carbon stocks emission and removal estimates to support a REDD+ policy can be reached.