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Results of the Monte Carlo analysis with 10 000 000 simulations using simple random sampling for 6 sets of sources of uncertainty: Recurring, Modelling and Both sources, in Tier-1 and -2

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posted on 2013-07-18, 00:00 authored by Johanne Pelletier, Davy Martin, Catherine Potvin

Figure 4. Results of the Monte Carlo analysis with 10 000 000 simulations using simple random sampling for 6 sets of sources of uncertainty: Recurring, Modelling and Both sources, in Tier-1 and -2. The boxplots show the mean for each simulation (red line and red value), the 50% confidence interval (blue box) and the 95% confidence interval (black whiskers). Negative values for emissions on the Y axis indicate that the model predicts the forest sector to be a sink for carbon while positive values predict a source of carbon. It is possible to note that for a single tier, the mean emission estimated is not the same when modelling sources of error are included because these were characterized using discrete probabilities. The fact that these probabilities are discrete and therefore are not based on a symmetric probability distribution function will affect the mean obtained. Table 13, made available with the supplementary information (at, provides details on these discrete probabilities.


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.