Maps of Panama illustrating the reclassification methodology in order to replicate the procedure described in Bucki <em>et al</em> (2012)

<p><strong>Figure 1.</strong> Maps of Panama illustrating the reclassification methodology in order to replicate the procedure described in Bucki <em>et al</em> (<a href="" target="_blank">2012</a>). The Morphological Spatial Pattern Analysis was used to distinguish between intact and non-intact forests, based on a distance threshold (edge width). The authors however specify that the MSPA is one of the many tools that could be used to distinguish between IFL and NIFL and obtain a proxy of degraded forests. Better stratification tools or more detailed land-cover assessment can be used if available. The forest degradation identified with the MSPA proxy depends on the observational window, including the spatial resolution and the periodicity of the input maps; fine resolution and high periodicity would possibly lead to capture finer land-use patterns (Bucki <em>et al</em> <a href="" target="_blank">2012</a>).</p> <p><strong>Abstract</strong></p> <p>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 <em>et al</em> (2012 <em>Environ. Res. Lett.</em> <strong>7</strong> 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 CO<sub>2</sub> 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.</p>