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The reference region was defined by selecting Quilombola territories and agrarian-reform settlements whose
land-use systems, tenure and governance arrangements are comparable to Curiaú and that meet the
methodology’s minimum robustness criteria: at least ten units and a total forest area in 2022 of at least fifty
times Curiaú’s 2022 forest area. To exclude extreme outliers in terms of scale, only units whose natural forest
area in 2022 lay between the 5th and 95th percentiles of the distribution across all candidates were retained,
thereby removing very small and very large units while maintaining a coherent sample. The resulting
reference region is used to characterize deforestation dynamics in mosaics dominated by smallholder and
traditional communities.

For each unit in the reference region, the forest area in 2022 and the area of pixels that transitioned from
natural forest in 2022 to anthropic classes in 2023 were computed. For this study, only a single annual
transition, the 2022-2023 forest-to-anthropic change, was considered as an annual deforestation rate. For
each reference unit, an annual deforestation proportion was calculated as the ratio between 2022-2023
deforestation area and 2022 forest area. These proportions were summarized across the reference region,
separately for Quilombola territories and settlements, and an overall reference deforestation rate for
smallholder-dominated mosaics was derived as an area-weighted mean, using each unit’s 2022 natural-forest
area as the weighting factor.

To ensure the integrity of the dynamic baseline, an independent accuracy assessment was conducted through
10-metre land-cover maps. A probability-based stratified random sample of reference points was drawn
across the project area and the reference region, with strata defined by mapped class (natural forest and
anthropic use) and spatial domain (Curiaú and the external reference units). The sample size was chosen to
meet the minimum requirements for each stratum and to allow estimation of user’s accuracy with
[42]
reasonably narrow confidence intervals. Each sample point was visually interpreted using multi-temporal
Sentinel-2 MSI Level-2A imagery at 10-metre resolution from the 2022 baseline year, to verify consistency
with the annual land-use and land-cover classification used in the study. Class-specific confusion matrices
were constructed to estimate user’s accuracy for map validation, and were used to support a conservative
accuracy-based adjustment following Olofsson et al. and IPCC guidance , together with the overall
[30]
[29]
accuracy of the map. Unbiased adjusted area estimates were then obtained by applying the overall map
accuracy level to the nominal 2022-2023 deforestation areas in both Curiaú and the reference region. The
numerical results of the accuracy assessment and the magnitude of the adjustments are reported in the
section "RESULTS".

These procedures yield a dynamic “micro-jurisdictional” baseline. Activity data are derived from externally
produced land-cover maps, corrected using probability-based accuracy assessment and design-based area
estimators, and summarized for a locality-matched reference region rather than generated through
counterfactual modelling. For contextual purposes, the resulting baseline structure was qualitatively
compared with other consolidated REDD+ frameworks currently applied in the voluntary carbon market.

Estimation of potential credits and climate-resilience analysis
Potential avoided deforestation is quantified as the difference between the dynamic baseline deforestation
within Curiaú and a project scenario in which deforestation is avoided. For the year applied, avoided
deforestation area is converted into avoided CO equivalent (CO e) emissions using carbon-stock factors
2
2
from the latest Brazilian Forest Reference Emission Level (FREL) for the forest type corresponding to
[51]
Curiaú in the official Instituto Brasileiro de Geografia e Estatística (IBGE, the Brazilian national statistical
and geographic agency), vegetation map. Leakage deductions and other conservative adjustments are then
applied, using parameter values that fall within the range typically adopted by major REDD+ standards in the
voluntary carbon market, in order to obtain conservative estimates of net emission reductions that could, in