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Supporting dynamic, evidence-based buffering
The proposed approach provides a scientifically grounded delineation of the areas that crediting areas reflect
actual zones of vulnerability. By deriving spatial risk directly from the α-β parameterization, RACZ translates
decades of empirical knowledge on distance-decay processes, landscape ecology, and structural
susceptibility [20,23,26,33,50,53] into an operational framework.

This evidence-based approach has direct implications for assessments of project performance. Because α
captures the external pressure envelope and β encodes internal structural resistance, RACZ identifies zones
where biomass, canopy structure, and ecological function are most vulnerable to degradation, areas that
traditional deforestation-only baselines fail to recognize [14,45] . In doing so, RACZ enhances not only project-
level accuracy but also portfolio-level conservativeness, an increasingly important criterion in international
carbon standards aiming to improve environmental integrity.

Finally, the RACZ framework also strengthens conservation practice by identifying where forests are most
vulnerable to edge-driven degradation, enabling more targeted deployment of restoration, fire prevention,
and connectivity interventions. By operationalizing these insights into a practical spatial product, RACZ
strengthens the capacity of conservation programs to anticipate degradation before it becomes irreversible,
ultimately improving the long-term resilience of forest landscapes.

Limitations and future work
Although the RACZ framework provides a mechanistic, ecologically grounded approach for delineating
degradation-risk-adaptive conservation zones, several important limitations should be acknowledged.

First, the current approach infers spatial vulnerability from structural and contextual predictors—distance to
deforestation, landscape configuration, and pressure indices—without yet incorporating temporal validation
of degradation processes. While extensive literature supports the assumption that RACZ-identified areas are
more susceptible to biomass loss, microclimatic alteration, and biodiversity decline, the model has not been
explicitly tested using time-series observations of canopy height, above-ground biomass, spectral degradation
indices, or species-level responses within RACZ zones. Longitudinal validation using LiDAR, radar
backscatter, repeat optical imagery, or ecological plot networks will be essential to confirm whether RACZ-
delineated areas indeed undergo accelerated structural or functional degradation, and to refine the decay
parameters accordingly.

Second, some components of the model, particularly the area factor, shape-based adjustments, and
landscape-specific decay rates, were calibrated using a combination of ecological theory and generalized
empirical regularities. While these formulations capture broad ecological patterns, they may not fully
represent biome-specific or region-specific dynamics. Forests differ widely in their sensitivity to
fragmentation. For example, humid tropical forests often show deep structural degradation gradients,
whereas dry forests, savannas, and temperate systems may exhibit different decay rates or disturbance
propagation pathways. Future work should therefore explore biome-specific parameterizations, including
alternative α and β distributions, distinct formulations for shape and area-derived susceptibility, and possibly
additional modifiers such as climatic seasonality, fire regimes, hydrological connectivity, or species
composition.

A related limitation lies in the structure of the decay function. The model assumes a simple exponential
model, which is consistent with diffusion-based ecological theory but may not capture multimodal or
threshold-driven behavior observed in some ecosystems, such as abrupt biomass collapse following fire
incursion, nonlinear feedbacks from edge-driven mortality, or spatial contagion reinforced by human