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activities. Likewise, the assumption that the nearest deforested pixel is the dominant source of degradation
pressure may underestimate risks in landscapes with multiple interacting disturbances dynamically over
time, such as selective logging, chronic understory fire, or edge-induced drought stress.

Revalidation of the model with updates of the RACZ area’s delineation, incorporating multi-source
disturbance layers and probabilistic representations of disturbance interactions, may help refine RACZ
estimates. The framework assumes that deforestation is the primary and spatially continuous driver of
disturbance, such that distance to the nearest deforestation provides a proxy for degradation risk. This
assumption may be less appropriate in landscapes dominated by multi-source or strongly directional
disturbances, where cumulative or anisotropic effects are not explicitly captured. Integrating multi-source
disturbance into the model, as well as with predictive models of land-use change, fire spread, or road
expansion could strengthen its utility for long-term conservation planning and dynamic carbon-crediting.

At a broader level, an important limitation is the dependence on the quality of deforestation and land-cover
data. Spatial errors in deforestation detection, classification bias, or temporal inconsistencies may propagate
uncertainty into the risk surface.

Future research should therefore focus on three key areas:
(1) Temporal validation, using independent multi-sensor datasets (GEDI, ICESat-2, Sentinel-1/2, Landsat
harmonics) to test whether RACZ-designated zones systematically undergo greater biomass, canopy, or
biodiversity loss.
(2) Biome and region-specific parameterization, ensuring that α, β, and the area-factor spline reflect
structural and ecological realities of distinct forest types.
(3) Model expansion and integration, incorporating additional disturbance pathways, alternative decay
functions, and links to predictive spatial models.

Despite these limitations, the RACZ framework represents an important step toward more ecologically
realistic, empirically grounded, and risk-adaptive zoning for carbon crediting.

CONCLUSION
This study presents a spatially explicit framework for modeling degradation risk based on distance to
deforestation and landscape structure. The RACZ model formalizes the relationship between anthropogenic
pressure and forest condition through a distance-decay function, enabling the delineation of conservation
areas grounded in ecological processes.

The framework was shown to be consistent across contrasting landscape contexts, demonstrating its ability
to represent spatial variability in degradation risk and to translate ecological principles into an operational
tool for conservation and carbon accounting.

By integrating spatial dynamics and ecological structure into a unified formulation, the RACZ model
provides a transparent and adaptable approach for identifying areas of vulnerability. Future work should
focus on expanding the framework. Continued refinement and validation will enhance its robustness and
ensure that it remains aligned with contemporary scientific understanding of forest degradation dynamics.

DECLARATIONS

Acknowledgments
We’d like to thank Aurélien Vivancos and Vanessa Fuentes Suguiyama in C3 Ambiental for their comments
on the paper.