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Implications for the NbS market and the integrity of carbon credits
The RACZ framework should not be understood as a methodological complement to existing ICVCM-
approved REDD+ standards, but rather as a distinct and additional scope within nature-based solutions.
REDD+ methodologies estimate avoided emissions by projecting future deforestation and quantifying the
carbon losses that would occur in the absence of the project. By contrast, RACZ does not model
deforestation trajectories; instead, it delineates the spatial extent of forest area that is vulnerable to
degradation driven by proximity to anthropogenic land uses.

Whereas REDD+ methodologies model the risk of forest loss using distance to remaining forest as a
predictor—consistent with its objective of forecasting where new clearing might occur—the RACZ model
uses distance to deforestation to represent how already-established edges propagate structural and functional
degradation into standing forests. This distinction aligns directly with empirical evidence, showing that edge-
driven degradation reduces biomass, ecosystem function, biodiversity, and growth rates, even in forests that
remain nominally intact.

In this sense, RACZ introduces a new conceptual scope for nature-based climate solutions, designed to
protect the ecological integrity and growth potential of vulnerable forest margins. Rather than extending or
substituting existing REDD+ frameworks, RACZ expands the methodological landscape by formalizing the
spatial component of degradation risk and enabling crediting approaches that reflect the well-documented
influence of fragmentation and anthropogenic proximity on long-term forest carbon dynamics.

The following are the potential implications of the RACZ approach for conservation-based carbon removal
credits and the main innovations of the methodology.

Risk-adaptiveness as a mechanism for improving crediting accuracy
The RACZ framework offers a pathway for substantially improving the environmental integrity of carbon
crediting by embedding ecological risk directly into the spatial definition of creditable areas. Current nature-
based methodologies, particularly those relying on avoided deforestation, typically delineate project
boundaries using projected deforestation risk or administrative units, implicitly assuming that vulnerability
is uniform across landscapes that are, in reality, highly heterogeneous [5,52] . Such assumptions can lead to
crediting forest areas that face little or no disturbance pressure, thereby weakening additionality and
overstating climate benefits.

By contrast, RACZ introduces risk-adaptiveness as a core design principle. By integrating external
anthropogenic pressure (operationalized as distance to deforestation) with internal ecological susceptibility
(derived from fragment size, shape, and structural configuration), the model identifies the portions of a
project area where degradation is most likely to occur. This ensures that crediting zones are geographically
restricted to areas with demonstrable, ecologically justified vulnerability, rather than areas selected through
uniform or administratively convenient rules.

In doing so, RACZ improves crediting accuracy by aligning carbon benefits with actual landscape processes,
reducing the risk of over-crediting, and increasing transparency for both project developers and standard-
setting bodies. The result is a crediting framework that is more scientifically defensible, more consistent with
contemporary understanding of forest degradation dynamics, and better suited to support high-integrity
nature-based climate solutions. Therefore, RACZ provides a more defensible and scientifically coherent basis
for avoiding over-crediting while strengthening environmental integrity across carbon portfolios.