A Multi-Layer Zero-Trust Framework for IoT-Driven Environmental Governance Using Computational GRC and Temporal Stability Modeling

Introduction
Current environmental, social, and governance (ESG) rating schemes tend to be static and not include data-level validation, while governance, risk, and compliance (GRC) schemes continue to be process-oriented instead of computational. The IoT-driven environmental rating solutions are typically unable to validate the trust in terms of signals. This leads to inconsistency and unrepeatability of sustainability ratings. This paper presents a computational framework for quantifying ESG trust with consideration of real-world uncertainties.

Methods
An IoT indoor air quality database is used to develop a multi-layer zero-trust architecture. This scheme involves four computational layers: data integrity validation based on the variability and risk of noise and missing values; GRC Engine relying on the violation threshold approach; environmental consistency modeling using PM correlations; and temporal stability estimation based on drift-sensitive signal dynamics. The final computation of the ESG-TRUST SIGNAL uses multiplicative layer-wise fusion for risk propagation. A benchmark weighted ESG index was built for comparative analysis along with ablation experiments. Which promotes t-test and confidence interval statistical validation.

Findings
Our new model generates an even lower trust value than the baseline ESG model. In a more conservative manner (Mean = 0.0803 vs. 0.4998), which is statistically confirmed (p < 0.001). According to ablation results, the temporal stability effect is found to have the most impact on reducing trust values, followed by data integrity and governance. In the multiplicative form, a natural way to compress the values is used for the uncertain environment.

Conclusion
With a zero-trust inspired approach, a computationally-based sustainability assessment method has been developed for the IoT smart environments. This model considers the reliability of the IoT sensors' signals and governance. It also considers temporal effects. The developed framework overcomes few limitations associated with existing ESG models, especially in terms of introducing signal-level trust and multi-layer risk aggregation.