AI-Driven Automated MRV Framework for Trustworthy Digital Carbon Markets: A Multi-Layered Architecture Integrating IoT and Blockchain

Abstract

The global scalability of carbon markets is currently hindered by structural inefficiencies in traditional Monitoring, Reporting, and Verification (MRV) frameworks, which remain manual, fragmented, and prone to greenwashing. This study proposes an integrated, multi-layered digital architecture designed to replace conventional auditing with an automated, high-integrity ecosystem. Following the PRISMA guidelines for systematic synthesis, the framework is structured into three interdependent layers: (1) the Physical-Perception Layer, which captures real-time environmental telemetry through satellite imagery, industrial IoT sensors, and mobile vehicle diagnostics; (2) the AI-Driven Analytics Layer, acting as a "Digital Auditor" that employs Machine Learning and Federated Learning to detect anomalies and verify carbon metrics; and (3) the Blockchain-Based Execution Layer, which automates market functions through smart contracts and tokenization protocols to ensure immutable recording and transparent settlement. A qualitative comparative analysis suggests that the proposed algorithm-centric model has the potential to reduce information asymmetry and transaction costs compared to human-centric systems, although empirical validation remains for future work. Ultimately, this research provides a conceptual technical foundation for a scalable and transparent global carbon market, aligning financial incentives with verifiable climate impact.