Quantum-AI and Geospatial Topological Analytics for Fraud Detection, Leakage Risk and Permanence Assessment in Carbon Credit Markets

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🔬研究者:Provides a multi-method framework for carbon credit integrity assessment, integrating geospatial, topological, and ML approaches.

🏢実務担当者:Offers a blueprint for building audit and review systems for carbon credit portfolios, enhancing credibility and compliance.

🏛政策担当者:Highlights the need for robust analytical standards in carbon market regulation and suggests methods for market surveillance.

Abstract Carbon-credit markets require analytical systems that can distinguish legitimate climate value from strategic misreporting, detect displacement of emissions beyond project boundaries, and assess whether claimed carbon benefits endure across long time horizons. This monograph develops an integrated research architecture that combines geospatial observation, topological data analysis, statistical inference, machine learning, and quantum-assisted computation for integrity assessment. The framework treats fraud, leakage, and permanence as related but non-identical risk processes, each demanding distinct assumptions, evidence structures, evaluation metrics, and governance responses. Spatial and temporal uncertainty are represented explicitly through multiscale features, graph structures, persistence summaries, causal models, calibrated predictive systems, and reproducible data pipelines. The analysis also emphasizes decision design: model outputs are translated into review thresholds, audit protocols, escalation rules, documentation standards, and portfolio-level risk controls rather than being treated as isolated scores. Across South Asia, Europe, Africa, and the Americas, the proposed methods are framed for heterogeneous monitoring capacity, land-use regimes, industrial project types, and regulatory settings. The result is a research-oriented blueprint for high-integrity carbon-market analytics that connects theory, computation, operational assurance, and policy governance while remaining adaptable to evolving measurement technologies and market rules. Keywords carbon credit integrity, Quantum-AI, geospatial analytics, topological data analysis, fraud detection, leakage risk, permanence assessment, causal inference, spatial econometrics, survival analysis, Bayesian evidence fusion, graph learning, anomaly detection, calibration, uncertainty quantification, remote sensing, digital MRV, data provenance, MLOps, market surveillance, climate finance, carbon accounting, governance, auditability

• openalex https://doi.org/10.62311/nesx/rb3jy-978-81-689097-7-9first seen 2026-08-16 05:01:14