AI-Augmented Blockchain Analytics for Carbon Credit Verification: An Intelligent Risk-Scoring Framework for Trustworthy Transition Finance

Abstract

Transition finance has emerged as a strategic instrument for channeling capital toward carbon-intensive industries that cannot achieve immediate net-zero outcomes but are committed to credible decarbonization pathways. The integrity of such financing depends on the trustworthiness of carbon credit verification, which remains undermined by fragmented data sources, opaque verification chains, and recurring fraud and double-counting incidents. This study proposes an artificial-intelligence-augmented blockchain analytics framework that introduces an intelligent risk-scoring layer between emission-data acquisition and on-chain credit issuance. The framework integrates supervised machine-learning classifiers (random forest, gradient boosting, and a deep neural network) with anomaly detection and ensemble aggregation to produce a composite carbon-credit risk score. The score is consumed by smart contracts as a programmable gating condition that determines whether a credit is issued, flagged for human review, or rejected, while every decision is anchored to an immutable distributed ledger. The framework was evaluated on a multi-sector synthetic carbon-credit dataset of 48,720 records spanning energy, manufacturing, transportation, and agroforestry projects between 2018 and 2023. The deep neural network achieved an area under the ROC curve of 0.96 and an F1 score of 0.93 in distinguishing fraudulent from genuine credits, while the AI-augmented blockchain pipeline reduced average verification latency from 4.6 hours to 41 seconds and decreased the estimated double-counting probability from 7.8 percent to below 0.1 percent compared with centralized rule-based baselines. The findings indicate that combining predictive risk analytics with cryptographic verification can deliver auditable, scalable, and trust-enhancing infrastructure for transition finance, with direct implications for regulators and sustainability-linked investors.

Keywords: Carbon credit verification; Blockchain analytics; Machine learning; Risk scoring; Smart contracts; Transition finance; Sustainable finance; Anomaly detection.