Hybrid Time-β-VAE and Transformer for Blockchain Carbon Anomaly Detection

Abstract

This study introduces an enhanced anomaly detection framework integrating Time-[Formula: see text]-Variational Autoencoders (Time-[Formula: see text]-VAE) and Transformer architectures for blockchain-based carbon trading markets. Against intensifying global climate challenges, ensuring carbon market integrity is critical. While blockchain technology enhances transparency, it simultaneously introduces novel regulatory complexities in detecting sophisticated anomalies. Our improved hybrid model, trained on raw transaction records of Moss Carbon Credit (MCO2) tokens sourced via Ethereum blockchain APIs, demonstrates significant efficacy in identifying critical anomalies including smart contract-driven token distribution and fake liquidity attacks through empirical case analysis. The research establishes a scientific framework for blockchain deployment and supervision in carbon markets.