Carbon Asset Financing Risk Evaluation Through Emission Trading Graph Reasoning

Abstract

Carbon asset financing provides liquidity for industrial enterprises through carbon allowances, certified emission reductions, and expected future carbon-credit revenues. However, financing risk may arise from unstable carbon prices, inaccurate emission accounting, delayed verification, weak compliance performance, overpledged carbon assets, and production changes that reduce available carbon credits. This study develops an emission trading graph reasoning model for carbon asset financing risk evaluation. The model constructs a heterogeneous knowledge graph linking industrial enterprises, emission accounts, carbon allowances, verification agencies, pledged carbon assets, production facilities, energy consumption records, loan contracts, trading counterparties, and compliance outcomes. A graph neural network learns enterprise-carbon asset representations, while a rule-guided inference module detects risk chains involving allowance shortfall, repeated pledge, delayed verification, abnormal energy consumption, and compliance penalty exposure. The empirical dataset contains 16,400 industrial enterprises, 42,000 emission accounts, 286,000 carbon trading records, 31,800 verification reports, 18,600 carbon-asset pledge contracts, 2.14 million monthly energy-consumption records, and 3,920 confirmed financing-risk events over 48 months. The proposed model reduces median risk-warning time from 92 days to 35 days before repayment stress or collateral impairment. It identifies 3,740 carbon-asset risk chains and 1,260 overpledged allowance structures. Portfolio simulation shows that graph-informed monitoring reduces expected impaired carbon-backed exposure by 74 million RMB during the validation window. Full quarterly assessment is completed in 8.2 minutes. These findings indicate that emission trading graph reasoning can improve carbon asset financing risk evaluation by linking industrial production, carbon compliance, asset valuation, and repayment behavior