Knowledge Graph Reasoning for Risk Assessment in Green Industrial Project Finance

Green industrial project finance supports renewable energy facilities, low-carbon manufacturing upgrades, pollution-control equipment, energy-saving renovation, and circular economy projects. These projects involve long investment cycles, policy subsidies, technical uncertainty, environmental compliance pressure, and complex stakeholder relationships. Traditional project risk assessment often focuses on financial ratios and collateral value, but it does not sufficiently capture policy dependency, environmental performance, supplier reliability, and sponsor credit risk. This study develops a knowledge graph reasoning model for risk assessment in green industrial project finance. The proposed method builds a project-centered knowledge graph containing project sponsors, engineering contractors, equipment suppliers, subsidy policies, environmental permits, carbon-reduction indicators, loan contracts, repayment schedules, and related enterprise networks. A graph neural encoder learns project risk representations, while a rule-based inference module identifies risk chains from delayed subsidies, weak environmental compliance, contractor disputes, technology performance gaps, and sponsor financial stress. The evaluation dataset contains 8,760 green industrial projects, 24,300 sponsor enterprises, 6,420 contractors, 18,900 equipment suppliers, 31,600 policy-subsidy records, 52,000 financing contracts, and 4,180 project-risk events over 50 months. The proposed model reduces median early-warning time from 96 days to 41 days before project repayment deterioration. It identifies 3,260 policy-dependent risk paths and 2,140 contractor-related delay chains. Portfolio simulation shows that prioritizing projects by graph-inferred risk exposure reduces expected overdue financing amount by 132 million RMB during the validation period. Full quarterly assessment is completed in 9.8 minutes. The findings indicate that knowledge graph reasoning can provide more interpretable and relationship-aware risk assessment for green industrial project finance.

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