AI-Driven Modeling of Microbial Carbon Capture Systems for ESG-Linked Carbon Accounting and Disclosures

It suggests a new framework integrating artificial intelligence with microbial carbon capture analysis to enhance Environmental, Social, and Governance (ESG) reporting. We have trained an XGBoost machine learning model that predicts soil carbon sequestration potential from microbial community structure and efficiency indicators across different ecosystems. The model correctly predicts 87% carbon storage capacity, using phospholipid fatty acid (PLFA) profiles, respiration, and environmental conditions. SHAP (SHapley Additive exPlanations) analysis revealed microbial efficiency indices, climate vulnerability scores, and microbial biomass as the major drivers of carbon sequestration potential. Our framework presents standardized carbon risk assessment matrices in line with emerging ESG disclosure expectations, enabling improved biological carbon capture potential quantification. This approach resolves a critical carbon accounting methodological shortcoming by coupling microbial dynamics, allowing firms to base carbon offset claims and climate resilience strategy on scientific premises. The AI system proves to be more accurate than standard carbon stock estimation approaches, especially in the prediction of sequestration stability under climate change scenarios.