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

Vibha Saraswat,

Harshit Mittal, Harshit Mittal

и другие.

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Май 14, 2025

Abstract 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 sequestration potential from community structure efficiency indicators across different ecosystems. The correctly 87% storage capacity, using phospholipid fatty acid (PLFA) profiles, respiration, environmental conditions. SHAP (SHapley Additive exPlanations) revealed indices, climate vulnerability scores, biomass as the major drivers of potential. Our presents standardized risk assessment matrices in line emerging ESG disclosure expectations, enabling improved biological quantification. This approach resolves critical accounting methodological shortcoming by coupling dynamics, allowing firms base offset claims resilience strategy on scientific premises. AI system proves be more accurate than standard stock estimation approaches, especially prediction stability under change scenarios.

Язык: Английский

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

Vibha Saraswat,

Harshit Mittal, Harshit Mittal

и другие.

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Май 14, 2025

Abstract 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 sequestration potential from community structure efficiency indicators across different ecosystems. The correctly 87% storage capacity, using phospholipid fatty acid (PLFA) profiles, respiration, environmental conditions. SHAP (SHapley Additive exPlanations) revealed indices, climate vulnerability scores, biomass as the major drivers of potential. Our presents standardized risk assessment matrices in line emerging ESG disclosure expectations, enabling improved biological quantification. This approach resolves critical accounting methodological shortcoming by coupling dynamics, allowing firms base offset claims resilience strategy on scientific premises. AI system proves be more accurate than standard stock estimation approaches, especially prediction stability under change scenarios.

Язык: Английский

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