Auditable Climate Risk Intelligence from Fragmented ESG Data: Deterministic Orchestration and Imbalance-Aware Learning for Scope 1-3 Validation
arXiv:2606.02604v1 Announce Type: new Abstract: ESG and climate risk data remain fragmented across heterogeneous Scope 1, Scope 2, and Scope 3 reporting environments, while conventional validation pipelines lack provenance aware auditability, hidden drift detection, and reproducibility oriented governance. This paper proposes a deterministic climate risk intelligence framework integrating single source of truth orchestration, temporal anomaly detection, imbalance aware ensemble learning, and explainability oriented governance for auditable ESG validation. To support open reproducibility, we co
The increasing pressure for corporate ESG transparency and the maturation of AI techniques for data analysis converge to address fragmented climate risk reporting.
This development allows for more reliable and auditable climate risk assessments, which is crucial for financial institutions, regulators, and companies making strategic investments.
The ability to integrate and validate disparate ESG data with higher fidelity will improve decision-making regarding climate-related financial exposures and operational risks.
- · Financial institutions
- · ESG data providers
- · Companies with strong ESG performance
- · AI/ML solution providers
- · Companies with opaque ESG reporting
- · Inefficient ESG audit firms
Improved accuracy in climate risk modeling will lead to more precise capital allocation decisions.
Higher quality climate risk data will influence insurance premiums and credit ratings, penalizing poor performers.
Standardization and auditability of ESG data could become a de-facto regulatory requirement, driving systemic changes in corporate disclosure.
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Read at arXiv cs.LG