{
  "id": 3819,
  "url": "https://arxiv.org/abs/2606.02604v1",
  "title": "Auditable Climate Risk Intelligence from Fragmented ESG Data: Deterministic Orchestration and Imbalance-Aware Learning for Scope 1-3 Validation",
  "summary": "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 ES",
  "authors": "Karan Sehgal, Khawar Naveed Bhatti",
  "category": "research",
  "topics": "regulation,transparency,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-23T13:33:00.000Z",
  "fetched_at": "2026-07-14T16:30:31.922Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/3819",
  "original_url": "https://arxiv.org/abs/2606.02604v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}