arXiv AI By Sourav Bose, Taoufik Bouraoui

Beyond Greenwashing: Algorithmic Resilience and the Non-Linear Governance Premium in Sovereign Capital Flows

Read the original on arXiv AI →

The paper shows that sovereign ESG metrics are mispriced by linear macro‑financial models during systemic shocks, revealing a fundamentally non‑linear risk transmission. Using a gradient‑boosting model on 147 sovereigns from 2010‑2024, forecast errors are reduced by over 39% during extreme macro events. The study identifies a strict, non‑linear "Governance Premium"—environmental and social gains provide no capital advantage until a regulatory threshold is crossed—and offers regime‑conditional uncertainty bounds that remain valid even with severe ESG reporting failures.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI
Jun 3

Auditable Climate Risk Intelligence from Fragmented ESG Data: Deterministic Orchestration and Imbalance-Aware Learning for Scope 1-3 Validation

arXiv:2606. 02604v1 Announce Type: cross 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.

By Karan Sehgal, Khawar Naveed Bhatti
arXiv Statistics ML
Sep 16

Statistical Inference for Score Decompositions

arXiv:2603.04275v2 Announce Type: replace-cross Abstract: We introduce inference methods for score decompositions, which partition scoring functions for predictive assessment into three interpretable...

By Timo Dimitriadis, Marius Puke