Research Focus
Corporate climate risk is no longer only a sustainability-reporting concern. It is an enterprise decision problem spanning physical hazards, transition pathways, greenhouse-gas measurement, supply-chain exposure, capital allocation, insurance, credit, operations, and disclosure.
This paper synthesizes evidence from climate finance, corporate climate management, machine learning for climate, climate-related natural language processing, measurement-reporting-verification systems, and emerging AI and sustainability regulation.
Core Contribution
The paper proposes the Climate AI Decision System (CADS) architecture: a governance-aware enterprise pattern for using AI in climate-risk management without losing traceability, accountability, or assurance readiness.
Key contributions include:
- An enterprise evidence map for climate-risk decisions
- Governance controls for AI-enabled climate workflows
- Source-grounded evidence requirements for generative systems
- Scope 3 and measurement-reporting-verification traceability patterns
- Human escalation and audit-trail requirements for high-impact decisions
Portfolio Relevance
This work extends my enterprise AI architecture focus into climate-risk decision systems. It connects production AI concerns - grounding, auditability, model-risk controls, and human responsibility - with board-level climate and sustainability decisions.
The practical argument is direct: trustworthy climate AI requires more than better models. It requires explicit decision rights, source-grounded evidence, model-risk controls, regulatory classification, and assurance-ready workflows.
Suggested Citation
Mahajan, Abhinav, AI-Driven Corporate Climate Risk Decision Systems for Global Enterprises: The CADS Architecture, Evidence, Governance, and Research Agenda (April 16, 2026). Available at SSRN: https://ssrn.com/abstract=6704278 or http://dx.doi.org/10.2139/ssrn.6704278.