IPCC AR6
1 article · search the full text for this term
-
Agentic AI-Driven Negative Emission Optimization: Large Language Model Orchestration and Quantum Annealing for Real-Time Carbon Capture Policy Simulation Beyond 1.5°C Thresholds
Abstract: The convergence of agentic artificial intelligence, large language model (LLM) orchestration, and quantum annealing presents a transformative paradigm for climate policy computation. This paper introduces Nega Optima a multi-agent computational architecture that integrates LLM-driven policy parsing, quantum annealing-based combinatorial optimization, and real-time integrated assessment model (IAM) feedback loops to simulate negative emission technology (NET) deployment strategies in a post-1.5°C overshoot scenario. Existing carbon capture optimization frameworks suffer from three systemic …
Published in International Journal of Climate Conditions · Vol. 3, Issue 2, 2026 · pp. 7–14 Read article