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

Year : 2026 | Volume : 03 | Issue : 02 | Page : 7 14
By

Mayuresh Sharma,

Aryan Sinha,

  1. Student, Department of Computer Science and Engineering, Greater Noida Institute of Technology, Greater Noida, Uttar Pradesh, India
  2. Student, Department of Computer Science and Engineering, Greater Noida Institute of Technology, Greater Noida, Uttar Pradesh, India

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 limitations: static policy inputs, classical solver intractability at continental deployment scales, and the absence of closed-loop policy refinement mechanisms. Nega Optima addresses these gaps through a hierarchical agent topology in which a Coordinator LLM agent decomposes multi-decadal climate targets into callable subtasks, delegating combinatorial fleet allocation problems to a hybrid quantum-classical annealer built on D-Wave Advantage architecture, while simultaneously streaming scenario outputs back to a Critic LLM agent for policy-coherence verification. Experimental results across 1,200 policy scenario simulations demonstrate that Nega Optima achieves a mean time-to-optimal-solution reduction of 67.3% relative to MILP baselines, with a carbon capture portfolio deviation of less than 4.2% from IPCC AR6 CDR pathway benchmarks. The framework further demonstrates robust equity-aware allocation under Land Use, Land-Use Change, and Forestry (LULUCF) constraints, reducing land competition conflicts by 41% in South and Southeast Asian deployment scenarios. These findings establish Nega Optima as a computationally credible and policy-relevant instrument for navigating the carbon budget emergency. (1) Nega Optima: first agentic AI framework coupling LLM orchestration with quantum annealing for NET policy optimization. (2) 67.3% reduction in solve time vs. classical MILP solvers across 1,200 scenario benchmarks. (3) Closed-loop Critic-LLM mechanism enforces policy coherence and UNFCCC alignment in real time. (4) Equity-weighted LULUCF constraints reduce land-conflict index by 41% in Asia-Pacific scenarios. (5) CDR portfolio deviation <4.2% from IPCC AR6 C1–C3 pathways under Monte Carlo uncertainty.

 

Keywords: Agentic AI, negative emission technologies, quantum annealing, LLM orchestration, carbon capture, climate policy simulation, IPCC AR6, integrated assessment models, multi-agent systems, CDR optimization.

[This article belongs to International Journal of Climate Conditions ]

How to cite this article: Mayuresh Sharma, Aryan Sinha. 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. International Journal of Climate Conditions. 2026; 03(02):7-14.
How to cite this URL: Mayuresh Sharma, Aryan Sinha. 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. International Journal of Climate Conditions. 2026; 03(02):7-14. Available from: https://journals.stmjournals.com/ijcc/article=2026/view=260340

References

  1. Bauer N, Calvin K, Emmerling J, Fricko O, Fujimori S, Hilaire J, Eom J, Krey V, Kriegler E, Mouratiadou I, de Boer HS. Shared socio-economic pathways of the energy sector–quantifying the narratives. Global Environmental Change. 2017 Jan 1;42 :316-30. https://doi.org/10.1016/j.gloenvcha.2016.07.006
  2. Homberger M, Simon U. On the application potential of gold nanoparticles in nanoelectronics and biomedicine. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 2010 Mar 28;368(1915):1405-53.
  3. Guivarch C, Kriegler E, Portugal-Pereira J, Bosetti V, Edmonds J, Fischedick M, Havlik P, Jaramillo P, Krey V, Lecocq F, Lucena AF. Intergovernmental Panel on Climate Change (IPCC) 2022. Annex III: Scenarios and Modelling Methods. In Climate change 2022: Mitigation of climate change. Contribution of working group III to the sixth assessment report of the intergovernmental panel on climate change 2023 (pp. 1841-1908). Cambridge University Press.
  4. Climate change 2022: Mitigation of climate change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press; 2022.https://www.ipcc.ch/report/ar6/wg3/
  5. Vuletić D. Strategic Risks of AI-Enabled Automation: Implications for Crisis Management and Systemic Resilience. International Journal of Disaster Risk Management. 2026 Mar 1;8(1):131-46.
  6. Kriegler E, Bauer N, Popp A, Humpenöder F, Leimbach M, Strefler J, Baumstark L, Bodirsky BL, Hilaire J, Klein D, Mouratiadou I. Fossil-fueled development (SSP5): An energy and resource intensive scenario for the 21st century. Global environmental change. 2017 Jan 1; 42:297-315. https://doi.org/10.1016/j.gloenvcha.2016.05.015
  7. Luderer G, Vrontisi Z, Bertram C, Edelenbosch OY, Pietzcker RC, Rogelj J, De Boer HS, Drouet L, Emmerling J, Fricko O, Fujimori S. Residual fossil CO2 emissions in 1.5–2 C pathways. Nature Climate Change. 2018 Jul;8(7):626-33.
  8. McGeoch CC, Farré P. Milestones on the quantum utility highway: Quantum annealing case study. ACM Transactions on Quantum Computing. 2023 Dec 16;5(1):1-30.
  9. Minx JC, Lamb WF, Callaghan MW, Fuss S, Hilaire J, Creutzig F, Amann T, Beringer T, de Oliveira Garcia W, Hartmann J, Khanna T. Negative emissions—Part 1: Research landscape and synthesis. Environmental research letters. 2018 Jun 1;13(6):063001.
  10. Popp, , Calvin, K., Fujimori, S., et al. (2017). Land-use futures in the shared socio-economic pathways. Global Environmental Change, 42, 331–345.
  11. Nakicenovic N, Lempert RJ, Janetos AC. A framework for the development of new socio-economic scenarios for climate change research: introductory essay: a forthcoming special issue of climatic change. Climatic change. 2014 Feb;122(3):351-61.
  12. Smith P, Davis SJ, Creutzig F, Fuss S, Minx J, Gabrielle B, Kato E, Jackson RB, Cowie A, Kriegler E, Van Vuuren DP. Biophysical and economic limits to negative CO2 emissions. Nature climate change. 2016 Jan;6(1):42-50.
  13. Wu Q, Bansal G, Zhang J, Wu Y, Li B, Zhu E, Jiang L, Zhang X, Zhang S, Liu J, Awadallah AH. Autogen: Enabling next-gen llm applications via multi-agent conversation. arXiv preprint arXiv:2308.08155. 2023 Aug 16. https://doi.org/10.48550/arXiv.2308.08155
  14. Yao S, Zhao J, Yu D, Du N, Shafran I, Narasimhan K, Cao Y. React: Synergizing reasoning and acting in language models. arXiv preprint arXiv:2210.03629. 2022 Oct 6. https://doi.org/10.48550/arXiv.2210.03629
  15. Yarkoni S, Raponi E, Bäck T, Schmitt S. Quantum annealing for industry applications: Introduction and review. Reports on Progress in Physics. 2022 Oct 1;85(10):104001.
  16. Volkanovska E. Language models for the analysis of and interaction with climate change documents. Environmental Data Science. 2025 Jan;4(e51)1-42. DOI: https://doi.org/10.1017/eds.2025.10024
Regular Issue Subscription Review Article
Volume 03
Issue 02
Received 04/07/2026
Accepted 11/07/2026
Published 13/07/2026
Publication Time 9 Days

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