Climate uncertainty represents one of the most consequential challenges for contemporary governance, affecting trillions of dollars in infrastructure investment, agricultural planning, and international mitigation policy. This research presents a comprehensive social-epistemic analysis of how quantum-classical hybrid computational frameworks are reshaping the production, validation, and utilization of climate knowledge. Through systematic examination of two critical climate processes, soil respiration and atmospheric ice nucleation; demonstrate that quantum-informed parameterizations substantially reduce uncertainty: 30–40% for cirrus cloud feedback and 52.7% for projected carbon cycle feedbacks. These reductions expand remaining carbon budgets by approximately 2,500 Pg C, extend mitigation timelines by 36–94 years for 1.5°C and 2.0°C targets, and yield economic implications exceeding $500–2,000 trillion across carbon pricing scenarios. Our analysis reveals three transformation pathways: (1) epistemic democratization, replacing empirical fitting and expert judgment with first-principles physical constraints; (2) policy resilience, enhancing decision confidence and enabling more gradual transition pathways; and (3) institutional restructuring, creating new interdisciplinary collaborations between quantum chemists, climate modelers, and policy analysts. However, environmental justice analysis shows that uncertainty reduction benefits are non-uniformly distributed, with vulnerable regions (South Asia, Africa) experiencing greater reductions but requiring deliberate governance mechanisms to ensure equitable outcomes. We identify the governance challenge of avoiding moral hazard, using expanded carbon budgets to enable ambition rather than delay. This research advances social scientific understanding of computational governance and provides actionable recommendations for integrating quantum-informed climate projections into international assessment frameworks, national adaptation planning, and climate litigation contexts.
Copyrights © 2026